US20120158574A1 - Systems and methods for detecting bust out fraud using credit data - Google Patents

Systems and methods for detecting bust out fraud using credit data Download PDF

Info

Publication number
US20120158574A1
US20120158574A1 US13/186,130 US201113186130A US2012158574A1 US 20120158574 A1 US20120158574 A1 US 20120158574A1 US 201113186130 A US201113186130 A US 201113186130A US 2012158574 A1 US2012158574 A1 US 2012158574A1
Authority
US
United States
Prior art keywords
bust out
data
consumer
fraud
bust
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US13/186,130
Inventor
Hakan Olof Brunzell
Arielle Renee Caron
Tak Wun Wong
Anthony J. Sumner
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Experian Information Solutions LLC
Original Assignee
Experian Information Solutions LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Experian Information Solutions LLC filed Critical Experian Information Solutions LLC
Priority to US13/186,130 priority Critical patent/US20120158574A1/en
Publication of US20120158574A1 publication Critical patent/US20120158574A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02Banking, e.g. interest calculation or account maintenance
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03Credit; Loans; Processing thereof
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08Insurance

Definitions

  • the disclosure relates generally to the field of financial protection.
  • the disclosure relates specifically to the field of fraud detection.
  • fraud detection systems are typically applied in two ways: at the point of credit application (sometimes referred to as application fraud systems), or through ongoing monitoring by a financial institute of its consumer transactions compared against an established profile of that consumer's behavior (sometimes referred to as transaction fraud systems).
  • Application fraud systems were not designed to detect fraud that takes place after the consumer's application is approved and credit is granted (sometimes referred to as post-book fraud); consequently, such systems often prove ineffective in detecting post-book fraud. For example, if a consumer is opening an account in his/her own name intending to commit fraud in the future, application fraud systems may verify the consumer's identity without analyzing the likelihood of the consumer engaging in fraud after the account is opened. Similarly, transaction fraud systems are ineffective in situations when evolving types of fraud that take advantage of more than one financial institution.
  • fraud One specific type of fraud that traditional fraud systems are unable to detect is fraud that typically occurs in an organized fashion, across multiple credit issuers, and involves a build-up phase of seemingly normal consumer behavior followed by an exceedingly large number of purchases, cash advances, or other uses of credit, and then subsequent abandonment of the account. This fraud is sometimes referred to as bust out fraud.
  • credit bureau scoring models are created using credit bureau data to detect bust out fraud. The credit bureau scoring models may be then applied to consumer data to determine whether a consumer is likely involved in bust out fraud before a consumer abandons his accounts.
  • a computer implemented method of developing a data filter for identifying bust out fraud may include electronically developing a credit bureau bust out model that predicts the propensity of a consumer to be engaged in bust out fraud analyzing substantially only credit bureau data.
  • a bust out fraud detection system may include a processor configured to run software modules; a data storage device storing a plurality of consumer records, the data storage device in electronic communication with the processor; and a bust out module configured to identify a subset of the plurality of records from the data storage device, receive a credit bureau bust out model from a storage repository, the credit bureau bust out model predicting which consumer records are likely involved and created using substantially only credit bureau data, apply the credit bureau bust out model to each of the subset of the plurality of consumer records to generate a credit bureau bust out score for each of the subset of the plurality of consumer records, and store in a storage repository the credit bureau bust out score associated with the subset of the plurality of the consumer records; and where the processor is able to run the bust out module.
  • a computer implemented method for generating scores that indicate bust out fraud may include electronically identifying a plurality of consumer records; electronically receiving a bust out filter from a storage repository, the bust out filter created using substantially only credit bureau data; electronically applying the bust out filter to each of the plurality consumer records to generate a bust out score for each of the plurality of consumer records; and electronically storing in a storage repository the bust out score associated with each of the consumer records.
  • FIG. 1 illustrates one embodiment of example scenario for detecting bust out fraud using credit bureau data.
  • FIG. 2 illustrates one embodiment of a computer hardware system configured to run software for implementing one or more embodiments of the fraud detection system described herein.
  • FIG. 3 illustrates one embodiment of a flowchart diagram for analyzing data to create a credit bureau bust out model using credit bureau data.
  • FIG. 4 illustrates one embodiment of a flowchart diagram for analyzing consumer data to apply a credit bureau bust out model and generate credit bureau bust out scores.
  • bust out fraud is a hybrid fraud and credit problem were an individual and/or entity opens multiple lines of credit/accounts increases utilization and then subsequently abandons the accounts.
  • the line of credit/accounts may include credit cards accounts, debit card accounts, equity lines, and so forth.
  • scoring models are specifically developed to predict bust out fraud using credit bureau data.
  • One advantage to using credit bureau data is that it provides information about the consumer across multiple consumer accounts at multiple institutions. The scoring models can be applied to one or more sets of consumer data to generate a score for each consumer predicting the likelihood that the consumer is involved in bust out fraud.
  • the scoring models can then be used alone or in combination with other scores and credit or demographic attributes to evaluate a consumer when opening an account, to monitor a portfolio of consumers, and/or to weed out undesirable prospective customers.
  • the scoring model may be used in an online environment or in a batch environment.
  • the scoring model is created using “bad” credit bureau data, which includes data for accounts that were classified as bust out accounts.
  • an account is classified as a bust out fraud account according to two aspects, action and intent.
  • Example account actions include, an account balance approaching or exceeding its limit, payments with bad checks, and/or similar behavior on other accounts linked to the same account holder.
  • Example account actions demonstrating intent include requests for a credit limit increase, requests for adding authorized users, frequent balance inquiries, use of balance transfers and convenience checks, and/or being unable to contact the account holder.
  • other requirements or definitions may be used to classify an account or a consumer as a bust out account.
  • the bad credit bureau data used to create the scoring module may include a variety of data including, for example, data indicating that an account is unpaid, the account is delinquent (for example 30 + days, 60 + days, 90 + days, 120 + days), the accounts balance is close to or over its limit, a payment on the account has been returned or bounced, attempts to contact the account owner via the providers phone number(s), address(es), and/or email address(es) have failed, and/or that similar data exists at one or more financial institutions.
  • the scoring model may also be created using “good” credit bureau data, which is data from non-fraud consumers.
  • the scoring models may be configured in a variety of ways.
  • the scoring models may be configured to enhance the prediction of bust out fraud, to reflect current bust out fraud trends, to increase the operational efficiency of identifying consumers that may be involved in bust out fraud, and/or to compliment existing fraud detection/prevention tools.
  • the scoring models also may be configured to predict bust out fraud for a certain amount of time prior to the abandoning of any accounts, such as, for example 6 to 8 weeks, or 1 to 3 months.
  • the scoring models may be configured to detect a significant portion of bust out fraud such as, for example 35%, 60%, or 78% of bust out fraud.
  • the scoring models also may be configured designate high risk consumers (for example, consumers with a higher score) from low risk consumers (for example, consumers with a lower score) so that the user of the system can focus on dealing with the higher risk consumers.
  • the scoring model may also factor in the potential amount at risk such that consumers that are most likely involved in bust out fraud and that have the highest potential collection balance are scored the highest.
  • the scoring models may utilize a variety of scoring methods, including numeric scores where the lower number indicates bust out fraud, numeric scores where a higher number indicates bust out fraud, letters scores (for example A, B, C, D or F), categories (for example good, bad), and so forth.
  • the scoring models may be configured to incorporate information on consumers that are flagged as potential “bust outs,” but do not end up as “bust outs.” The system may use the flagging of such “false positives” to look for other potentially harmful activity and/or further refine the scoring model.
  • the fraud detection system may advantageously be used alone or in combination with other scoring models and credit or demographic attributes to analyze a portfolio of consumers or prospective consumers.
  • these scores and/or attributes may be used with customizable thresholds (for example, tolerance levels for an amount of change).
  • one scoring model may evaluate changes in utilization, such as a consumer's use of credit against maximum available credit, and detect unusual velocity such as the number of new accounts opened or inquiries received in a certain time frame. Attributes may include changes in demographic information, such as a change in address or phone number.
  • One scoring model may detect a pattern of suspicious payment behaviors, such as nonpayment, delinquency, returned payment, smaller-than-usual payments, or larger-than-usual payments.
  • Other elements of the scoring model may include cross-database entity matching and pattern analysis to detect organized and/or collusive behaviors. It is recognized that many other attributes, scores, and/or model elements that may be used.
  • model is a broad term, and generally refers without limitation to systems, devices, and methods for amplifying, selecting, filtering, excluding, predicting, and/or identifying subsets of a dataset that are relevant, substantially relevant, and/or statistically relevant to the user.
  • the terms “consumer” and “consumers” may include applicants, customers, individuals, entities, groups of individuals, (for example, married couples, domestic partners, families, co-workers, and the likes), and so forth.
  • financial entity credit providers
  • credit issuers credit issuers
  • financial institutions clients
  • utility providers for example, telecommunications, gas, electric, water, sewer, or the like
  • bankcard issuers for example, credit card issuers
  • mortgage (for example, sub-prime) lenders and the like.
  • FIG. 1 shows a sample embodiment for using a scoring model that predicts bust out fraud using credit bureau data.
  • Company A 100 is a department store that provides credit cards for a large number of consumers.
  • Company A 100 has been having problems with bust out accounts where several of its consumers have built up their credit, reached a maximum credit line on their accounts, and then abandoned their accounts.
  • Company A 100 wants to know before consumers abandon their accounts, whether a particular consumer is engaging in bust out fraud.
  • Company A's 100 own consumer data does not provide a full picture of a consumer since the bust out behavior may be the result of a consumer's activity at other companies, such as Company B and/or Company C. Accordingly, Company decides to contact Credit Bureau 200 for assistance.
  • the Credit Bureau 200 stores data 220 about consumers, and part of that data includes consumer credit activities, balance, available credit and utilization, depth of credit experience, delinquency and derogatory statuses on tradelines, both current and historical, derogatory public records and inquiry history.
  • the Credit Bureau 200 decides to use this data 220 to create a bust out model 210 that scores consumer data indicating whether a consumer is engaged in bust out fraud.
  • the Credit Bureau 200 collects bad and good data from its credit bureau data 220 , analyzes the data, and creates a bust out model 210 that predicts which consumers may be involved in bust out fraud.
  • Company A 100 then sends the Credit Bureau 200 a set of its consumer data for Company A's 100 existing customers over the network 300 .
  • the Credit Bureau 200 applies the bust out model 210 in batch mode to Company A's 100 set of consumer data to determine which consumers may be involved in bust out fraud and creates a set of bust out score data.
  • This bust out score data includes bust out scores along with consumer identifiers for each score.
  • the Credit Bureau 200 then sends the bust out score data back to Company A 100 over the network 300 , and Company A uses the scores to flag existing consumers for immediate investigation.
  • Company A 100 may also send the Credit Bureau 200 a set of consumer data for its prospective consumers, which are consumers Company A 100 would like to send an offer of credit.
  • the Credit Bureau 200 applies the bust out model 210 in batch mode to Company A's 100 set of consumer data to determine which consumers may be involved in bust out fraud and creates a set of bust out scores. For this data, Company A 100 has requested that the Credit Bureau 200 append the scores to the set of consumer data. Thus, Credit Bureau 200 then sends the set of consumer data, which now includes the scores, back to Company A 100 .
  • Company A 100 uses the scores to remove some of the consumers from the set of prospective consumers since Company A 100 does not want to extend an offer of credit to a consumer who has a high likelihood to be engaged in bust out fraud.
  • Company A 100 sends the Credit Bureau 200 a set of consumer data for new customers that are applying at the store for credit from Company A 100 .
  • the Credit Bureau 200 then applies the bust out model 210 to the set of consumer data and sends bust out score data, which includes a score for each consumer in the set of consumer data, back to Company A.
  • Company A then uses the scores to decide whether to approve or deny the credit applications for each of the consumers.
  • FIG. 1 and the example scenario above provide an embodiment of using the systems and methods disclosed here, and are not intended to be limiting in any way.
  • the scoring models are created using samples of credit bureau data using both bad data (for example, bust out account data) and good data (for example, non-fraud account data).
  • the samples of credit bureau data include a minimum number of bad accounts, such as, for example, 100 bust out accounts, 1000 bust out accounts, 3128 bust out accounts, or 5000 bust out accounts, though the number of bad accounts included may vary.
  • the sample of credit bureau data may also include a random sampling of non-fraud accounts or a selected sampling of non-fraud accounts.
  • non-fraud data includes credit bureau data for accounts that are not involved in bust out fraud, whereas in other embodiments, non-fraud data includes credit bureau data for accounts that are not involved in any type of fraud.
  • the number of non-fraud accounts is approximately 20 to 13000 times the number of bad accounts.
  • some or all of the consumer data to be scored is received from a third party.
  • the consumer data may include data for one or more consumers and may be received in real-time or in batch format.
  • the third party sends the data in an encrypted format, such as, for example PGP encryption, password protection using WinZip 9.1 or higher with 256-Bit encryption, or any other encryption scheme.
  • the consumer data may be sent via a secure connection, an email, File Transmission Protocol site, ConnectDirect Mailbox, a disk, tape drive, zip drive, CD-ROM, and so forth.
  • the third party providing the consumer data is the same party that is receiving the bust out score data. It is recognized that in other embodiments, a different party may receive the bust out score data than the one that submits the consumer data, and/or multiple parties may provide consumer data and/or multiple parties may receive the bust out score data.
  • the bust out score data includes the scores generated by the scoring model along with corresponding identifiers for the consumers in the set of consumers data.
  • the bust out score data may also include reason code data that indicates factors that contributed to one or more of the scores.
  • the bust out score data may include data for one or more consumer and may be sent in real-time or in batch format. In other embodiments, the bust out score data only includes scores, includes other consumer data, and or is appended to the consumer data.
  • the bust out score data is sent to a third party in an encrypted format, such as, for example PGP encryption, password protection using WinZip 9.1 or higher with 256-Bit encryption, or any other encryption scheme.
  • the bust out score data may be sent via a secure connection, an email, File Transmission Protocol site, ConnectDirect Mailbox, a disk, tape drive, zip drive, CD-ROM, and so forth.
  • FIG. 2 illustrates one embodiment of a fraud detection system 410 that creates scoring models using credit bureau data, where the scoring models predict whether a consumer will engage in bust out fraud.
  • the fraud detection system 410 also applies the created scoring models to predict whether a particular consumer or set of consumers are engaging in bust out fraud and scores the consumer or set of consumers to indicate whether they are likely involved in bust out fraud.
  • the exemplary fraud detection system 410 communicates with a third party system 420 via a communications medium 430 and includes a scoring module 414 for creating a scoring model using credit bureau data and scoring consumers in a data file along with a customization module 416 that allows the third party system 420 to set preferences, thresholds and/or tolerance levels for defining “bust out” data, creating the scoring model, applying the scoring model, formatting the bust out score data, and setting up the data exchange.
  • the fraud detection system 410 also includes a processor (not shown) configured to run modules, such as 414 and 416 .
  • the fraud detection system 410 also includes a credit bureau database 500 that stores credit bureau data, such as, for example, consumer data, account data, non-fraud account data, and/or bad account data.
  • the fraud detection system retrieves credit bureau data from the credit bureau database 500 and uses that data to create a scoring model.
  • the fraud detection system 410 then receives third party system 420 consumer data 425 and applies the scoring model to the third party system 420 consumer data 425 .
  • the fraud detection system 410 can also apply the scoring model to consumer data 455 from other third party systems 450 .
  • the fraud detection system 410 may also include a consumer data database 502 that stores all or a subset of the third party consumer data 425 as well as some or all of the bust out score data.
  • the consumer data database 502 may store consumer identity information and a history information regarding one or more of the provided scores. It some embodiments, the fraud detection system 410 may also communicate with other systems (not shown).
  • the fraud detection system 410 and/or the third party systems 420 , 450 run on one or more computing devices. Moreover, in some embodiments, the features of the fraud detection system 410 and/or the third party systems 420 , 450 may be available via a fully-hosted application service provider (ASP) that manages and provides communication between the fraud detection system 410 and one or more of the third party systems 420 , 450 via a web interface or other interface. In other embodiments, the fraud detection system 410 and/or the third party systems 420 , 450 may be available via partially-hosted ASPs or other providers. In yet further embodiments, the fraud detection system 410 and/or the third party systems 420 , 450 may be a client-side installed solution allowing for direct communication between the fraud detection system 410 and one or more of the third party systems 420 , 450 .
  • ASP application service provider
  • the computing device is IBM, Macintosh, or Linux/Unix compatible devices.
  • the computing device comprises a server, a laptop computer, a cell phone, a personal digital assistant, a kiosk, or an audio player, for example.
  • the computing device includes one or more CPUs, which may each include microprocessors.
  • the computing device may further include one or more memory devices, such as random access memory (RAM) for temporary storage of information and read only memory (ROM) for permanent storage of information, and one or more mass storage devices, such as hard drives, diskettes, or optical media storage devices.
  • RAM random access memory
  • ROM read only memory
  • the modules of the computing are in communication via a standards based bus system, such as bus systems using Peripheral Component Interconnect (PCI), Microchannel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA (EISA) architectures, for example.
  • PCI Peripheral Component Interconnect
  • ISA Industrial Standard Architecture
  • EISA Extended ISA
  • components of the computing device communicate via a network, such as a local area network that may be secured.
  • the computing device is generally controlled and coordinated by operating system software, such as the Windows 95, Windows 98, Windows NT, Windows 2000, Windows XP, Windows Vista, Linux, SunOS, Solaris, PalmOS, Blackberry OS, or other compatible operating systems.
  • operating system software such as the Windows 95, Windows 98, Windows NT, Windows 2000, Windows XP, Windows Vista, Linux, SunOS, Solaris, PalmOS, Blackberry OS, or other compatible operating systems.
  • the operating system may be any available operating system, such as MAC OS X.
  • the computing device may be controlled by a proprietary operating system.
  • Conventional operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, and I/O services, and provide a user interface, such as a graphical user interface (GUI), among other things.
  • GUI graphical user interface
  • the computing device may include one or more commonly available input/output (I/O) devices and interfaces, such as a keyboard, mouse, touchpad, microphone, and printer. Thus, in one embodiment the computing device may be controlled using the keyboard and mouse input devices, while in another embodiment the user may provide voice commands to the computing device via a microphone.
  • the I/O devices and interfaces include one or more display device, such as a monitor, that allows the visual presentation of data to a user. More particularly, a display device provides for the presentation of GUIs, application software data, and multimedia presentations, for example.
  • the computing device may also include one or more multimedia devices, such as speakers, video cards, graphics accelerators, and microphones, for example.
  • the computing devices include a communication interface to various external devices and the communications medium 430 via wired or wireless communication links.
  • the data sources may include one or more internal and/or external data sources.
  • one or more of the data sources may be implemented using a relational database, such as, for example, Sybase, Oracle, CodeBase and Microsoft® SQL Server as well as other types of databases such as, for example, a flat file database, an entity-relationship database, and object-oriented database, and/or a record-based database.
  • module refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, C or C++.
  • the module may include, by way of example, components, such as, for example, software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
  • a software module may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python.
  • software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts.
  • Software instructions may be embedded in firmware, such as an EPROM.
  • hardware modules may be comprised of connected logic units, such as gates and flip-flops, and/or may be comprised of programmable units, such as programmable gate arrays or processors.
  • the modules described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage.
  • the communications medium 430 is one or more networks, such as, for example, a LAN, WAN, or the Internet, for example, via a wired, wireless, or combination of wired and wireless, communication link.
  • the communications medium 430 communicates with various computing devices and/or other electronic devices via wired or wireless communication links.
  • the computing device may be configured to communicate with the communications medium using any combination of one or more networks, LANs, WANs, or the Internet, for example, via a wired, wireless, or combination of wired and wireless communication links.
  • one or more the third party systems 420 , 450 and the fraud detection system 410 may communicate using two or more different types of communications mediums 430 , and the fraud detection system 410 may communicate with one or more of the third party systems 420 , 450 using different types of communications mediums 430 .
  • FIG. 3 illustrates an embodiment of a flowchart showing one method (for example, a computer implemented method) of analyzing credit bureau data (for example, bad data and good data) to create bust out models.
  • the method can be performed online, in real-time, batch, periodically, and/or on a delayed basis for individual records or a plurality of records.
  • the exemplary method may be stored as a process accessible by the scoring module 414 and/or other modules of the fraud protection system 410 .
  • the blocks described below may be removed, others may be added, and the sequence of the blocks may be altered.
  • the method is initiated (block 509 ), and the fraud detection system 410 accesses bust out credit bureau data (block 510 ).
  • the fraud detection system 410 also accesses non-fraud credit bureau data (block 520 ).
  • the bust out credit bureau data and non-fraud credit bureau data include consumer demographic, credit, and other credit bureau data (for example, historical balance data for a period of time, credit limits data for a period of time, or the like). Specific criteria for being categorized as a bust out data may vary greatly and may be based on a variety of possible data types and different ways of weighing the data.
  • the bust out and/or non-fraud credit bureau data may also include archived data or a random selection of credit bureau data.
  • the fraud detection system 410 develops a model using the bust out credit bureau data and the non-fraud credit bureau data (block 530 ), which determines whether a consumer is involved in bust out fraud.
  • the development of the model comprises identifying consumer characteristics, attributes, or segmentations that are statistically correlated (for example, a statistically significant correlation) with the occurrence of a bust out account.
  • the development of the model may include developing a set of heuristic rules, filters, and/or electronic data screens to determine and/or identify and/or predict which consumer profiles would be classified as a bust out account based on the credit bureau data.
  • the development of the model can also include developing a set of heuristic rules, filters, and/or electronic data screens to determine and/or identify and/or predict which data is attributable to bust out accounts based on the credit bureau data.
  • FIG. 3 it is recognized that other embodiments of FIG. 3 may be used.
  • the method of FIG. 3 could be repeatedly performed to create multiple bust out models, the non-fraud credit bureau data may be accessed before the bust out credit bureau data, and/or the bust out credit bureau data and the non-fraud credit bureau data may be accessed at the same time or in parallel.
  • FIG. 4 illustrates an embodiment of a flowchart illustrating a method of applying a bust out model, which was created using credit data, to predict whether a consumer to be involved in bust out fraud.
  • the exemplary method may be stored as a process accessible by the scoring module 414 and/or other components of the fraud detection system 410 .
  • the blocks described below may be removed, others may be added, and the sequence of the blocks may be altered.
  • the method is initiated (block 609 ), and the fraud detection system 410 selects or receives consumer data (block 610 ).
  • the consumer data includes data for one or more consumers.
  • the fraud detection system 410 may also obtain consumer data from a third party system 420 , 450 and/or the consumer data database 502 .
  • the fraud detection system 410 analyzes the consumer data by applying the bust out model to the data, generates generate a score(s) indicating the likelihood that the consumer(s) is involved in bust out fraud (block 620 ).
  • the fraud detection system 410 then outputs bust out score data (block 630 ).
  • the bust out score data may be sent to a third party system 420 , the user, another module, another system, and/or stored in the consumer data database 502 , or the like.
  • FIG. 4 it is recognized that other embodiments of FIG. 4 may be used.
  • the method of FIG. 4 could stored the bust out score data in a database and/or apply additional rules such as, for example, removing data for consumers that are not involved in bust out fraud.

Abstract

Systems and methods are disclosed for predicting bust out fraud using credit bureau data. In one embodiment, credit bureau scoring models are created using credit bureau data to detect bust out fraud. The credit bureau scoring models may be then applied to consumer data to determine whether a consumer is involved in bust out fraud.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • The present application is a continuation of U.S. patent application Ser. No. 12/904,088, filed Oct. 13, 2010, entitled SYSTEMS AND METHODS FOR DETECTING BUST OUT FRAUD USING CREDIT DATA, which is a continuation of U.S. patent application Ser. No. 12/220,320, filed Jul. 23, 2008, now U.S. Pat. No. 7,991,689. The foregoing applications and patent are hereby incorporated herein by reference in their entirety, including specifically but not limited to the systems and methods relating to bust out fraud detection.
  • BACKGROUND
  • 1. Field of the Invention
  • The disclosure relates generally to the field of financial protection. The disclosure relates specifically to the field of fraud detection.
  • 2. Description of the Related Art
  • The occurrence of fraud and related dollar losses is growing because it has been very difficult for the financial industry to detect bust out fraud using traditional fraud detection systems. Traditional fraud detection systems are typically applied in two ways: at the point of credit application (sometimes referred to as application fraud systems), or through ongoing monitoring by a financial institute of its consumer transactions compared against an established profile of that consumer's behavior (sometimes referred to as transaction fraud systems).
  • Application fraud systems were not designed to detect fraud that takes place after the consumer's application is approved and credit is granted (sometimes referred to as post-book fraud); consequently, such systems often prove ineffective in detecting post-book fraud. For example, if a consumer is opening an account in his/her own name intending to commit fraud in the future, application fraud systems may verify the consumer's identity without analyzing the likelihood of the consumer engaging in fraud after the account is opened. Similarly, transaction fraud systems are ineffective in situations when evolving types of fraud that take advantage of more than one financial institution.
  • SUMMARY OF THE DISCLOSURE
  • One specific type of fraud that traditional fraud systems are unable to detect is fraud that typically occurs in an organized fashion, across multiple credit issuers, and involves a build-up phase of seemingly normal consumer behavior followed by an exceedingly large number of purchases, cash advances, or other uses of credit, and then subsequent abandonment of the account. This fraud is sometimes referred to as bust out fraud.
  • Consequently, it would be advantageous to have methods and systems that automatically detect such fraudulent activity. In some embodiments, credit bureau scoring models are created using credit bureau data to detect bust out fraud. The credit bureau scoring models may be then applied to consumer data to determine whether a consumer is likely involved in bust out fraud before a consumer abandons his accounts.
  • In one embodiment, a computer implemented method of developing a data filter for identifying bust out fraud is disclosed. The computer implemented method may include electronically developing a credit bureau bust out model that predicts the propensity of a consumer to be engaged in bust out fraud analyzing substantially only credit bureau data.
  • In another embodiment, a bust out fraud detection system is disclosed. The bust out fraud system may include a processor configured to run software modules; a data storage device storing a plurality of consumer records, the data storage device in electronic communication with the processor; and a bust out module configured to identify a subset of the plurality of records from the data storage device, receive a credit bureau bust out model from a storage repository, the credit bureau bust out model predicting which consumer records are likely involved and created using substantially only credit bureau data, apply the credit bureau bust out model to each of the subset of the plurality of consumer records to generate a credit bureau bust out score for each of the subset of the plurality of consumer records, and store in a storage repository the credit bureau bust out score associated with the subset of the plurality of the consumer records; and where the processor is able to run the bust out module.
  • In a further embodiment, a computer implemented method for generating scores that indicate bust out fraud is provided. The computer implemented method may include electronically identifying a plurality of consumer records; electronically receiving a bust out filter from a storage repository, the bust out filter created using substantially only credit bureau data; electronically applying the bust out filter to each of the plurality consumer records to generate a bust out score for each of the plurality of consumer records; and electronically storing in a storage repository the bust out score associated with each of the consumer records.
  • For purposes of the summary, certain aspects, advantages and novel features of the invention have been described herein. Of course, it is to be understood that not necessarily all such aspects, advantages or features will be embodied in any particular embodiment of the invention. Thus, for example, those skilled in the art will recognize that the invention may be embodied or carried out in a manner that achieves one advantage or group of advantages as taught herein without necessarily achieving other advantages as may be taught or suggested herein.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The foregoing and other features, aspects and advantages of the present invention are described in detail below with reference to the drawings of various embodiments, which are intended to illustrate and not to limit the invention. The drawings comprise the following figures.
  • FIG. 1 illustrates one embodiment of example scenario for detecting bust out fraud using credit bureau data.
  • FIG. 2 illustrates one embodiment of a computer hardware system configured to run software for implementing one or more embodiments of the fraud detection system described herein.
  • FIG. 3 illustrates one embodiment of a flowchart diagram for analyzing data to create a credit bureau bust out model using credit bureau data.
  • FIG. 4 illustrates one embodiment of a flowchart diagram for analyzing consumer data to apply a credit bureau bust out model and generate credit bureau bust out scores.
  • DETAILED DESCRIPTION
  • Embodiments of the invention will now be described with reference to the accompanying figures, wherein like numerals refer to like elements throughout. The terminology used in the description presented herein is not intended to be interpreted in any limited or restrictive manner, simply because it is being utilized in conjunction with a detailed description of certain specific embodiments of the invention. Furthermore, embodiments of the invention may comprise several novel features, no single one of which is solely responsible for its desirable attributes or which is essential to practicing the inventions herein described. In addition, it is recognized that a feature of one embodiment may be included as a feature in a different embodiment.
  • Some embodiments discussed herein provide systems and methods for predicting bust out fraud. “Bust out” fraud is a hybrid fraud and credit problem were an individual and/or entity opens multiple lines of credit/accounts increases utilization and then subsequently abandons the accounts. The line of credit/accounts may include credit cards accounts, debit card accounts, equity lines, and so forth. In one embodiment, scoring models are specifically developed to predict bust out fraud using credit bureau data. One advantage to using credit bureau data is that it provides information about the consumer across multiple consumer accounts at multiple institutions. The scoring models can be applied to one or more sets of consumer data to generate a score for each consumer predicting the likelihood that the consumer is involved in bust out fraud. The scoring models can then be used alone or in combination with other scores and credit or demographic attributes to evaluate a consumer when opening an account, to monitor a portfolio of consumers, and/or to weed out undesirable prospective customers. In addition, the scoring model may be used in an online environment or in a batch environment.
  • In one embodiment, the scoring model is created using “bad” credit bureau data, which includes data for accounts that were classified as bust out accounts. In some embodiments, an account is classified as a bust out fraud account according to two aspects, action and intent. Example account actions include, an account balance approaching or exceeding its limit, payments with bad checks, and/or similar behavior on other accounts linked to the same account holder. Example account actions demonstrating intent include requests for a credit limit increase, requests for adding authorized users, frequent balance inquiries, use of balance transfers and convenience checks, and/or being unable to contact the account holder. In other embodiments, other requirements or definitions may be used to classify an account or a consumer as a bust out account. Thus, the bad credit bureau data used to create the scoring module may include a variety of data including, for example, data indicating that an account is unpaid, the account is delinquent (for example 30+ days, 60+ days, 90+ days, 120+ days), the accounts balance is close to or over its limit, a payment on the account has been returned or bounced, attempts to contact the account owner via the providers phone number(s), address(es), and/or email address(es) have failed, and/or that similar data exists at one or more financial institutions. The scoring model may also be created using “good” credit bureau data, which is data from non-fraud consumers.
  • The scoring models may be configured in a variety of ways. For example, the scoring models may be configured to enhance the prediction of bust out fraud, to reflect current bust out fraud trends, to increase the operational efficiency of identifying consumers that may be involved in bust out fraud, and/or to compliment existing fraud detection/prevention tools. The scoring models also may be configured to predict bust out fraud for a certain amount of time prior to the abandoning of any accounts, such as, for example 6 to 8 weeks, or 1 to 3 months. In addition, the scoring models may be configured to detect a significant portion of bust out fraud such as, for example 35%, 60%, or 78% of bust out fraud. The scoring models also may be configured designate high risk consumers (for example, consumers with a higher score) from low risk consumers (for example, consumers with a lower score) so that the user of the system can focus on dealing with the higher risk consumers. The scoring model may also factor in the potential amount at risk such that consumers that are most likely involved in bust out fraud and that have the highest potential collection balance are scored the highest.
  • The scoring models may utilize a variety of scoring methods, including numeric scores where the lower number indicates bust out fraud, numeric scores where a higher number indicates bust out fraud, letters scores (for example A, B, C, D or F), categories (for example good, bad), and so forth.
  • Moreover, the scoring models may be configured to incorporate information on consumers that are flagged as potential “bust outs,” but do not end up as “bust outs.” The system may use the flagging of such “false positives” to look for other potentially harmful activity and/or further refine the scoring model.
  • In some embodiments, the fraud detection system may advantageously be used alone or in combination with other scoring models and credit or demographic attributes to analyze a portfolio of consumers or prospective consumers. In some embodiments, these scores and/or attributes may be used with customizable thresholds (for example, tolerance levels for an amount of change). For example, one scoring model may evaluate changes in utilization, such as a consumer's use of credit against maximum available credit, and detect unusual velocity such as the number of new accounts opened or inquiries received in a certain time frame. Attributes may include changes in demographic information, such as a change in address or phone number. One scoring model may detect a pattern of suspicious payment behaviors, such as nonpayment, delinquency, returned payment, smaller-than-usual payments, or larger-than-usual payments. Other elements of the scoring model may include cross-database entity matching and pattern analysis to detect organized and/or collusive behaviors. It is recognized that many other attributes, scores, and/or model elements that may be used.
  • In general, the term “model” as used herein is a broad term, and generally refers without limitation to systems, devices, and methods for amplifying, selecting, filtering, excluding, predicting, and/or identifying subsets of a dataset that are relevant, substantially relevant, and/or statistically relevant to the user. In addition, the terms “consumer” and “consumers” may include applicants, customers, individuals, entities, groups of individuals, (for example, married couples, domestic partners, families, co-workers, and the likes), and so forth. Furthermore, the terms “financial entity,” “credit providers,” “credit issuers,” “financial institutions,” “clients,” “utility providers,” “utility service providers,” “phone service providers,” “financial service providers,” are broad interchangeable terms and generally refer without limitation to banks, financial companies, credit unions, savings institutions, retailers, utility (for example, telecommunications, gas, electric, water, sewer, or the like) providers, bankcard issuers, credit card issuers, mortgage (for example, sub-prime) lenders, and the like.
  • I. Example Scenario
  • One example scenario will now be discussed with respect to FIG. 1, which shows a sample embodiment for using a scoring model that predicts bust out fraud using credit bureau data.
  • In the example, Company A 100 is a department store that provides credit cards for a large number of consumers. However, Company A 100 has been having problems with bust out accounts where several of its consumers have built up their credit, reached a maximum credit line on their accounts, and then abandoned their accounts. Thus, Company A 100 wants to know before consumers abandon their accounts, whether a particular consumer is engaging in bust out fraud. Company A's 100 own consumer data does not provide a full picture of a consumer since the bust out behavior may be the result of a consumer's activity at other companies, such as Company B and/or Company C. Accordingly, Company decides to contact Credit Bureau 200 for assistance.
  • The Credit Bureau 200 stores data 220 about consumers, and part of that data includes consumer credit activities, balance, available credit and utilization, depth of credit experience, delinquency and derogatory statuses on tradelines, both current and historical, derogatory public records and inquiry history. The Credit Bureau 200 decides to use this data 220 to create a bust out model 210 that scores consumer data indicating whether a consumer is engaged in bust out fraud. To create the bust out model 210, the Credit Bureau 200 collects bad and good data from its credit bureau data 220, analyzes the data, and creates a bust out model 210 that predicts which consumers may be involved in bust out fraud.
  • Company A 100 then sends the Credit Bureau 200 a set of its consumer data for Company A's 100 existing customers over the network 300. The Credit Bureau 200 applies the bust out model 210 in batch mode to Company A's 100 set of consumer data to determine which consumers may be involved in bust out fraud and creates a set of bust out score data. This bust out score data includes bust out scores along with consumer identifiers for each score. The Credit Bureau 200 then sends the bust out score data back to Company A 100 over the network 300, and Company A uses the scores to flag existing consumers for immediate investigation.
  • Company A 100 may also send the Credit Bureau 200 a set of consumer data for its prospective consumers, which are consumers Company A 100 would like to send an offer of credit. The Credit Bureau 200 applies the bust out model 210 in batch mode to Company A's 100 set of consumer data to determine which consumers may be involved in bust out fraud and creates a set of bust out scores. For this data, Company A 100 has requested that the Credit Bureau 200 append the scores to the set of consumer data. Thus, Credit Bureau 200 then sends the set of consumer data, which now includes the scores, back to Company A 100. Company A 100 uses the scores to remove some of the consumers from the set of prospective consumers since Company A 100 does not want to extend an offer of credit to a consumer who has a high likelihood to be engaged in bust out fraud.
  • Next, as part of its credit application process, Company A 100 sends the Credit Bureau 200 a set of consumer data for new customers that are applying at the store for credit from Company A 100. The Credit Bureau 200 then applies the bust out model 210 to the set of consumer data and sends bust out score data, which includes a score for each consumer in the set of consumer data, back to Company A. Company A then uses the scores to decide whether to approve or deny the credit applications for each of the consumers.
  • FIG. 1 and the example scenario above, provide an embodiment of using the systems and methods disclosed here, and are not intended to be limiting in any way.
  • II. Data
  • A. Credit Bureau Data
  • The scoring models are created using samples of credit bureau data using both bad data (for example, bust out account data) and good data (for example, non-fraud account data). In one embodiment, the samples of credit bureau data include a minimum number of bad accounts, such as, for example, 100 bust out accounts, 1000 bust out accounts, 3128 bust out accounts, or 5000 bust out accounts, though the number of bad accounts included may vary. The sample of credit bureau data may also include a random sampling of non-fraud accounts or a selected sampling of non-fraud accounts. In one embodiment, non-fraud data includes credit bureau data for accounts that are not involved in bust out fraud, whereas in other embodiments, non-fraud data includes credit bureau data for accounts that are not involved in any type of fraud. In one embodiment, the number of non-fraud accounts is approximately 20 to 13000 times the number of bad accounts.
  • B. Consumer Data
  • In some embodiments, some or all of the consumer data to be scored is received from a third party. The consumer data may include data for one or more consumers and may be received in real-time or in batch format. In one embodiment, the third party sends the data in an encrypted format, such as, for example PGP encryption, password protection using WinZip 9.1 or higher with 256-Bit encryption, or any other encryption scheme. In addition, the consumer data may be sent via a secure connection, an email, File Transmission Protocol site, ConnectDirect Mailbox, a disk, tape drive, zip drive, CD-ROM, and so forth.
  • In one embodiment, the third party providing the consumer data is the same party that is receiving the bust out score data. It is recognized that in other embodiments, a different party may receive the bust out score data than the one that submits the consumer data, and/or multiple parties may provide consumer data and/or multiple parties may receive the bust out score data.
  • C. Bust Out Score Data
  • In one embodiment, the bust out score data includes the scores generated by the scoring model along with corresponding identifiers for the consumers in the set of consumers data. The bust out score data may also include reason code data that indicates factors that contributed to one or more of the scores. The bust out score data may include data for one or more consumer and may be sent in real-time or in batch format. In other embodiments, the bust out score data only includes scores, includes other consumer data, and or is appended to the consumer data.
  • In one embodiment, the bust out score data is sent to a third party in an encrypted format, such as, for example PGP encryption, password protection using WinZip 9.1 or higher with 256-Bit encryption, or any other encryption scheme. In addition, the bust out score data may be sent via a secure connection, an email, File Transmission Protocol site, ConnectDirect Mailbox, a disk, tape drive, zip drive, CD-ROM, and so forth.
  • III. Fraud Detection System
  • FIG. 2 illustrates one embodiment of a fraud detection system 410 that creates scoring models using credit bureau data, where the scoring models predict whether a consumer will engage in bust out fraud. The fraud detection system 410 also applies the created scoring models to predict whether a particular consumer or set of consumers are engaging in bust out fraud and scores the consumer or set of consumers to indicate whether they are likely involved in bust out fraud. The exemplary fraud detection system 410 communicates with a third party system 420 via a communications medium 430 and includes a scoring module 414 for creating a scoring model using credit bureau data and scoring consumers in a data file along with a customization module 416 that allows the third party system 420 to set preferences, thresholds and/or tolerance levels for defining “bust out” data, creating the scoring model, applying the scoring model, formatting the bust out score data, and setting up the data exchange. The fraud detection system 410 also includes a processor (not shown) configured to run modules, such as 414 and 416. The fraud detection system 410 also includes a credit bureau database 500 that stores credit bureau data, such as, for example, consumer data, account data, non-fraud account data, and/or bad account data.
  • In one embodiment, the fraud detection system retrieves credit bureau data from the credit bureau database 500 and uses that data to create a scoring model. The fraud detection system 410 then receives third party system 420 consumer data 425 and applies the scoring model to the third party system 420 consumer data 425. In other embodiments, the fraud detection system 410 can also apply the scoring model to consumer data 455 from other third party systems 450. The fraud detection system 410 may also include a consumer data database 502 that stores all or a subset of the third party consumer data 425 as well as some or all of the bust out score data. For example, the consumer data database 502 may store consumer identity information and a history information regarding one or more of the provided scores. It some embodiments, the fraud detection system 410 may also communicate with other systems (not shown).
  • IV. System Information
  • A. Computing Devices
  • In one embodiment, the fraud detection system 410 and/or the third party systems 420, 450 run on one or more computing devices. Moreover, in some embodiments, the features of the fraud detection system 410 and/or the third party systems 420, 450 may be available via a fully-hosted application service provider (ASP) that manages and provides communication between the fraud detection system 410 and one or more of the third party systems 420, 450 via a web interface or other interface. In other embodiments, the fraud detection system 410 and/or the third party systems 420, 450 may be available via partially-hosted ASPs or other providers. In yet further embodiments, the fraud detection system 410 and/or the third party systems 420, 450 may be a client-side installed solution allowing for direct communication between the fraud detection system 410 and one or more of the third party systems 420, 450.
  • In one embodiment, the computing device is IBM, Macintosh, or Linux/Unix compatible devices. In another embodiment, the computing device comprises a server, a laptop computer, a cell phone, a personal digital assistant, a kiosk, or an audio player, for example. In one embodiment, the computing device includes one or more CPUs, which may each include microprocessors. The computing device may further include one or more memory devices, such as random access memory (RAM) for temporary storage of information and read only memory (ROM) for permanent storage of information, and one or more mass storage devices, such as hard drives, diskettes, or optical media storage devices.
  • In one embodiment, the modules of the computing are in communication via a standards based bus system, such as bus systems using Peripheral Component Interconnect (PCI), Microchannel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA (EISA) architectures, for example. In some embodiments, components of the computing device communicate via a network, such as a local area network that may be secured.
  • The computing device is generally controlled and coordinated by operating system software, such as the Windows 95, Windows 98, Windows NT, Windows 2000, Windows XP, Windows Vista, Linux, SunOS, Solaris, PalmOS, Blackberry OS, or other compatible operating systems. In Macintosh systems, the operating system may be any available operating system, such as MAC OS X. In other embodiments, the computing device may be controlled by a proprietary operating system. Conventional operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, and I/O services, and provide a user interface, such as a graphical user interface (GUI), among other things.
  • The computing device may include one or more commonly available input/output (I/O) devices and interfaces, such as a keyboard, mouse, touchpad, microphone, and printer. Thus, in one embodiment the computing device may be controlled using the keyboard and mouse input devices, while in another embodiment the user may provide voice commands to the computing device via a microphone. In one embodiment, the I/O devices and interfaces include one or more display device, such as a monitor, that allows the visual presentation of data to a user. More particularly, a display device provides for the presentation of GUIs, application software data, and multimedia presentations, for example. The computing device may also include one or more multimedia devices, such as speakers, video cards, graphics accelerators, and microphones, for example.
  • In one embodiment, the computing devices include a communication interface to various external devices and the communications medium 430 via wired or wireless communication links.
  • B. Data Sources
  • The data sources, including the consumer data 425, the credit bureau database 500, and the consumer data database 502, may include one or more internal and/or external data sources. In some embodiments, one or more of the data sources may be implemented using a relational database, such as, for example, Sybase, Oracle, CodeBase and Microsoft® SQL Server as well as other types of databases such as, for example, a flat file database, an entity-relationship database, and object-oriented database, and/or a record-based database.
  • C. Modules
  • In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, C or C++. The module may include, by way of example, components, such as, for example, software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. A software module may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software instructions may be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules may be comprised of connected logic units, such as gates and flip-flops, and/or may be comprised of programmable units, such as programmable gate arrays or processors. The modules described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage.
  • D. Communications Medium
  • In the embodiment of FIG. 2, the communications medium 430 is one or more networks, such as, for example, a LAN, WAN, or the Internet, for example, via a wired, wireless, or combination of wired and wireless, communication link. The communications medium 430 communicates with various computing devices and/or other electronic devices via wired or wireless communication links. For example, the computing device may be configured to communicate with the communications medium using any combination of one or more networks, LANs, WANs, or the Internet, for example, via a wired, wireless, or combination of wired and wireless communication links. It is also recognized that one or more the third party systems 420, 450 and the fraud detection system 410 may communicate using two or more different types of communications mediums 430, and the fraud detection system 410 may communicate with one or more of the third party systems 420, 450 using different types of communications mediums 430.
  • V. Flowcharts
  • A. Creating Bust Out Models Using Credit Data
  • FIG. 3 illustrates an embodiment of a flowchart showing one method (for example, a computer implemented method) of analyzing credit bureau data (for example, bad data and good data) to create bust out models. The method can be performed online, in real-time, batch, periodically, and/or on a delayed basis for individual records or a plurality of records. The exemplary method may be stored as a process accessible by the scoring module 414 and/or other modules of the fraud protection system 410. In different embodiments, the blocks described below may be removed, others may be added, and the sequence of the blocks may be altered.
  • With reference to FIG. 3, the method is initiated (block 509), and the fraud detection system 410 accesses bust out credit bureau data (block 510). The fraud detection system 410 also accesses non-fraud credit bureau data (block 520). In an embodiment, the bust out credit bureau data and non-fraud credit bureau data include consumer demographic, credit, and other credit bureau data (for example, historical balance data for a period of time, credit limits data for a period of time, or the like). Specific criteria for being categorized as a bust out data may vary greatly and may be based on a variety of possible data types and different ways of weighing the data. The bust out and/or non-fraud credit bureau data may also include archived data or a random selection of credit bureau data.
  • The fraud detection system 410 develops a model using the bust out credit bureau data and the non-fraud credit bureau data (block 530), which determines whether a consumer is involved in bust out fraud. In one embodiment, the development of the model comprises identifying consumer characteristics, attributes, or segmentations that are statistically correlated (for example, a statistically significant correlation) with the occurrence of a bust out account. The development of the model may include developing a set of heuristic rules, filters, and/or electronic data screens to determine and/or identify and/or predict which consumer profiles would be classified as a bust out account based on the credit bureau data. The development of the model can also include developing a set of heuristic rules, filters, and/or electronic data screens to determine and/or identify and/or predict which data is attributable to bust out accounts based on the credit bureau data.
  • It is recognized that other embodiments of FIG. 3 may be used. For example, the method of FIG. 3 could be repeatedly performed to create multiple bust out models, the non-fraud credit bureau data may be accessed before the bust out credit bureau data, and/or the bust out credit bureau data and the non-fraud credit bureau data may be accessed at the same time or in parallel.
  • B. Using The Bust Out Models To Score Consumer Data
  • FIG. 4 illustrates an embodiment of a flowchart illustrating a method of applying a bust out model, which was created using credit data, to predict whether a consumer to be involved in bust out fraud. The exemplary method may be stored as a process accessible by the scoring module 414 and/or other components of the fraud detection system 410. In some embodiments, the blocks described below may be removed, others may be added, and the sequence of the blocks may be altered.
  • With reference to FIG. 4, the method is initiated (block 609), and the fraud detection system 410 selects or receives consumer data (block 610). The consumer data includes data for one or more consumers. In some embodiments, the fraud detection system 410 may also obtain consumer data from a third party system 420, 450 and/or the consumer data database 502. The fraud detection system 410 analyzes the consumer data by applying the bust out model to the data, generates generate a score(s) indicating the likelihood that the consumer(s) is involved in bust out fraud (block 620). The fraud detection system 410 then outputs bust out score data (block 630). The bust out score data may be sent to a third party system 420, the user, another module, another system, and/or stored in the consumer data database 502, or the like.
  • It is recognized that other embodiments of FIG. 4 may be used. For example, the method of FIG. 4 could stored the bust out score data in a database and/or apply additional rules such as, for example, removing data for consumers that are not involved in bust out fraud.
  • VI. Additional Embodiments
  • Although the foregoing has been described in terms of some embodiments, other embodiments will be apparent to those of ordinary skill in the art from the disclosure herein. Moreover, the described embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel methods and systems described herein may be embodied in a variety of other forms without departing from the spirit thereof. Accordingly, other combinations, omissions, substitutions, and modifications will be apparent to the skilled artisan in view of the disclosure herein.

Claims (13)

1. A bust out fraud detection system, the system comprising:
a processor configured to run software modules;
a data storage device storing a plurality of consumer records, the data storage device in electronic communication with the processor; and
a bust out module configured to:
identify a subset of the plurality of records from the data storage device;
receive a credit bureau bust out model from a storage repository, the credit bureau bust out model predicting which consumer records are likely involved and created using substantially only credit bureau data;
apply the credit bureau bust out model to each of the subset of the plurality of consumer records to generate a credit bureau bust out score for each of the subset of the plurality of consumer records; and
store in a storage repository the credit bureau bust out score associated with the subset of the plurality of the consumer records; and
the processor able to run the bust out module.
2. The bust out fraud detecting system of claim 1, wherein the plurality of consumer records is received in real time.
3. The bust out fraud detection system of claim 1, wherein the plurality of consumer records relate to prospective consumers that may be approved for credit.
4. The bust out fraud detection system of claim 1, wherein the plurality of consumer records is received in a batch.
5. The bust out fraud detection system of claim 1, wherein the plurality of consumer records represent existing consumer accounts.
6. The bust out fraud detection system of claim 1, wherein the credit bureau bust out model predicts fraud one to three months in advance.
7. A computer implemented method for generating scores that indicate bust out fraud comprising:
identifying a plurality of consumer records;
receiving a bust out filter from a storage repository, the bust out filter created using substantially only credit bureau data;
applying the bust out filter to each of the plurality consumer records to generate a bust out score for each of the plurality of consumer records; and
storing in a storage repository the bust out score associated with each of the consumer records.
8. The computer implemented method of claim 7, wherein the plurality of consumer records is received in real time.
9. The computer implemented method of claim 7, wherein the plurality of consumer records related to potential prospective customers that may be approved for credit.
10. The computer implemented method of claim 7, wherein the plurality of consumer records is received in a batch.
11. The computer implemented method of claim 7, wherein the plurality of consumer records represents existing consumer accounts.
12. The computer implemented method of claim 7, wherein the bust out model predicts fraud one to three months in advance.
13. A storage medium having a computer program stored thereon for causing a suitably programmed system to process computer-program code by performing the method of claim 7 when such program is executed on the system.
US13/186,130 2008-07-23 2011-07-19 Systems and methods for detecting bust out fraud using credit data Abandoned US20120158574A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US13/186,130 US20120158574A1 (en) 2008-07-23 2011-07-19 Systems and methods for detecting bust out fraud using credit data

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US12/220,320 US7991689B1 (en) 2008-07-23 2008-07-23 Systems and methods for detecting bust out fraud using credit data
US12/904,088 US8001042B1 (en) 2008-07-23 2010-10-13 Systems and methods for detecting bust out fraud using credit data
US13/186,130 US20120158574A1 (en) 2008-07-23 2011-07-19 Systems and methods for detecting bust out fraud using credit data

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US12/904,088 Continuation US8001042B1 (en) 2008-07-23 2010-10-13 Systems and methods for detecting bust out fraud using credit data

Publications (1)

Publication Number Publication Date
US20120158574A1 true US20120158574A1 (en) 2012-06-21

Family

ID=44314444

Family Applications (3)

Application Number Title Priority Date Filing Date
US12/220,320 Active 2029-06-15 US7991689B1 (en) 2008-07-23 2008-07-23 Systems and methods for detecting bust out fraud using credit data
US12/904,088 Active US8001042B1 (en) 2008-07-23 2010-10-13 Systems and methods for detecting bust out fraud using credit data
US13/186,130 Abandoned US20120158574A1 (en) 2008-07-23 2011-07-19 Systems and methods for detecting bust out fraud using credit data

Family Applications Before (2)

Application Number Title Priority Date Filing Date
US12/220,320 Active 2029-06-15 US7991689B1 (en) 2008-07-23 2008-07-23 Systems and methods for detecting bust out fraud using credit data
US12/904,088 Active US8001042B1 (en) 2008-07-23 2010-10-13 Systems and methods for detecting bust out fraud using credit data

Country Status (1)

Country Link
US (3) US7991689B1 (en)

Cited By (63)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090035069A1 (en) * 2007-07-30 2009-02-05 Drew Krehbiel Methods and apparatus for protecting offshore structures
US20100094758A1 (en) * 2008-10-13 2010-04-15 Experian Marketing Solutions, Inc. Systems and methods for providing real time anonymized marketing information
US8364588B2 (en) 2007-05-25 2013-01-29 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US8364518B1 (en) 2009-07-08 2013-01-29 Experian Ltd. Systems and methods for forecasting household economics
US8452611B1 (en) 2004-09-01 2013-05-28 Search America, Inc. Method and apparatus for assessing credit for healthcare patients
US8464939B1 (en) 2007-12-14 2013-06-18 Consumerinfo.Com, Inc. Card registry systems and methods
US8595101B1 (en) 2008-09-08 2013-11-26 Exerian Information Solutions, Inc. Systems and methods for managing consumer accounts using data migration
US8621244B1 (en) 2012-10-04 2013-12-31 Datalogix Inc. Method and apparatus for matching consumers
US8626560B1 (en) 2009-06-30 2014-01-07 Experian Information Solutions, Inc. System and method for evaluating vehicle purchase loyalty
US8626646B2 (en) 2006-10-05 2014-01-07 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US8639920B2 (en) 2009-05-11 2014-01-28 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US20140129420A1 (en) * 2012-11-08 2014-05-08 Mastercard International Incorporated Telecom social network analysis driven fraud prediction and credit scoring
US8725613B1 (en) 2010-04-27 2014-05-13 Experian Information Solutions, Inc. Systems and methods for early account score and notification
US8738515B2 (en) 2007-04-12 2014-05-27 Experian Marketing Solutions, Inc. Systems and methods for determining thin-file records and determining thin-file risk levels
US8775299B2 (en) 2011-07-12 2014-07-08 Experian Information Solutions, Inc. Systems and methods for large-scale credit data processing
US8781953B2 (en) 2003-03-21 2014-07-15 Consumerinfo.Com, Inc. Card management system and method
US8856894B1 (en) 2012-11-28 2014-10-07 Consumerinfo.Com, Inc. Always on authentication
US8930263B1 (en) 2003-05-30 2015-01-06 Consumerinfo.Com, Inc. Credit data analysis
US8930251B2 (en) 2008-06-18 2015-01-06 Consumerinfo.Com, Inc. Debt trending systems and methods
US8930262B1 (en) 2010-11-02 2015-01-06 Experian Technology Ltd. Systems and methods of assisted strategy design
US9058340B1 (en) 2007-11-19 2015-06-16 Experian Marketing Solutions, Inc. Service for associating network users with profiles
US9058627B1 (en) 2002-05-30 2015-06-16 Consumerinfo.Com, Inc. Circular rotational interface for display of consumer credit information
US9147042B1 (en) 2010-11-22 2015-09-29 Experian Information Solutions, Inc. Systems and methods for data verification
US9152727B1 (en) 2010-08-23 2015-10-06 Experian Marketing Solutions, Inc. Systems and methods for processing consumer information for targeted marketing applications
US9256904B1 (en) 2008-08-14 2016-02-09 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
USD759690S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD759689S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD760256S1 (en) 2014-03-25 2016-06-28 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
US9483606B1 (en) 2011-07-08 2016-11-01 Consumerinfo.Com, Inc. Lifescore
US9558519B1 (en) 2011-04-29 2017-01-31 Consumerinfo.Com, Inc. Exposing reporting cycle information
US9576030B1 (en) 2014-05-07 2017-02-21 Consumerinfo.Com, Inc. Keeping up with the joneses
US9607336B1 (en) 2011-06-16 2017-03-28 Consumerinfo.Com, Inc. Providing credit inquiry alerts
US9633322B1 (en) 2013-03-15 2017-04-25 Consumerinfo.Com, Inc. Adjustment of knowledge-based authentication
US9652802B1 (en) 2010-03-24 2017-05-16 Consumerinfo.Com, Inc. Indirect monitoring and reporting of a user's credit data
US9654541B1 (en) 2012-11-12 2017-05-16 Consumerinfo.Com, Inc. Aggregating user web browsing data
US9690820B1 (en) 2007-09-27 2017-06-27 Experian Information Solutions, Inc. Database system for triggering event notifications based on updates to database records
US9697263B1 (en) 2013-03-04 2017-07-04 Experian Information Solutions, Inc. Consumer data request fulfillment system
US9710852B1 (en) 2002-05-30 2017-07-18 Consumerinfo.Com, Inc. Credit report timeline user interface
US9830646B1 (en) 2012-11-30 2017-11-28 Consumerinfo.Com, Inc. Credit score goals and alerts systems and methods
US9853959B1 (en) 2012-05-07 2017-12-26 Consumerinfo.Com, Inc. Storage and maintenance of personal data
US10102536B1 (en) 2013-11-15 2018-10-16 Experian Information Solutions, Inc. Micro-geographic aggregation system
US10242019B1 (en) 2014-12-19 2019-03-26 Experian Information Solutions, Inc. User behavior segmentation using latent topic detection
US10255598B1 (en) 2012-12-06 2019-04-09 Consumerinfo.Com, Inc. Credit card account data extraction
US10262364B2 (en) 2007-12-14 2019-04-16 Consumerinfo.Com, Inc. Card registry systems and methods
US10282748B2 (en) 2013-02-20 2019-05-07 Datalogix Holdings, Inc. System and method for measuring advertising effectiveness
US10339527B1 (en) 2014-10-31 2019-07-02 Experian Information Solutions, Inc. System and architecture for electronic fraud detection
US10380654B2 (en) 2006-08-17 2019-08-13 Experian Information Solutions, Inc. System and method for providing a score for a used vehicle
WO2019160597A1 (en) * 2018-02-14 2019-08-22 American Express Travel Related Services Company, Inc. Authentication challenges based on fraud initiation requests
US10592982B2 (en) 2013-03-14 2020-03-17 Csidentity Corporation System and method for identifying related credit inquiries
US10593004B2 (en) 2011-02-18 2020-03-17 Csidentity Corporation System and methods for identifying compromised personally identifiable information on the internet
US10678894B2 (en) 2016-08-24 2020-06-09 Experian Information Solutions, Inc. Disambiguation and authentication of device users
US10685398B1 (en) 2013-04-23 2020-06-16 Consumerinfo.Com, Inc. Presenting credit score information
US10699319B1 (en) 2016-05-12 2020-06-30 State Farm Mutual Automobile Insurance Company Cross selling recommendation engine
US10699028B1 (en) 2017-09-28 2020-06-30 Csidentity Corporation Identity security architecture systems and methods
US10735183B1 (en) 2017-06-30 2020-08-04 Experian Information Solutions, Inc. Symmetric encryption for private smart contracts among multiple parties in a private peer-to-peer network
US10757154B1 (en) 2015-11-24 2020-08-25 Experian Information Solutions, Inc. Real-time event-based notification system
US10896472B1 (en) 2017-11-14 2021-01-19 Csidentity Corporation Security and identity verification system and architecture
US11030562B1 (en) 2011-10-31 2021-06-08 Consumerinfo.Com, Inc. Pre-data breach monitoring
US11151468B1 (en) 2015-07-02 2021-10-19 Experian Information Solutions, Inc. Behavior analysis using distributed representations of event data
US11157997B2 (en) 2006-03-10 2021-10-26 Experian Information Solutions, Inc. Systems and methods for analyzing data
US11227001B2 (en) 2017-01-31 2022-01-18 Experian Information Solutions, Inc. Massive scale heterogeneous data ingestion and user resolution
US11544783B1 (en) 2016-05-12 2023-01-03 State Farm Mutual Automobile Insurance Company Heuristic credit risk assessment engine
US11620403B2 (en) 2019-01-11 2023-04-04 Experian Information Solutions, Inc. Systems and methods for secure data aggregation and computation

Families Citing this family (32)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8732004B1 (en) 2004-09-22 2014-05-20 Experian Information Solutions, Inc. Automated analysis of data to generate prospect notifications based on trigger events
US11887175B2 (en) 2006-08-31 2024-01-30 Cpl Assets, Llc Automatically determining a personalized set of programs or products including an interactive graphical user interface
US8359278B2 (en) 2006-10-25 2013-01-22 IndentityTruth, Inc. Identity protection
US7657569B1 (en) 2006-11-28 2010-02-02 Lower My Bills, Inc. System and method of removing duplicate leads
US7778885B1 (en) 2006-12-04 2010-08-17 Lower My Bills, Inc. System and method of enhancing leads
US8606626B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. Systems and methods for providing a direct marketing campaign planning environment
US8606666B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. System and method for providing an aggregation tool
US10373198B1 (en) 2008-06-13 2019-08-06 Lmb Mortgage Services, Inc. System and method of generating existing customer leads
US8560161B1 (en) 2008-10-23 2013-10-15 Experian Information Solutions, Inc. System and method for monitoring and predicting vehicle attributes
US20100293114A1 (en) * 2009-05-15 2010-11-18 Mohammed Salahuddin Khan Real estate investment method for purchasing a plurality of distressed properties from a single institution at formula-derived prices
US8412605B2 (en) * 2009-12-01 2013-04-02 Bank Of America Corporation Comprehensive suspicious activity monitoring and alert system
US10453093B1 (en) 2010-04-30 2019-10-22 Lmb Mortgage Services, Inc. System and method of optimizing matching of leads
US8515842B2 (en) * 2010-09-14 2013-08-20 Evolution Finance, Inc. Systems and methods for monitoring and optimizing credit scores
US11301922B2 (en) 2010-11-18 2022-04-12 AUTO I.D., Inc. System and method for providing comprehensive vehicle information
US10977727B1 (en) 2010-11-18 2021-04-13 AUTO I.D., Inc. Web-based system and method for providing comprehensive vehicle build information
US8458069B2 (en) * 2011-03-04 2013-06-04 Brighterion, Inc. Systems and methods for adaptive identification of sources of fraud
US8819793B2 (en) 2011-09-20 2014-08-26 Csidentity Corporation Systems and methods for secure and efficient enrollment into a federation which utilizes a biometric repository
US10424011B2 (en) * 2011-11-02 2019-09-24 Gain Credit Holdings, Inc Systems and methods for shared lending risk
US10055727B2 (en) * 2012-11-05 2018-08-21 Mfoundry, Inc. Cloud-based systems and methods for providing consumer financial data
US9576262B2 (en) 2012-12-05 2017-02-21 Microsoft Technology Licensing, Llc Self learning adaptive modeling system
US8966074B1 (en) * 2013-09-13 2015-02-24 Network Kinetix, LLC System and method for real-time analysis of network traffic
US8886570B1 (en) * 2013-10-29 2014-11-11 Quisk, Inc. Hacker-resistant balance monitoring
US10262362B1 (en) 2014-02-14 2019-04-16 Experian Information Solutions, Inc. Automatic generation of code for attributes
US10580054B2 (en) 2014-12-18 2020-03-03 Experian Information Solutions, Inc. System, method, apparatus and medium for simultaneously generating vehicle history reports and preapproved financing options
US10409867B1 (en) 2016-06-16 2019-09-10 Experian Information Solutions, Inc. Systems and methods of managing a database of alphanumeric values
US10656190B2 (en) * 2017-04-13 2020-05-19 Oracle International Corporation Non-parametric statistical behavioral identification ecosystem for electricity fraud detection
US11210276B1 (en) 2017-07-14 2021-12-28 Experian Information Solutions, Inc. Database system for automated event analysis and detection
US10740404B1 (en) 2018-03-07 2020-08-11 Experian Information Solutions, Inc. Database system for dynamically generating customized models
US10565181B1 (en) 2018-03-07 2020-02-18 Experian Information Solutions, Inc. Database system for dynamically generating customized models
US11157835B1 (en) 2019-01-11 2021-10-26 Experian Information Solutions, Inc. Systems and methods for generating dynamic models based on trigger events
US11410178B2 (en) 2020-04-01 2022-08-09 Mastercard International Incorporated Systems and methods for message tracking using real-time normalized scoring
US11715106B2 (en) 2020-04-01 2023-08-01 Mastercard International Incorporated Systems and methods for real-time institution analysis based on message traffic

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6064990A (en) * 1998-03-31 2000-05-16 International Business Machines Corporation System for electronic notification of account activity
US6072894A (en) * 1997-10-17 2000-06-06 Payne; John H. Biometric face recognition for applicant screening
US6088686A (en) * 1995-12-12 2000-07-11 Citibank, N.A. System and method to performing on-line credit reviews and approvals
US20020077964A1 (en) * 1999-12-15 2002-06-20 Brody Robert M. Systems and methods for providing consumers anonymous pre-approved offers from a consumer-selected group of merchants
US20030078877A1 (en) * 2001-10-18 2003-04-24 Kenneth Beirne Method, system, and storage medium for pre-screening customers for credit card approval at a point of sale
US20040039686A1 (en) * 2002-01-10 2004-02-26 Klebanoff Victor Franklin Method and system for detecting payment account fraud
US7059531B2 (en) * 2001-07-10 2006-06-13 American Express Travel Related Services Company, Inc. Method and system for smellprint recognition biometrics on a fob
US7121471B2 (en) * 2001-07-10 2006-10-17 American Express Travel Related Services Company, Inc. Method and system for DNA recognition biometrics on a fob
US7154375B2 (en) * 2001-07-10 2006-12-26 American Express Travel Related Services Company, Inc. Biometric safeguard method with a fob
US20070106582A1 (en) * 2005-10-04 2007-05-10 Baker James C System and method of detecting fraud
US20070226093A1 (en) * 2002-12-20 2007-09-27 Chan Cynthia M Financial services data model
US7303120B2 (en) * 2001-07-10 2007-12-04 American Express Travel Related Services Company, Inc. System for biometric security using a FOB
US20080294540A1 (en) * 2007-05-25 2008-11-27 Celka Christopher J System and method for automated detection of never-pay data sets

Family Cites Families (431)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3316395A (en) 1963-05-23 1967-04-25 Credit Corp Comp Credit risk computer
US4491725A (en) 1982-09-29 1985-01-01 Pritchard Lawrence E Medical insurance verification and processing system
US4812628A (en) 1985-05-02 1989-03-14 Visa International Service Association Transaction system with off-line risk assessment
US4775935A (en) 1986-09-22 1988-10-04 Westinghouse Electric Corp. Video merchandising system with variable and adoptive product sequence presentation order
US4827508A (en) 1986-10-14 1989-05-02 Personal Library Software, Inc. Database usage metering and protection system and method
US4935870A (en) 1986-12-15 1990-06-19 Keycom Electronic Publishing Apparatus for downloading macro programs and executing a downloaded macro program responding to activation of a single key
US4872113A (en) 1987-08-27 1989-10-03 Jbs Associates, Inc. Credit check scanner data analysis system
US4868570A (en) 1988-01-15 1989-09-19 Arthur D. Little, Inc. Method and system for storing and retrieving compressed data
US4947028A (en) 1988-07-19 1990-08-07 Arbor International, Inc. Automated order and payment system
US5247575A (en) 1988-08-16 1993-09-21 Sprague Peter J Information distribution system
US5201010A (en) 1989-05-01 1993-04-06 Credit Verification Corporation Method and system for building a database and performing marketing based upon prior shopping history
US5687322A (en) 1989-05-01 1997-11-11 Credit Verification Corporation Method and system for selective incentive point-of-sale marketing in response to customer shopping histories
US5056019A (en) 1989-08-29 1991-10-08 Citicorp Pos Information Servies, Inc. Automated purchase reward accounting system and method
US5202986A (en) 1989-09-28 1993-04-13 Bull Hn Information Systems Inc. Prefix search tree partial key branching
US5276868A (en) 1990-05-23 1994-01-04 Digital Equipment Corp. Method and apparatus for pointer compression in structured databases
US5555409A (en) 1990-12-04 1996-09-10 Applied Technical Sysytem, Inc. Data management systems and methods including creation of composite views of data
US5325509A (en) 1991-03-05 1994-06-28 Zitel Corporation Method of operating a cache memory including determining desirability of cache ahead or cache behind based on a number of available I/O operations
US5301105A (en) 1991-04-08 1994-04-05 Desmond D. Cummings All care health management system
US5640577A (en) 1991-12-30 1997-06-17 Davox Corporation Data processing system with automated at least partial forms completion
US6985883B1 (en) 1992-02-03 2006-01-10 Ebs Dealing Resources, Inc. Credit management for electronic brokerage system
GB9204450D0 (en) 1992-03-02 1992-04-15 Ibm Concurrent access to indexed data files
US5239462A (en) 1992-02-25 1993-08-24 Creative Solutions Groups, Inc. Method and apparatus for automatically determining the approval status of a potential borrower
US5583760A (en) 1992-05-22 1996-12-10 Beneficial Franchise Company, Inc. System for establishing and administering funded and post-funded charge accounts
US5737732A (en) 1992-07-06 1998-04-07 1St Desk Systems, Inc. Enhanced metatree data structure for storage indexing and retrieval of information
US5819226A (en) 1992-09-08 1998-10-06 Hnc Software Inc. Fraud detection using predictive modeling
US5341429A (en) 1992-12-04 1994-08-23 Testdrive Corporation Transformation of ephemeral material
US5521813A (en) 1993-01-15 1996-05-28 Strategic Weather Services System and method for the advanced prediction of weather impact on managerial planning applications
AU674189B2 (en) 1993-02-23 1996-12-12 Moore North America, Inc. A method and system for gathering and analyzing customer and purchasing information
CN1135822C (en) 1993-03-31 2004-01-21 碧蓝方案有限公司 Data correction system for communications network
US5640551A (en) 1993-04-14 1997-06-17 Apple Computer, Inc. Efficient high speed trie search process
US5560007A (en) 1993-06-30 1996-09-24 Borland International, Inc. B-tree key-range bit map index optimization of database queries
AU687880B2 (en) 1993-08-27 1998-03-05 Decisioning.Com, Inc. Closed loop financial transaction method and apparatus
US5583763A (en) 1993-09-09 1996-12-10 Mni Interactive Method and apparatus for recommending selections based on preferences in a multi-user system
US5930776A (en) 1993-11-01 1999-07-27 The Golden 1 Credit Union Lender direct credit evaluation and loan processing system
US5611052A (en) 1993-11-01 1997-03-11 The Golden 1 Credit Union Lender direct credit evaluation and loan processing system
US5644778A (en) 1993-11-02 1997-07-01 Athena Of North America, Inc. Medical transaction system
US5881131A (en) 1993-11-16 1999-03-09 Bell Atlantic Network Services, Inc. Analysis and validation system for provisioning network related facilities
US5550734A (en) 1993-12-23 1996-08-27 The Pharmacy Fund, Inc. Computerized healthcare accounts receivable purchasing collections securitization and management system
US6108641A (en) 1994-01-03 2000-08-22 Merrill Lynch, Pierce, Fenner & Smith Integrated nested account financial system with medical savings subaccount
US5471382A (en) 1994-01-10 1995-11-28 Informed Access Systems, Inc. Medical network management system and process
US5692107A (en) 1994-03-15 1997-11-25 Lockheed Missiles & Space Company, Inc. Method for generating predictive models in a computer system
US5584024A (en) 1994-03-24 1996-12-10 Software Ag Interactive database query system and method for prohibiting the selection of semantically incorrect query parameters
US6513018B1 (en) 1994-05-05 2003-01-28 Fair, Isaac And Company, Inc. Method and apparatus for scoring the likelihood of a desired performance result
JP2683870B2 (en) 1994-05-23 1997-12-03 日本アイ・ビー・エム株式会社 Character string search system and method
US5528701A (en) 1994-09-02 1996-06-18 Panasonic Technologies, Inc. Trie based method for indexing handwritten databases
US5832447A (en) 1994-05-24 1998-11-03 Envoy Corporation Automated system and method for providing real-time verification of health insurance eligibility
CN1152365A (en) 1994-06-06 1997-06-18 诺基亚电信公司 Method for storing and retrieving data and memory arrangement
US5590038A (en) 1994-06-20 1996-12-31 Pitroda; Satyan G. Universal electronic transaction card including receipt storage and system and methods of conducting electronic transactions
US5557514A (en) 1994-06-23 1996-09-17 Medicode, Inc. Method and system for generating statistically-based medical provider utilization profiles
US5577239A (en) 1994-08-10 1996-11-19 Moore; Jeffrey Chemical structure storage, searching and retrieval system
US5768423A (en) 1994-09-02 1998-06-16 Panasonic Technologies Inc. Trie structure based method and apparatus for indexing and searching handwritten databases with dynamic search sequencing
AU3734395A (en) 1994-10-03 1996-04-26 Helfgott & Karas, P.C. A database accessing system
US6073104A (en) 1994-11-09 2000-06-06 Field; Richard G. System for invoice record management and asset-backed commercial paper program management
US6460036B1 (en) 1994-11-29 2002-10-01 Pinpoint Incorporated System and method for providing customized electronic newspapers and target advertisements
US5758257A (en) 1994-11-29 1998-05-26 Herz; Frederick System and method for scheduling broadcast of and access to video programs and other data using customer profiles
US5835915A (en) 1995-01-24 1998-11-10 Tandem Computer Remote duplicate database facility with improved throughput and fault tolerance
US5696907A (en) 1995-02-27 1997-12-09 General Electric Company System and method for performing risk and credit analysis of financial service applications
JPH11511273A (en) 1995-04-13 1999-09-28 エルダット・コミュニケーション・リミテッド Promotional data processor system and interactive variable display particularly useful therefor
US6601048B1 (en) 1997-09-12 2003-07-29 Mci Communications Corporation System and method for detecting and managing fraud
US5926800A (en) 1995-04-24 1999-07-20 Minerva, L.P. System and method for providing a line of credit secured by an assignment of a life insurance policy
US5699527A (en) 1995-05-01 1997-12-16 Davidson; David Edward Method and system for processing loan
US5884289A (en) 1995-06-16 1999-03-16 Card Alert Services, Inc. Debit card fraud detection and control system
US5659731A (en) 1995-06-19 1997-08-19 Dun & Bradstreet, Inc. Method for rating a match for a given entity found in a list of entities
US7181427B1 (en) 1995-09-12 2007-02-20 Jp Morgan Chase Bank, N.A. Automated credit application system
US5878403A (en) 1995-09-12 1999-03-02 Cmsi Computer implemented automated credit application analysis and decision routing system
US6321205B1 (en) 1995-10-03 2001-11-20 Value Miner, Inc. Method of and system for modeling and analyzing business improvement programs
US6393406B1 (en) 1995-10-03 2002-05-21 Value Mines, Inc. Method of and system for valving elements of a business enterprise
US5797136A (en) 1995-10-05 1998-08-18 International Business Machines Corporation Optional quantifiers in relational and object-oriented views of database systems
US5774692A (en) 1995-10-05 1998-06-30 International Business Machines Corporation Outer quantifiers in object-oriented queries and views of database systems
EP0770967A3 (en) 1995-10-26 1998-12-30 Koninklijke Philips Electronics N.V. Decision support system for the management of an agile supply chain
JP3152871B2 (en) 1995-11-10 2001-04-03 富士通株式会社 Dictionary search apparatus and method for performing a search using a lattice as a key
US5907828A (en) 1995-12-26 1999-05-25 Meyer; Bennett S. System and method for implementing and administering lender-owned credit life insurance policies
US5822410A (en) 1996-01-11 1998-10-13 Gte Telecom Services Inc Churn amelioration system and method therefor
US6044352A (en) 1996-01-11 2000-03-28 Deavers; Karl Method and system for processing and recording the transactions in a medical savings fund account
US5745654A (en) 1996-02-13 1998-04-28 Hnc Software, Inc. Fast explanations of scored observations
US5933809A (en) 1996-02-29 1999-08-03 Medcom Solutions, Inc. Computer software for processing medical billing record information
US6067522A (en) 1996-03-01 2000-05-23 Warady; Arthur D. Health and welfare benefit enrollment and billing system and method
US6038551A (en) 1996-03-11 2000-03-14 Microsoft Corporation System and method for configuring and managing resources on a multi-purpose integrated circuit card using a personal computer
US5884287A (en) 1996-04-12 1999-03-16 Lfg, Inc. System and method for generating and displaying risk and return in an investment portfolio
US5848396A (en) 1996-04-26 1998-12-08 Freedom Of Information, Inc. Method and apparatus for determining behavioral profile of a computer user
US5930759A (en) 1996-04-30 1999-07-27 Symbol Technologies, Inc. Method and system for processing health care electronic data transactions
US5995922A (en) 1996-05-02 1999-11-30 Microsoft Corporation Identifying information related to an input word in an electronic dictionary
US5987434A (en) 1996-06-10 1999-11-16 Libman; Richard Marc Apparatus and method for transacting marketing and sales of financial products
US6094643A (en) 1996-06-14 2000-07-25 Card Alert Services, Inc. System for detecting counterfeit financial card fraud
US5844218A (en) 1996-07-16 1998-12-01 Transaction Technology, Inc. Method and system for using an application programmable smart card for financial transactions in multiple countries
US5956693A (en) 1996-07-19 1999-09-21 Geerlings; Huib Computer system for merchant communication to customers
US5861827A (en) 1996-07-24 1999-01-19 Unisys Corporation Data compression and decompression system with immediate dictionary updating interleaved with string search
US6129273A (en) 1996-08-21 2000-10-10 Shah; Dinesh V. Method and apparatus for an automated, computer approved, check cashing system
US5966699A (en) 1996-10-11 1999-10-12 Zandi; Richard System and method for conducting loan auction over computer network
US5822751A (en) 1996-12-16 1998-10-13 Microsoft Corporation Efficient multidimensional data aggregation operator implementation
WO1998031114A1 (en) 1997-01-06 1998-07-16 Bellsouth Corporation Method and system for tracking network use
US8640160B2 (en) 1997-01-06 2014-01-28 At&T Intellectual Property I, L.P. Method and system for providing targeted advertisements
US6983478B1 (en) 2000-02-01 2006-01-03 Bellsouth Intellectual Property Corporation Method and system for tracking network use
US6262337B1 (en) 1997-02-18 2001-07-17 Ludwig Institute For Cancer Research Transgenic animal with recombinant vascular endothelial growth factor B (VEGF-B DNA) and uses thereof
JP4092441B2 (en) 1997-02-24 2008-05-28 日産自動車株式会社 Exhaust gas purification catalyst
US5903888A (en) 1997-02-28 1999-05-11 Oracle Corporation Method and apparatus for using incompatible types of indexes to process a single query
US5970478A (en) 1997-03-12 1999-10-19 Walker Asset Management Limited Partnership Method, apparatus, and program for customizing credit accounts
US6014632A (en) 1997-04-15 2000-01-11 Financial Growth Resources, Inc. Apparatus and method for determining insurance benefit amounts based on groupings of long-term care patients with common characteristics
US6182060B1 (en) 1997-04-15 2001-01-30 Robert Hedgcock Method and apparatus for storing, retrieving, and processing multi-dimensional customer-oriented data sets
GB2321751B (en) 1997-04-22 1999-02-10 Searchspace Limited A monitoring system and method
US5963932A (en) 1997-04-29 1999-10-05 Oracle Corporation Method and apparatus for transforming queries
US6018723A (en) 1997-05-27 2000-01-25 Visa International Service Association Method and apparatus for pattern generation
US6119103A (en) 1997-05-27 2000-09-12 Visa International Service Association Financial risk prediction systems and methods therefor
US6523022B1 (en) 1997-06-09 2003-02-18 Allen Hobbs Method and apparatus for selectively augmenting retrieved information from a network resource
US6144948A (en) 1997-06-23 2000-11-07 Walker Digital, Llc Instant credit card marketing system for reservations for future services
US5822750A (en) 1997-06-30 1998-10-13 International Business Machines Corporation Optimization of correlated SQL queries in a relational database management system
US5905985A (en) 1997-06-30 1999-05-18 International Business Machines Corporation Relational database modifications based on multi-dimensional database modifications
US6029154A (en) 1997-07-28 2000-02-22 Internet Commerce Services Corporation Method and system for detecting fraud in a credit card transaction over the internet
US6523041B1 (en) 1997-07-29 2003-02-18 Acxiom Corporation Data linking system and method using tokens
US6073140A (en) 1997-07-29 2000-06-06 Acxiom Corporation Method and system for the creation, enhancement and update of remote data using persistent keys
US6766327B2 (en) 1997-07-29 2004-07-20 Acxiom Corporation Data linking system and method using encoded links
US5940812A (en) 1997-08-19 1999-08-17 Loanmarket Resources, L.L.C. Apparatus and method for automatically matching a best available loan to a potential borrower via global telecommunications network
US5995947A (en) 1997-09-12 1999-11-30 Imx Mortgage Exchange Interactive mortgage and loan information and real-time trading system
US6151601A (en) 1997-11-12 2000-11-21 Ncr Corporation Computer architecture and method for collecting, analyzing and/or transforming internet and/or electronic commerce data for storage into a data storage area
US6128624A (en) 1997-11-12 2000-10-03 Ncr Corporation Collection and integration of internet and electronic commerce data in a database during web browsing
US6044351A (en) 1997-12-18 2000-03-28 Jones; Annie M. W. Minimum income probability distribution predictor for health care facilities
US6098052A (en) 1998-02-10 2000-08-01 First Usa Bank, N.A. Credit card collection strategy model
US6208973B1 (en) 1998-02-27 2001-03-27 Onehealthbank.Com Point of service third party financial management vehicle for the healthcare industry
US6263337B1 (en) 1998-03-17 2001-07-17 Microsoft Corporation Scalable system for expectation maximization clustering of large databases
US7580856B1 (en) 1998-04-27 2009-08-25 Robert K. Pliha Systems and methods for distributing targeted incentives to financial institution customers
US6044357A (en) 1998-05-05 2000-03-28 International Business Machines Corporation Modeling a multifunctional firm operating in a competitive market with multiple brands
US6385594B1 (en) 1998-05-08 2002-05-07 Lendingtree, Inc. Method and computer network for co-ordinating a loan over the internet
US6185543B1 (en) 1998-05-15 2001-02-06 Marketswitch Corp. Method and apparatus for determining loan prepayment scores
PT1080415T (en) 1998-05-21 2017-05-02 Equifax Inc System and method for authentication of network users
US6311169B2 (en) 1998-06-11 2001-10-30 Consumer Credit Associates, Inc. On-line consumer credit data reporting system
EP0977128A1 (en) 1998-07-28 2000-02-02 Matsushita Electric Industrial Co., Ltd. Method and system for storage and retrieval of multimedia objects by decomposing a tree-structure into a directed graph
US6163770A (en) 1998-08-25 2000-12-19 Financial Growth Resources, Inc. Computer apparatus and method for generating documentation using a computed value for a claims cost affected by at least one concurrent, different insurance policy for the same insured
US6223171B1 (en) 1998-08-25 2001-04-24 Microsoft Corporation What-if index analysis utility for database systems
GB2343763B (en) 1998-09-04 2003-05-21 Shell Services Internat Ltd Data processing system
US6339769B1 (en) 1998-09-14 2002-01-15 International Business Machines Corporation Query optimization by transparently altering properties of relational tables using materialized views
US6171112B1 (en) 1998-09-18 2001-01-09 Wyngate, Inc. Methods and apparatus for authenticating informed consent
US6269325B1 (en) 1998-10-21 2001-07-31 Unica Technologies, Inc. Visual presentation technique for data mining software
US6405181B2 (en) 1998-11-03 2002-06-11 Nextcard, Inc. Method and apparatus for real time on line credit approval
US6567791B2 (en) 1998-11-03 2003-05-20 Nextcard, Inc. Method and apparatus for a verifiable on line rejection of an application for credit
US6718313B1 (en) 1998-11-03 2004-04-06 Next Card, Inc. Integrating live chat into an online credit card application
AU1612500A (en) 1998-11-09 2000-05-29 E-Fin, Llc Computer-driven information management system for selectively matching credit applicants with money lenders through a global communications network
US6263334B1 (en) 1998-11-11 2001-07-17 Microsoft Corporation Density-based indexing method for efficient execution of high dimensional nearest-neighbor queries on large databases
US6254000B1 (en) 1998-11-13 2001-07-03 First Data Corporation System and method for providing a card transaction authorization fraud warning
CA2352302A1 (en) 1998-11-30 2000-06-08 Index Systems, Inc. Smart agent based on habit, statistical inference and psycho-demographic profiling
US7260823B2 (en) 2001-01-11 2007-08-21 Prime Research Alliance E., Inc. Profiling and identification of television viewers
US6542894B1 (en) 1998-12-09 2003-04-01 Unica Technologies, Inc. Execution of multiple models using data segmentation
US6496819B1 (en) 1998-12-28 2002-12-17 Oracle Corporation Rewriting a query in terms of a summary based on functional dependencies and join backs, and based on join derivability
US6236977B1 (en) 1999-01-04 2001-05-22 Realty One, Inc. Computer implemented marketing system
US7571139B1 (en) 1999-02-19 2009-08-04 Giordano Joseph A System and method for processing financial transactions
US6334110B1 (en) 1999-03-10 2001-12-25 Ncr Corporation System and method for analyzing customer transactions and interactions
US6985887B1 (en) 1999-03-19 2006-01-10 Suncrest Llc Apparatus and method for authenticated multi-user personal information database
US6631496B1 (en) 1999-03-22 2003-10-07 Nec Corporation System for personalizing, organizing and managing web information
US6430539B1 (en) 1999-05-06 2002-08-06 Hnc Software Predictive modeling of consumer financial behavior
US6721713B1 (en) 1999-05-27 2004-04-13 Andersen Consulting Llp Business alliance identification in a web architecture framework
US6748369B2 (en) 1999-06-21 2004-06-08 General Electric Company Method and system for automated property valuation
US6804346B1 (en) 1999-07-13 2004-10-12 Interactive Intelligence, Inc. Staged predictive dialing system
US7395239B1 (en) 1999-07-19 2008-07-01 American Business Financial System and method for automatically processing loan applications
US8666757B2 (en) 1999-07-28 2014-03-04 Fair Isaac Corporation Detection of upcoding and code gaming fraud and abuse in prospective payment healthcare systems
US7171371B2 (en) 1999-09-03 2007-01-30 Smg Trust Method and system for providing pre and post operative support and care
US7185016B1 (en) 2000-09-01 2007-02-27 Cognos Incorporated Methods and transformations for transforming metadata model
US7260724B1 (en) 1999-09-20 2007-08-21 Security First Corporation Context sensitive dynamic authentication in a cryptographic system
US20020138297A1 (en) 2001-03-21 2002-09-26 Lee Eugene M. Apparatus for and method of analyzing intellectual property information
US6792458B1 (en) 1999-10-04 2004-09-14 Urchin Software Corporation System and method for monitoring and analyzing internet traffic
US6374229B1 (en) 1999-10-20 2002-04-16 Billingnetwork.Com, Inc. Integrated internet facilitated billing, data processing and communication system
US20030065563A1 (en) 1999-12-01 2003-04-03 Efunds Corporation Method and apparatus for atm-based cross-selling of products and services
US6959281B1 (en) 1999-12-06 2005-10-25 Freeling Kenneth A Digital computer system and methods for conducting a poll to produce a demographic profile corresponding to an accumulation of response data from encrypted identities
KR100554695B1 (en) 1999-12-10 2006-02-22 엔티티 도꼬모 인코퍼레이티드 Mobile communication terminal
US6418436B1 (en) 1999-12-20 2002-07-09 First Data Corporation Scoring methodology for purchasing card fraud detection
US6456983B1 (en) 1999-12-23 2002-09-24 General Electric Company Method for managing disposition of delinquent accounts
US6901406B2 (en) 1999-12-29 2005-05-31 General Electric Capital Corporation Methods and systems for accessing multi-dimensional customer data
US7003491B2 (en) 1999-12-29 2006-02-21 General Electric Capital Corporation Methods and systems for a collections model for loans
US7082435B1 (en) 2000-01-03 2006-07-25 Oracle International Corporation Method and mechanism for implementing and accessing virtual database table structures
US7191150B1 (en) 2000-02-01 2007-03-13 Fair Isaac Corporation Enhancing delinquent debt collection using statistical models of debt historical information and account events
JP2001216403A (en) 2000-02-04 2001-08-10 Hiroshi Shirakawa Auction system and auction method
US20020069122A1 (en) 2000-02-22 2002-06-06 Insun Yun Method and system for maximizing credit card purchasing power and minimizing interest costs over the internet
US20010034618A1 (en) 2000-02-24 2001-10-25 Kessler David G. Healthcare payment and compliance system
WO2001065453A1 (en) 2000-02-29 2001-09-07 Expanse Networks, Inc. Privacy-protected targeting system
US6873979B2 (en) 2000-02-29 2005-03-29 Marketswitch Corporation Method of building predictive models on transactional data
US20020010594A1 (en) 2000-03-20 2002-01-24 Levine Michael R. Method of payment for a healthcare service
WO2001073652A1 (en) 2000-03-24 2001-10-04 Access Business Group International Llc System and method for detecting fraudulent transactions
US20020188478A1 (en) 2000-03-24 2002-12-12 Joe Breeland Health-care systems and methods
US20020023051A1 (en) 2000-03-31 2002-02-21 Kunzle Adrian E. System and method for recommending financial products to a customer based on customer needs and preferences
US7263506B2 (en) 2000-04-06 2007-08-28 Fair Isaac Corporation Identification and management of fraudulent credit/debit card purchases at merchant ecommerce sites
US6366903B1 (en) 2000-04-20 2002-04-02 Microsoft Corporation Index and materialized view selection for a given workload
AU2001257280C1 (en) 2000-04-24 2009-01-15 Visa International Service Association Online payer authentication service
US7426474B2 (en) 2000-04-25 2008-09-16 The Rand Corporation Health cost calculator/flexible spending account calculator
AU6108901A (en) 2000-04-27 2001-11-07 Webfeat Inc Method and system for retrieving search results from multiple disparate databases
JP2001312586A (en) 2000-04-28 2001-11-09 Tokio Marine & Fire Insurance Co Ltd Support system for providing of ranking-related service and support method therefor
US6847942B1 (en) 2000-05-02 2005-01-25 General Electric Canada Equipment Finance G.P. Method and apparatus for managing credit inquiries within account receivables
AU2001259815A1 (en) 2000-05-04 2001-11-12 Mighty Net, Incorporated Card management system and method therefore
GB2384890A (en) 2000-05-09 2003-08-06 Fair Isaac And Company Approach for re-using business rules
US7401131B2 (en) 2000-05-22 2008-07-15 Verizon Business Global Llc Method and system for implementing improved containers in a global ecosystem of interrelated services
US7003517B1 (en) 2000-05-24 2006-02-21 Inetprofit, Inc. Web-based system and method for archiving and searching participant-based internet text sources for customer lead data
US7295988B1 (en) 2000-05-25 2007-11-13 William Reeves Computer system for optical scanning, storage, organization, authentication and electronic transmitting and receiving of medical records and patient information, and other sensitive legal documents
US6901384B2 (en) 2000-06-03 2005-05-31 American Home Credit, Inc. System and method for automated process of deal structuring
US20060155639A1 (en) 2000-06-03 2006-07-13 Joan Lynch System and method for automated process of deal structuring
US7542948B2 (en) 2000-06-16 2009-06-02 Honda Giken Kogyo Kabushiki Kaisha Genetic design method and apparatus
US7024386B1 (en) 2000-06-23 2006-04-04 Ebs Group Limited Credit handling in an anonymous trading system
US6983379B1 (en) 2000-06-30 2006-01-03 Hitwise Pty. Ltd. Method and system for monitoring online behavior at a remote site and creating online behavior profiles
US7610216B1 (en) 2000-07-13 2009-10-27 Ebay Inc. Method and system for detecting fraud
US7617116B2 (en) 2000-08-04 2009-11-10 Athenahealth, Inc. Practice management and billing automation system
US6574623B1 (en) 2000-08-15 2003-06-03 International Business Machines Corporation Query transformation and simplification for group by queries with rollup/grouping sets in relational database management systems
US20050154664A1 (en) 2000-08-22 2005-07-14 Guy Keith A. Credit and financial information and management system
DE10046110B8 (en) 2000-09-18 2006-07-06 Siemens Ag Medical diagnostic device with patient recognition
GB2384087A (en) 2000-09-29 2003-07-16 Fair Isaac And Company Inc Score based decisioning
US6631374B1 (en) 2000-09-29 2003-10-07 Oracle Corp. System and method for providing fine-grained temporal database access
US6597775B2 (en) 2000-09-29 2003-07-22 Fair Isaac Corporation Self-learning real-time prioritization of telecommunication fraud control actions
US6850606B2 (en) 2001-09-25 2005-02-01 Fair Isaac Corporation Self-learning real-time prioritization of telecommunication fraud control actions
US7043531B1 (en) 2000-10-04 2006-05-09 Inetprofit, Inc. Web-based customer lead generator system with pre-emptive profiling
US6904408B1 (en) 2000-10-19 2005-06-07 Mccarthy John Bionet method, system and personalized web content manager responsive to browser viewers' psychological preferences, behavioral responses and physiological stress indicators
US7383215B1 (en) 2000-10-26 2008-06-03 Fair Isaac Corporation Data center for account management
US20020107849A1 (en) 2000-11-01 2002-08-08 Hickey Matthew W. Scholarship search method and system
IL146597A0 (en) 2001-11-20 2002-08-14 Gordon Goren Method and system for creating meaningful summaries from interrelated sets of information
AU2002235142A1 (en) 2000-11-27 2002-06-03 Nextworth, Inc. Anonymous transaction system
US20020103680A1 (en) 2000-11-30 2002-08-01 Newman Les A. Systems, methods and computer program products for managing employee benefits
US20030009418A1 (en) 2000-12-08 2003-01-09 Green Gerald M. Systems and methods for electronically verifying and processing information
WO2002059770A1 (en) 2000-12-18 2002-08-01 Cora Alisuag Computer oriented record administration system
JP2002197186A (en) 2000-12-27 2002-07-12 Fujitsu Ltd Personal information management device
US20020128960A1 (en) 2000-12-29 2002-09-12 Lambiotte Kenneth G. Systems and methods for managing accounts
US20020087460A1 (en) 2001-01-04 2002-07-04 Hornung Katharine A. Method for identity theft protection
US7072842B2 (en) 2001-01-08 2006-07-04 P5, Inc. Payment of health care insurance claims using short-term loans
US7529698B2 (en) 2001-01-16 2009-05-05 Raymond Anthony Joao Apparatus and method for providing transaction history information, account history information, and/or charge-back information
US7472088B2 (en) 2001-01-19 2008-12-30 Jpmorgan Chase Bank N.A. System and method for offering a financial product
US8078524B2 (en) 2001-02-22 2011-12-13 Fair Isaac Corporation Method and apparatus for explaining credit scores
US7246068B2 (en) 2001-03-23 2007-07-17 Thomas Jr James C Computerized system for combining insurance company and credit card transactions
US7392221B2 (en) 2001-04-06 2008-06-24 General Electric Capital Corporation Methods and systems for identifying early terminating loan customers
US7216102B2 (en) 2001-04-06 2007-05-08 General Electric Capital Corporation Methods and systems for auctioning of pre-selected customer lists
US20030009426A1 (en) 2001-04-19 2003-01-09 Marcelo Ruiz-Sanchez Methods and apparatus for protecting against credit card fraud, check fraud, and identity theft
US20020184054A1 (en) 2001-04-26 2002-12-05 Robert Cox Two-way practice management data integration
US20020161711A1 (en) 2001-04-30 2002-10-31 Sartor Karalyn K. Fraud detection method
US7542993B2 (en) 2001-05-10 2009-06-02 Equifax, Inc. Systems and methods for notifying a consumer of changes made to a credit report
US7028052B2 (en) 2001-05-10 2006-04-11 Equifax, Inc. Systems and methods for notifying a consumer of changes made to a credit report
US7325193B2 (en) 2001-06-01 2008-01-29 International Business Machines Corporation Automated management of internet and/or web site content
WO2002099598A2 (en) 2001-06-07 2002-12-12 First Usa Bank, N.A. System and method for rapid updating of credit information
US7174302B2 (en) 2001-06-11 2007-02-06 Evolution Benefits, Inc. System and method for processing flexible spending account transactions
JP2003016261A (en) 2001-07-05 2003-01-17 Asahi Bank Ltd Total financing managing system, credit scoring deciding system and credit guarantee managing system
US7801828B2 (en) 2001-07-06 2010-09-21 Candella George J Method and system for detecting identity theft in non-personal and personal transactions
US20030229507A1 (en) 2001-07-13 2003-12-11 Damir Perge System and method for matching donors and charities
US7747453B2 (en) 2001-08-06 2010-06-29 Ulrich Medical Concepts, Inc. System and method for managing patient encounters
US20030037054A1 (en) 2001-08-09 2003-02-20 International Business Machines Corporation Method for controlling access to medical information
US20030046112A1 (en) 2001-08-09 2003-03-06 International Business Machines Corporation Method of providing medical financial information
US8306829B2 (en) 2001-08-15 2012-11-06 Chamberlin Edmonds & Associates Method for determining eligibility for an assistance program
US7366694B2 (en) 2001-08-16 2008-04-29 Mortgage Grader, Inc. Credit/financing process
US20030050795A1 (en) 2001-09-12 2003-03-13 Baldwin Byron S. Health care debt financing system and method
US7333937B2 (en) 2001-09-13 2008-02-19 Ads Responsecorp, Inc. Health care financing method
US20030061163A1 (en) 2001-09-27 2003-03-27 Durfield Richard C. Method and apparatus for verification/authorization by credit or debit card owner of use of card concurrently with merchant transaction
US20030208412A1 (en) 2001-09-28 2003-11-06 Hillestad Willam E. Method and system facilitating transactions between consumers and service providers
US7536346B2 (en) 2001-10-29 2009-05-19 Equifax, Inc. System and method for facilitating reciprocative small business financial information exchanges
US7370044B2 (en) 2001-11-19 2008-05-06 Equifax, Inc. System and method for managing and updating information relating to economic entities
US20030130933A1 (en) 2001-12-31 2003-07-10 Xiao-Ming Huang Method and apparatus for determining a customer's likelihood of paying off a financial account
US20040205157A1 (en) 2002-01-31 2004-10-14 Eric Bibelnieks System, method, and computer program product for realtime profiling of web site visitors
US7813937B1 (en) 2002-02-15 2010-10-12 Fair Isaac Corporation Consistency modeling of healthcare claims to detect fraud and abuse
US20030171942A1 (en) 2002-03-06 2003-09-11 I-Centrix Llc Contact relationship management system and method
US20030182214A1 (en) 2002-03-20 2003-09-25 Taylor Michael K. Fraud detection and security system for financial institutions
US20040122764A1 (en) 2002-03-27 2004-06-24 Bernie Bilski Capped bill systems, methods and products
US20060059110A1 (en) 2002-04-03 2006-03-16 Ajay Madhok System and method for detecting card fraud
CA2381689A1 (en) 2002-04-12 2003-10-12 Algorithmics International Corp. System, method and framework for generating scenarios
US20030212618A1 (en) 2002-05-07 2003-11-13 General Electric Capital Corporation Systems and methods associated with targeted leading indicators
US7383227B2 (en) 2002-05-14 2008-06-03 Early Warning Services, Llc Database for check risk decisions populated with check activity data from banks of first deposit
US7509117B2 (en) 2002-05-31 2009-03-24 Nokia Corporation Apparatus, and associated method, for notifying a user in a radio communication system of a commercially-related transaction
US8224723B2 (en) 2002-05-31 2012-07-17 Jpmorgan Chase Bank, N.A. Account opening system, method and computer program product
US20040073456A1 (en) 2002-06-07 2004-04-15 Gottlieb Joshua L. Multiple eligibility medical claims recovery system
US20030233259A1 (en) 2002-06-14 2003-12-18 Anthony Mistretta Medicare enrollment processing
US20040030667A1 (en) 2002-08-02 2004-02-12 Capital One Financial Corporation Automated systems and methods for generating statistical models
JP2004078435A (en) 2002-08-13 2004-03-11 Ibm Japan Ltd Risk management device, risk management system, risk management method, future expected profit computing method, and program
US20040044615A1 (en) 2002-09-03 2004-03-04 Xue Xun Sean Multiple severity and urgency risk events credit scoring system
US20040044617A1 (en) 2002-09-03 2004-03-04 Duojia Lu Methods and systems for enterprise risk auditing and management
US20040049473A1 (en) 2002-09-05 2004-03-11 David John Gower Information analytics systems and methods
US7356506B2 (en) 2002-09-18 2008-04-08 General Electric Capital Corporation Methods and apparatus for evaluating a credit application
US20040064402A1 (en) 2002-09-27 2004-04-01 Wells Fargo Home Mortgage, Inc. Method of refinancing a mortgage loan and a closing package for same
US20040122735A1 (en) 2002-10-09 2004-06-24 Bang Technologies, Llc System, method and apparatus for an integrated marketing vehicle platform
US7240059B2 (en) 2002-11-14 2007-07-03 Seisint, Inc. System and method for configuring a parallel-processing database system
AU2003295619A1 (en) 2002-11-15 2004-06-15 Fair Isaac Corporation Fraud and abuse detection and entity profiling in hierarchical coded payment systems
US7720761B2 (en) 2002-11-18 2010-05-18 Jpmorgan Chase Bank, N. A. Method and system for enhancing credit line management, price management and other discretionary levels setting for financial accounts
US6826535B2 (en) 2003-04-08 2004-11-30 Richard Glee Wood Method for reducing fraud in healthcare programs using a smart card
US20040111292A1 (en) 2002-12-06 2004-06-10 Hutchins Patton A. Healthcare credit evaluation method
US7305359B2 (en) 2002-12-12 2007-12-04 Siemens Medical Solutions Health Services Corporation Healthcare cash management accounting system
US20070072190A1 (en) 2002-12-16 2007-03-29 Abhinav Aggarwal System and method for universal identification of biological humans
AU2003295807A1 (en) 2002-12-30 2004-07-29 Fannie Mae System and method for verifying loan data at delivery
US20050102226A1 (en) 2002-12-30 2005-05-12 Dror Oppenheimer System and method of accounting for mortgage related transactions
US20040215556A1 (en) 2003-01-10 2004-10-28 Merkley John Eugene Marketing of an agricultural input via electronic communications
CA2418163A1 (en) 2003-01-31 2004-07-31 Ibm Canada Limited - Ibm Canada Limitee Method of query transformation using window aggregation
FI117181B (en) 2003-01-31 2006-07-14 Qitec Technology Group Oy A method and system for identifying a user's identity
US7403942B1 (en) 2003-02-04 2008-07-22 Seisint, Inc. Method and system for processing data records
US7200602B2 (en) 2003-02-07 2007-04-03 International Business Machines Corporation Data set comparison and net change processing
US20040230448A1 (en) 2003-02-14 2004-11-18 William Schaich System for managing and reporting financial account activity
US20040267660A1 (en) 2003-02-21 2004-12-30 Automated Financial Systems, Inc. Risk management system
US20040177030A1 (en) 2003-03-03 2004-09-09 Dan Shoham Psychometric Creditworthiness Scoring for Business Loans
EP1599845A1 (en) 2003-03-04 2005-11-30 Gamelogic Inc. User authentication system and method
US20040177046A1 (en) 2003-03-05 2004-09-09 Ogram Mark Ellery Credit card protection system
US7451113B1 (en) 2003-03-21 2008-11-11 Mighty Net, Inc. Card management system and method
US8069076B2 (en) 2003-03-25 2011-11-29 Cox Communications, Inc. Generating audience analytics
US20040193535A1 (en) 2003-03-26 2004-09-30 Reza Barazesh Global failure risk score
US20050137912A1 (en) 2003-03-31 2005-06-23 Rao R. B. Systems and methods for automated classification of health insurance claims to predict claim outcome
US20040199462A1 (en) 2003-04-02 2004-10-07 Ed Starrs Fraud control method and system for network transactions
US7246740B2 (en) 2003-04-03 2007-07-24 First Data Corporation Suspicious persons database
US20050209880A1 (en) 2003-04-24 2005-09-22 Drelicharz Peggy A Integrated healthcare information system
CA2427209A1 (en) 2003-04-30 2004-10-30 Ibm Canada Limited - Ibm Canada Limitee Optimization of queries on views defined by conditional expressions having mutually exclusive conditions
US7458508B1 (en) 2003-05-12 2008-12-02 Id Analytics, Inc. System and method for identity-based fraud detection
US7686214B1 (en) 2003-05-12 2010-03-30 Id Analytics, Inc. System and method for identity-based fraud detection using a plurality of historical identity records
US7562814B1 (en) 2003-05-12 2009-07-21 Id Analytics, Inc. System and method for identity-based fraud detection through graph anomaly detection
US20040243518A1 (en) 2003-05-13 2004-12-02 Clifton John William Individual identity authentication system
US20040243588A1 (en) 2003-05-29 2004-12-02 Thomas Tanner Systems and methods for administering a global information database
US20050004805A1 (en) 2003-06-10 2005-01-06 Venkataraman Srinivasan System and method of suggestive analysis of customer data
US7747559B2 (en) 2003-06-13 2010-06-29 Equifax, Inc. Systems and processes for automated criteria and attribute generation, searching, auditing and reporting of data
US20050027633A1 (en) 2003-06-25 2005-02-03 Joey Fortuna Application and processes for the review and adjustment of the full lifecycle of consumer finances
US20050027632A1 (en) 2003-07-31 2005-02-03 Ubs Financial Services, Inc. Financial investment advice system and method
US20050144143A1 (en) 2003-09-03 2005-06-30 Steven Freiberg Method and system for identity theft prevention, detection and victim assistance
US7835983B2 (en) 2003-09-18 2010-11-16 Trans Union Llc Credit approval monitoring system and method
US20050086071A1 (en) 2003-10-15 2005-04-21 Fox Charles S.Jr. System and method for managing patient care
US20050086072A1 (en) 2003-10-15 2005-04-21 Fox Charles S.Jr. Task-based system and method for managing patient care through automated recognition
US7314162B2 (en) 2003-10-17 2008-01-01 Digimore Corporation Method and system for reporting identity document usage
US7527967B2 (en) 2003-11-25 2009-05-05 Academia Sinica Recombinant baculovirus and virus-like particle
US8489498B1 (en) 2003-12-01 2013-07-16 Fannie Mae System and method for processing a loan
US20050125350A1 (en) 2003-12-09 2005-06-09 Tidwell Lisa C. Systems and methods for assessing the risk of financial transaction using geographic-related information
US20050130704A1 (en) 2003-12-15 2005-06-16 Dun & Bradstreet, Inc. Credit limit recommendation
US20050144067A1 (en) 2003-12-19 2005-06-30 Palo Alto Research Center Incorporated Identifying and reporting unexpected behavior in targeted advertising environment
JP4069078B2 (en) 2004-01-07 2008-03-26 松下電器産業株式会社 DRAM control device and DRAM control method
EP1714424A4 (en) 2004-02-05 2010-12-15 Veritas Mobile Solutions Pte L System and method for authenticating the identity of a user
US7630933B2 (en) 2004-02-20 2009-12-08 Horizon Digital Finance, Llc System and method for matching loan consumers and lenders
US7380707B1 (en) 2004-02-25 2008-06-03 Jpmorgan Chase Bank, N.A. Method and system for credit card reimbursements for health care transactions
US7467127B1 (en) 2004-02-27 2008-12-16 Hyperion Solutions Corporation View selection for a multidimensional database
US7708190B2 (en) 2004-03-10 2010-05-04 At&T Intellectual Property I, L.P. Multiple options to decline authorization of payment card charges
US20050209922A1 (en) 2004-03-19 2005-09-22 Hofmeister Kurt J Credit based product marketing method
US20070067297A1 (en) 2004-04-30 2007-03-22 Kublickis Peter J System and methods for a micropayment-enabled marketplace with permission-based, self-service, precision-targeted delivery of advertising, entertainment and informational content and relationship marketing to anonymous internet users
US20050256809A1 (en) 2004-05-14 2005-11-17 Pasha Sadri Systems and methods for providing notification and feedback based on electronic payment transactions
WO2005114886A2 (en) 2004-05-21 2005-12-01 Rsa Security Inc. System and method of fraud reduction
US20050278246A1 (en) 2004-06-14 2005-12-15 Mark Friedman Software solution management of problem loans
WO2005124677A2 (en) 2004-06-14 2005-12-29 Dun & Bradstreet System and method for self-monitoring credit information
US7272728B2 (en) 2004-06-14 2007-09-18 Iovation, Inc. Network security and fraud detection system and method
US7314166B2 (en) 2004-06-16 2008-01-01 American Express Travel Related Services Company, Inc. System and method for calculating recommended charge limits
EP1626369A1 (en) 2004-08-13 2006-02-15 EBS Group limited Automated trading system
US7298872B2 (en) 2004-08-17 2007-11-20 Shawn Glisson Electronic identification system for form location, organization, and endorsment
US20060041464A1 (en) 2004-08-19 2006-02-23 Transunion Llc. System and method for developing an analytic fraud model
US20070093234A1 (en) 2004-08-20 2007-04-26 Willis John A Identify theft protection and notification system
US20060080263A1 (en) 2004-10-13 2006-04-13 Willis John A Identity theft protection and notification system
US20060041443A1 (en) 2004-08-23 2006-02-23 Horvath Charles W Jr Variable data business system and method therefor
EP1785421A1 (en) 2004-08-30 2007-05-16 Ono Pharmaceutical Co., Ltd. Tropan compound
US7970672B2 (en) 2004-09-01 2011-06-28 Metareward, Inc. Real-time marketing of credit-based goods or services
US7904306B2 (en) 2004-09-01 2011-03-08 Search America, Inc. Method and apparatus for assessing credit for healthcare patients
US7590589B2 (en) 2004-09-10 2009-09-15 Hoffberg Steven M Game theoretic prioritization scheme for mobile ad hoc networks permitting hierarchal deference
US20060059073A1 (en) 2004-09-15 2006-03-16 Walzak Rebecca B System and method for analyzing financial risk
WO2006036814A2 (en) 2004-09-22 2006-04-06 Citibank, N.A. Systems and methods for offering credit line products
US20060080139A1 (en) 2004-10-08 2006-04-13 Woodhaven Health Services Preadmission health care cost and reimbursement estimation tool
US8326672B2 (en) 2004-10-29 2012-12-04 American Express Travel Related Services Company, Inc. Using commercial share of wallet in financial databases
US7814004B2 (en) 2004-10-29 2010-10-12 American Express Travel Related Services Company, Inc. Method and apparatus for development and use of a credit score based on spend capacity
US8630929B2 (en) 2004-10-29 2014-01-14 American Express Travel Related Services Company, Inc. Using commercial share of wallet to make lending decisions
US20070244732A1 (en) 2004-10-29 2007-10-18 American Express Travel Related Services Co., Inc., A New York Corporation Using commercial share of wallet to manage vendors
US7788147B2 (en) 2004-10-29 2010-08-31 American Express Travel Related Services Company, Inc. Method and apparatus for estimating the spend capacity of consumers
US7912770B2 (en) 2004-10-29 2011-03-22 American Express Travel Related Services Company, Inc. Method and apparatus for consumer interaction based on spend capacity
US7840484B2 (en) 2004-10-29 2010-11-23 American Express Travel Related Services Company, Inc. Credit score and scorecard development
US20060242050A1 (en) 2004-10-29 2006-10-26 American Express Travel Related Services Company, Inc. Method and apparatus for targeting best customers based on spend capacity
EP1836674A4 (en) 2004-11-16 2009-12-16 Health Dialog Data Service Inc Systems and methods for predicting healthcare related risk events and financial risk
US20060131390A1 (en) 2004-12-16 2006-06-22 Kim Mike I Method and system for providing transaction notification and mobile reply authorization
WO2006069199A2 (en) 2004-12-20 2006-06-29 Armorpoint, Inc. Personal credit management and monitoring system and method
US7581112B2 (en) 2004-12-30 2009-08-25 Ebay, Inc. Identifying fraudulent activities and the perpetrators thereof
US20060153346A1 (en) 2005-01-11 2006-07-13 Metro Enterprises, Inc. On-line authentication registration system
US20060173776A1 (en) 2005-01-28 2006-08-03 Barry Shalley A Method of Authentication
US20060173772A1 (en) 2005-02-02 2006-08-03 Hayes John B Systems and methods for automated processing, handling, and facilitating a trade credit transaction
US20060178983A1 (en) 2005-02-07 2006-08-10 Robert Nice Mortgage broker system allowing broker to match mortgagor with multiple lenders and method therefor
US7314167B1 (en) 2005-03-08 2008-01-01 Pisafe, Inc. Method and apparatus for providing secure identification, verification and authorization
WO2006099081A2 (en) 2005-03-10 2006-09-21 Debix, Inc. Method and system for managing account information
US20060235743A1 (en) 2005-04-18 2006-10-19 Sbc Knowledge Ventures, Lp System and method for determining profitability scores
US20060239512A1 (en) 2005-04-22 2006-10-26 Imme, Llc Anti-identity theft system and method
US20060265243A1 (en) 2005-05-20 2006-11-23 Jeffrey Racho System and method for establishing or verifying a person's identity using SMS and MMS over a wireless communications network
US10643217B2 (en) 2005-05-26 2020-05-05 Efunds Corporation Debit-based identity theft monitoring and prevention
US20060271457A1 (en) 2005-05-26 2006-11-30 Romain Martin R Identity theft monitoring and prevention
US8271364B2 (en) 2005-06-09 2012-09-18 Bank Of America Corporation Method and apparatus for obtaining, organizing, and analyzing multi-source data
US7343149B2 (en) 2005-06-13 2008-03-11 Lucent Technologies Inc. Network support for credit card notification
TW200701732A (en) 2005-06-21 2007-01-01 Ite2 Technology Inc Method and system for verifying personal identity in internet trades
US20060294199A1 (en) 2005-06-24 2006-12-28 The Zeppo Network, Inc. Systems and Methods for Providing A Foundational Web Platform
US20070016522A1 (en) 2005-07-15 2007-01-18 Zhiping Wang Data processing system for a billing address-based credit watch
US7556192B2 (en) 2005-08-04 2009-07-07 Capital One Financial Corp. Systems and methods for decisioning or approving a financial credit account based on a customer's check-writing behavior
US7860805B2 (en) 2005-08-15 2010-12-28 Personal Estate Manager, Inc. Computer-implemented personal information manager method and system
US20070061195A1 (en) 2005-09-13 2007-03-15 Yahoo! Inc. Framework for selecting and delivering advertisements over a network based on combined short-term and long-term user behavioral interests
US8396747B2 (en) 2005-10-07 2013-03-12 Kemesa Inc. Identity theft and fraud protection system and method
US7672865B2 (en) 2005-10-21 2010-03-02 Fair Isaac Corporation Method and apparatus for retail data mining using pair-wise co-occurrence consistency
US20080228635A1 (en) 2005-10-24 2008-09-18 Megdal Myles G Reducing risks related to check verification
US20080255897A1 (en) 2005-10-24 2008-10-16 Megdal Myles G Using commercial share of wallet in financial databases
US8346638B2 (en) 2005-10-26 2013-01-01 Capital One Financial Corporation Systems and methods for processing transaction data to perform a merchant chargeback
US8560350B2 (en) 2005-11-22 2013-10-15 Robert J. Nadai Method, system and computer program product for generating an electronic bill having optimized insurance claim items
US20070299699A1 (en) 2006-01-05 2007-12-27 Thomas Policelli System and Method for Initiation of Payment of a Member Cost Portion of Insurance Claim Expenses
US7610257B1 (en) 2006-01-10 2009-10-27 Sas Institute Inc. Computer-implemented risk evaluation systems and methods
US7664691B2 (en) 2006-02-01 2010-02-16 Intuit Inc. Method and apparatus for facilitating financial monitoring by guardians
US20070198407A1 (en) 2006-02-02 2007-08-23 Ntelagent Self-pay management system and process for the healthcare industry
US20070198336A1 (en) 2006-02-23 2007-08-23 Thompson Mark A Automated system and method for discounting medical bills of self-pay patients
US9996880B2 (en) 2006-02-28 2018-06-12 Intersections, Inc. Method and system for preventing and detecting identity theft
US7711636B2 (en) 2006-03-10 2010-05-04 Experian Information Solutions, Inc. Systems and methods for analyzing data
WO2007120793A2 (en) 2006-04-12 2007-10-25 Unifile, Llc Patient information storage and access
US7593549B2 (en) 2006-04-27 2009-09-22 Bruce Reiner Apparatus and method for utilizing biometrics in medical applications
US20070288271A1 (en) 2006-06-13 2007-12-13 Kirk William Klinkhammer Sub-prime automobile sale and finance system
US20080015979A1 (en) 2006-07-14 2008-01-17 Shanan Bentley Web-based searching for payment card products with credit pre-approvals
CA2660124A1 (en) 2006-08-07 2008-02-21 Dominium Intellectual Property Inc. Method and system for providing multiple funding sources for health insurance and other expenditures
US20080066188A1 (en) 2006-08-08 2008-03-13 Dusic Kwak Identity verification system
US8458062B2 (en) 2006-08-11 2013-06-04 Capital One Financial Corporation Real-time product matching
US8027888B2 (en) 2006-08-31 2011-09-27 Experian Interactive Innovation Center, Llc Online credit card prescreen systems and methods
US20080077526A1 (en) 2006-09-20 2008-03-27 First Data Corporation Online payer authorization systems and methods
US7801811B1 (en) 2006-10-10 2010-09-21 United Services Automobile Association (Usaa) Methods of and systems for money laundering risk assessment
US7805362B1 (en) 2006-10-10 2010-09-28 United Services Automobile Association (Usaa) Methods of and systems for money laundering risk assessment
US7860786B2 (en) 2006-10-17 2010-12-28 Canopy Acquisition, Llc Predictive score for lending
GB0621189D0 (en) 2006-10-25 2006-12-06 Payfont Ltd Secure authentication and payment system
US7856494B2 (en) 2006-11-14 2010-12-21 Fmr Llc Detecting and interdicting fraudulent activity on a network
US20080120133A1 (en) 2006-11-21 2008-05-22 Arvind Krishnaswami Method for predicting the payment of medical debt
US20080126233A1 (en) 2006-11-29 2008-05-29 Verizon Services Organization Inc. Purchase notification system
US8239325B2 (en) 2007-01-18 2012-08-07 Paymentone Corporation Method and system to verify the identity of a user
EP2111593A2 (en) 2007-01-26 2009-10-28 Information Resources, Inc. Analytic platform
US7949597B2 (en) 2007-02-02 2011-05-24 Zadoorian James A Method of collecting delinquent specialized debt
CN101291329A (en) 2007-04-16 2008-10-22 林仲宇 Method for network on-line payment double authentication by telephone and identifying card
US10769290B2 (en) 2007-05-11 2020-09-08 Fair Isaac Corporation Systems and methods for fraud detection via interactive link analysis
US7575157B2 (en) 2007-05-22 2009-08-18 Bank Of America Corporation Fraud protection
US7620596B2 (en) 2007-06-01 2009-11-17 The Western Union Company Systems and methods for evaluating financial transaction risk
US8214291B2 (en) 2007-10-19 2012-07-03 Ebay Inc. Unified identity verification
US7653593B2 (en) 2007-11-08 2010-01-26 Equifax, Inc. Macroeconomic-adjusted credit risk score systems and methods
US7849004B2 (en) 2008-02-29 2010-12-07 American Express Travel Related Services Company, Inc. Total structural risk model
US8458083B2 (en) 2008-02-29 2013-06-04 American Express Travel Related Services Company, Inc. Total structural risk model
US20090222378A1 (en) 2008-02-29 2009-09-03 American Express Travel Related Services Company, Inc. Total structural risk model
US20090222376A1 (en) 2008-02-29 2009-09-03 American Express Travel Related Services Company, Inc. Total structural risk model
US20090222373A1 (en) 2008-02-29 2009-09-03 American Express Travel Related Services Company, Inc. Total structural risk model
US20090222380A1 (en) 2008-02-29 2009-09-03 American Express Travel Related Services Company, Inc Total structural risk model
US7853520B2 (en) 2008-02-29 2010-12-14 American Express Travel Related Services Company, Inc. Total structural risk model
US7814008B2 (en) 2008-02-29 2010-10-12 American Express Travel Related Services Company, Inc. Total structural risk model
US20090222308A1 (en) 2008-03-03 2009-09-03 Zoldi Scott M Detecting first party fraud abuse
US20090248572A1 (en) 2008-03-28 2009-10-01 American Express Travel Related Services Company, Inc. Consumer behaviors at lender level
US7877323B2 (en) 2008-03-28 2011-01-25 American Express Travel Related Services Company, Inc. Consumer behaviors at lender level
US7844544B2 (en) 2008-03-28 2010-11-30 American Express Travel Related Services Company, Inc. Consumer behaviors at lender level
US20090248569A1 (en) 2008-03-28 2009-10-01 American Express Travel Related Services Company, Inc. Consumer behaviors at lender level
US7805363B2 (en) 2008-03-28 2010-09-28 American Express Travel Related Services Company, Inc. Consumer behaviors at lender level
US7882027B2 (en) 2008-03-28 2011-02-01 American Express Travel Related Services Company, Inc. Consumer behaviors at lender level
US20090248573A1 (en) 2008-03-28 2009-10-01 American Express Travel Related Services Company, Inc. Consumer behaviors at lender level
US20090254476A1 (en) 2008-04-04 2009-10-08 Quickreceipt Solutions Incorporated Method and system for managing personal and financial information
US8943549B2 (en) 2008-08-12 2015-01-27 First Data Corporation Methods and systems for online fraud protection
US8307412B2 (en) 2008-10-20 2012-11-06 Microsoft Corporation User authentication management
US8117106B2 (en) 2008-10-30 2012-02-14 Telesign Corporation Reputation scoring and reporting system
US8244643B2 (en) 2008-11-08 2012-08-14 Fonwallet Transaction Solutions, Inc. System and method for processing financial transaction data using an intermediary service
US9357384B2 (en) 2009-02-09 2016-05-31 International Business Machines Corporation System and method to support identity theft protection as part of a distributed service oriented ecosystem
US7690032B1 (en) 2009-05-22 2010-03-30 Daon Holdings Limited Method and system for confirming the identity of a user

Patent Citations (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6088686A (en) * 1995-12-12 2000-07-11 Citibank, N.A. System and method to performing on-line credit reviews and approvals
US6072894A (en) * 1997-10-17 2000-06-06 Payne; John H. Biometric face recognition for applicant screening
US6064990A (en) * 1998-03-31 2000-05-16 International Business Machines Corporation System for electronic notification of account activity
US20020077964A1 (en) * 1999-12-15 2002-06-20 Brody Robert M. Systems and methods for providing consumers anonymous pre-approved offers from a consumer-selected group of merchants
US7154375B2 (en) * 2001-07-10 2006-12-26 American Express Travel Related Services Company, Inc. Biometric safeguard method with a fob
US7303120B2 (en) * 2001-07-10 2007-12-04 American Express Travel Related Services Company, Inc. System for biometric security using a FOB
US7059531B2 (en) * 2001-07-10 2006-06-13 American Express Travel Related Services Company, Inc. Method and system for smellprint recognition biometrics on a fob
US7121471B2 (en) * 2001-07-10 2006-10-17 American Express Travel Related Services Company, Inc. Method and system for DNA recognition biometrics on a fob
US20030078877A1 (en) * 2001-10-18 2003-04-24 Kenneth Beirne Method, system, and storage medium for pre-screening customers for credit card approval at a point of sale
US20040039686A1 (en) * 2002-01-10 2004-02-26 Klebanoff Victor Franklin Method and system for detecting payment account fraud
US7428509B2 (en) * 2002-01-10 2008-09-23 Mastercard International Incorporated Method and system for detecting payment account fraud
US20070226093A1 (en) * 2002-12-20 2007-09-27 Chan Cynthia M Financial services data model
US20070106582A1 (en) * 2005-10-04 2007-05-10 Baker James C System and method of detecting fraud
US7668769B2 (en) * 2005-10-04 2010-02-23 Basepoint Analytics, LLC System and method of detecting fraud
US20100145836A1 (en) * 2005-10-04 2010-06-10 Basepoint Analytics Llc System and method of detecting fraud
US20080294540A1 (en) * 2007-05-25 2008-11-27 Celka Christopher J System and method for automated detection of never-pay data sets

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ANONYMOUS "Fair Isaac Introduces Falcon One System to Combat Fraud at Every Customer Interaction; New, Flexible Enterprise Software System Helps Financial Institutions Detect Many Types of Fraud, Improve Profitability and Reinforce Customer Trust"May 5 , 2005 Business Wire , NA *
ANONYMOUS "Fair Isaac Offers New Fraud Tool "June 13, 2005 NATIONAL MORTGAGE NEWS *
ANONYMOUS "Tackling the issue of bust-out fraud"July 24, 2007 RETAIL BANKER INTERNATIONAL *

Cited By (138)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9710852B1 (en) 2002-05-30 2017-07-18 Consumerinfo.Com, Inc. Credit report timeline user interface
US9400589B1 (en) 2002-05-30 2016-07-26 Consumerinfo.Com, Inc. Circular rotational interface for display of consumer credit information
US9058627B1 (en) 2002-05-30 2015-06-16 Consumerinfo.Com, Inc. Circular rotational interface for display of consumer credit information
US8781953B2 (en) 2003-03-21 2014-07-15 Consumerinfo.Com, Inc. Card management system and method
US8930263B1 (en) 2003-05-30 2015-01-06 Consumerinfo.Com, Inc. Credit data analysis
US8452611B1 (en) 2004-09-01 2013-05-28 Search America, Inc. Method and apparatus for assessing credit for healthcare patients
US8930216B1 (en) 2004-09-01 2015-01-06 Search America, Inc. Method and apparatus for assessing credit for healthcare patients
US11157997B2 (en) 2006-03-10 2021-10-26 Experian Information Solutions, Inc. Systems and methods for analyzing data
US10380654B2 (en) 2006-08-17 2019-08-13 Experian Information Solutions, Inc. System and method for providing a score for a used vehicle
US11257126B2 (en) 2006-08-17 2022-02-22 Experian Information Solutions, Inc. System and method for providing a score for a used vehicle
US8626646B2 (en) 2006-10-05 2014-01-07 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US10963961B1 (en) 2006-10-05 2021-03-30 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US11631129B1 (en) 2006-10-05 2023-04-18 Experian Information Solutions, Inc System and method for generating a finance attribute from tradeline data
US9563916B1 (en) 2006-10-05 2017-02-07 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US10121194B1 (en) 2006-10-05 2018-11-06 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US8738515B2 (en) 2007-04-12 2014-05-27 Experian Marketing Solutions, Inc. Systems and methods for determining thin-file records and determining thin-file risk levels
US8364588B2 (en) 2007-05-25 2013-01-29 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US9251541B2 (en) 2007-05-25 2016-02-02 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US20090035069A1 (en) * 2007-07-30 2009-02-05 Drew Krehbiel Methods and apparatus for protecting offshore structures
US11347715B2 (en) 2007-09-27 2022-05-31 Experian Information Solutions, Inc. Database system for triggering event notifications based on updates to database records
US9690820B1 (en) 2007-09-27 2017-06-27 Experian Information Solutions, Inc. Database system for triggering event notifications based on updates to database records
US10528545B1 (en) 2007-09-27 2020-01-07 Experian Information Solutions, Inc. Database system for triggering event notifications based on updates to database records
US9058340B1 (en) 2007-11-19 2015-06-16 Experian Marketing Solutions, Inc. Service for associating network users with profiles
US9767513B1 (en) 2007-12-14 2017-09-19 Consumerinfo.Com, Inc. Card registry systems and methods
US11379916B1 (en) 2007-12-14 2022-07-05 Consumerinfo.Com, Inc. Card registry systems and methods
US10262364B2 (en) 2007-12-14 2019-04-16 Consumerinfo.Com, Inc. Card registry systems and methods
US9230283B1 (en) 2007-12-14 2016-01-05 Consumerinfo.Com, Inc. Card registry systems and methods
US10614519B2 (en) 2007-12-14 2020-04-07 Consumerinfo.Com, Inc. Card registry systems and methods
US8464939B1 (en) 2007-12-14 2013-06-18 Consumerinfo.Com, Inc. Card registry systems and methods
US9542682B1 (en) 2007-12-14 2017-01-10 Consumerinfo.Com, Inc. Card registry systems and methods
US10878499B2 (en) 2007-12-14 2020-12-29 Consumerinfo.Com, Inc. Card registry systems and methods
US8930251B2 (en) 2008-06-18 2015-01-06 Consumerinfo.Com, Inc. Debt trending systems and methods
US10115155B1 (en) 2008-08-14 2018-10-30 Experian Information Solution, Inc. Multi-bureau credit file freeze and unfreeze
US11636540B1 (en) 2008-08-14 2023-04-25 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US9489694B2 (en) 2008-08-14 2016-11-08 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US11004147B1 (en) 2008-08-14 2021-05-11 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US10650448B1 (en) 2008-08-14 2020-05-12 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US9256904B1 (en) 2008-08-14 2016-02-09 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US9792648B1 (en) 2008-08-14 2017-10-17 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US8595101B1 (en) 2008-09-08 2013-11-26 Exerian Information Solutions, Inc. Systems and methods for managing consumer accounts using data migration
US20100094758A1 (en) * 2008-10-13 2010-04-15 Experian Marketing Solutions, Inc. Systems and methods for providing real time anonymized marketing information
US9595051B2 (en) 2009-05-11 2017-03-14 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US8639920B2 (en) 2009-05-11 2014-01-28 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US8966649B2 (en) 2009-05-11 2015-02-24 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US8626560B1 (en) 2009-06-30 2014-01-07 Experian Information Solutions, Inc. System and method for evaluating vehicle purchase loyalty
US8364518B1 (en) 2009-07-08 2013-01-29 Experian Ltd. Systems and methods for forecasting household economics
US10909617B2 (en) 2010-03-24 2021-02-02 Consumerinfo.Com, Inc. Indirect monitoring and reporting of a user's credit data
US9652802B1 (en) 2010-03-24 2017-05-16 Consumerinfo.Com, Inc. Indirect monitoring and reporting of a user's credit data
US8725613B1 (en) 2010-04-27 2014-05-13 Experian Information Solutions, Inc. Systems and methods for early account score and notification
US9152727B1 (en) 2010-08-23 2015-10-06 Experian Marketing Solutions, Inc. Systems and methods for processing consumer information for targeted marketing applications
US10417704B2 (en) 2010-11-02 2019-09-17 Experian Technology Ltd. Systems and methods of assisted strategy design
US8930262B1 (en) 2010-11-02 2015-01-06 Experian Technology Ltd. Systems and methods of assisted strategy design
US9684905B1 (en) 2010-11-22 2017-06-20 Experian Information Solutions, Inc. Systems and methods for data verification
US9147042B1 (en) 2010-11-22 2015-09-29 Experian Information Solutions, Inc. Systems and methods for data verification
US10593004B2 (en) 2011-02-18 2020-03-17 Csidentity Corporation System and methods for identifying compromised personally identifiable information on the internet
US11861691B1 (en) 2011-04-29 2024-01-02 Consumerinfo.Com, Inc. Exposing reporting cycle information
US9558519B1 (en) 2011-04-29 2017-01-31 Consumerinfo.Com, Inc. Exposing reporting cycle information
US10719873B1 (en) 2011-06-16 2020-07-21 Consumerinfo.Com, Inc. Providing credit inquiry alerts
US9607336B1 (en) 2011-06-16 2017-03-28 Consumerinfo.Com, Inc. Providing credit inquiry alerts
US10798197B2 (en) 2011-07-08 2020-10-06 Consumerinfo.Com, Inc. Lifescore
US10176233B1 (en) 2011-07-08 2019-01-08 Consumerinfo.Com, Inc. Lifescore
US11665253B1 (en) 2011-07-08 2023-05-30 Consumerinfo.Com, Inc. LifeScore
US9483606B1 (en) 2011-07-08 2016-11-01 Consumerinfo.Com, Inc. Lifescore
US8775299B2 (en) 2011-07-12 2014-07-08 Experian Information Solutions, Inc. Systems and methods for large-scale credit data processing
US11568348B1 (en) 2011-10-31 2023-01-31 Consumerinfo.Com, Inc. Pre-data breach monitoring
US11030562B1 (en) 2011-10-31 2021-06-08 Consumerinfo.Com, Inc. Pre-data breach monitoring
US11356430B1 (en) 2012-05-07 2022-06-07 Consumerinfo.Com, Inc. Storage and maintenance of personal data
US9853959B1 (en) 2012-05-07 2017-12-26 Consumerinfo.Com, Inc. Storage and maintenance of personal data
US8621244B1 (en) 2012-10-04 2013-12-31 Datalogix Inc. Method and apparatus for matching consumers
US20140129420A1 (en) * 2012-11-08 2014-05-08 Mastercard International Incorporated Telecom social network analysis driven fraud prediction and credit scoring
US11012491B1 (en) 2012-11-12 2021-05-18 ConsumerInfor.com, Inc. Aggregating user web browsing data
US11863310B1 (en) 2012-11-12 2024-01-02 Consumerinfo.Com, Inc. Aggregating user web browsing data
US9654541B1 (en) 2012-11-12 2017-05-16 Consumerinfo.Com, Inc. Aggregating user web browsing data
US10277659B1 (en) 2012-11-12 2019-04-30 Consumerinfo.Com, Inc. Aggregating user web browsing data
US8856894B1 (en) 2012-11-28 2014-10-07 Consumerinfo.Com, Inc. Always on authentication
US10366450B1 (en) 2012-11-30 2019-07-30 Consumerinfo.Com, Inc. Credit data analysis
US11308551B1 (en) 2012-11-30 2022-04-19 Consumerinfo.Com, Inc. Credit data analysis
US11132742B1 (en) 2012-11-30 2021-09-28 Consumerlnfo.com, Inc. Credit score goals and alerts systems and methods
US11651426B1 (en) 2012-11-30 2023-05-16 Consumerlnfo.com, Inc. Credit score goals and alerts systems and methods
US9830646B1 (en) 2012-11-30 2017-11-28 Consumerinfo.Com, Inc. Credit score goals and alerts systems and methods
US10963959B2 (en) 2012-11-30 2021-03-30 Consumerinfo. Com, Inc. Presentation of credit score factors
US9916621B1 (en) 2012-11-30 2018-03-13 Consumerinfo.Com, Inc. Presentation of credit score factors
US10255598B1 (en) 2012-12-06 2019-04-09 Consumerinfo.Com, Inc. Credit card account data extraction
US10373194B2 (en) 2013-02-20 2019-08-06 Datalogix Holdings, Inc. System and method for measuring advertising effectiveness
US10282748B2 (en) 2013-02-20 2019-05-07 Datalogix Holdings, Inc. System and method for measuring advertising effectiveness
US9697263B1 (en) 2013-03-04 2017-07-04 Experian Information Solutions, Inc. Consumer data request fulfillment system
US10592982B2 (en) 2013-03-14 2020-03-17 Csidentity Corporation System and method for identifying related credit inquiries
US10740762B2 (en) 2013-03-15 2020-08-11 Consumerinfo.Com, Inc. Adjustment of knowledge-based authentication
US11775979B1 (en) 2013-03-15 2023-10-03 Consumerinfo.Com, Inc. Adjustment of knowledge-based authentication
US10169761B1 (en) 2013-03-15 2019-01-01 ConsumerInfo.com Inc. Adjustment of knowledge-based authentication
US9633322B1 (en) 2013-03-15 2017-04-25 Consumerinfo.Com, Inc. Adjustment of knowledge-based authentication
US11288677B1 (en) 2013-03-15 2022-03-29 Consumerlnfo.com, Inc. Adjustment of knowledge-based authentication
US10685398B1 (en) 2013-04-23 2020-06-16 Consumerinfo.Com, Inc. Presenting credit score information
US10102536B1 (en) 2013-11-15 2018-10-16 Experian Information Solutions, Inc. Micro-geographic aggregation system
US10580025B2 (en) 2013-11-15 2020-03-03 Experian Information Solutions, Inc. Micro-geographic aggregation system
USD759690S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD759689S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD760256S1 (en) 2014-03-25 2016-06-28 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
US11620314B1 (en) 2014-05-07 2023-04-04 Consumerinfo.Com, Inc. User rating based on comparing groups
US10936629B2 (en) 2014-05-07 2021-03-02 Consumerinfo.Com, Inc. Keeping up with the joneses
US9576030B1 (en) 2014-05-07 2017-02-21 Consumerinfo.Com, Inc. Keeping up with the joneses
US10019508B1 (en) 2014-05-07 2018-07-10 Consumerinfo.Com, Inc. Keeping up with the joneses
US10990979B1 (en) 2014-10-31 2021-04-27 Experian Information Solutions, Inc. System and architecture for electronic fraud detection
US10339527B1 (en) 2014-10-31 2019-07-02 Experian Information Solutions, Inc. System and architecture for electronic fraud detection
US11436606B1 (en) 2014-10-31 2022-09-06 Experian Information Solutions, Inc. System and architecture for electronic fraud detection
US10445152B1 (en) 2014-12-19 2019-10-15 Experian Information Solutions, Inc. Systems and methods for dynamic report generation based on automatic modeling of complex data structures
US11010345B1 (en) 2014-12-19 2021-05-18 Experian Information Solutions, Inc. User behavior segmentation using latent topic detection
US10242019B1 (en) 2014-12-19 2019-03-26 Experian Information Solutions, Inc. User behavior segmentation using latent topic detection
US11151468B1 (en) 2015-07-02 2021-10-19 Experian Information Solutions, Inc. Behavior analysis using distributed representations of event data
US11159593B1 (en) 2015-11-24 2021-10-26 Experian Information Solutions, Inc. Real-time event-based notification system
US10757154B1 (en) 2015-11-24 2020-08-25 Experian Information Solutions, Inc. Real-time event-based notification system
US11729230B1 (en) 2015-11-24 2023-08-15 Experian Information Solutions, Inc. Real-time event-based notification system
US10699319B1 (en) 2016-05-12 2020-06-30 State Farm Mutual Automobile Insurance Company Cross selling recommendation engine
US10832249B1 (en) 2016-05-12 2020-11-10 State Farm Mutual Automobile Insurance Company Heuristic money laundering detection engine
US10810593B1 (en) 2016-05-12 2020-10-20 State Farm Mutual Automobile Insurance Company Heuristic account fraud detection engine
US11164238B1 (en) 2016-05-12 2021-11-02 State Farm Mutual Automobile Insurance Company Cross selling recommendation engine
US10970641B1 (en) 2016-05-12 2021-04-06 State Farm Mutual Automobile Insurance Company Heuristic context prediction engine
US11164091B1 (en) 2016-05-12 2021-11-02 State Farm Mutual Automobile Insurance Company Natural language troubleshooting engine
US11032422B1 (en) 2016-05-12 2021-06-08 State Farm Mutual Automobile Insurance Company Heuristic sales agent training assistant
US11461840B1 (en) 2016-05-12 2022-10-04 State Farm Mutual Automobile Insurance Company Heuristic document verification and real time deposit engine
US11544783B1 (en) 2016-05-12 2023-01-03 State Farm Mutual Automobile Insurance Company Heuristic credit risk assessment engine
US10769722B1 (en) * 2016-05-12 2020-09-08 State Farm Mutual Automobile Insurance Company Heuristic credit risk assessment engine
US11556934B1 (en) 2016-05-12 2023-01-17 State Farm Mutual Automobile Insurance Company Heuristic account fraud detection engine
US11734690B1 (en) 2016-05-12 2023-08-22 State Farm Mutual Automobile Insurance Company Heuristic money laundering detection engine
US10810663B1 (en) 2016-05-12 2020-10-20 State Farm Mutual Automobile Insurance Company Heuristic document verification and real time deposit engine
US11550886B2 (en) 2016-08-24 2023-01-10 Experian Information Solutions, Inc. Disambiguation and authentication of device users
US10678894B2 (en) 2016-08-24 2020-06-09 Experian Information Solutions, Inc. Disambiguation and authentication of device users
US11681733B2 (en) 2017-01-31 2023-06-20 Experian Information Solutions, Inc. Massive scale heterogeneous data ingestion and user resolution
US11227001B2 (en) 2017-01-31 2022-01-18 Experian Information Solutions, Inc. Massive scale heterogeneous data ingestion and user resolution
US10735183B1 (en) 2017-06-30 2020-08-04 Experian Information Solutions, Inc. Symmetric encryption for private smart contracts among multiple parties in a private peer-to-peer network
US11652607B1 (en) 2017-06-30 2023-05-16 Experian Information Solutions, Inc. Symmetric encryption for private smart contracts among multiple parties in a private peer-to-peer network
US11157650B1 (en) 2017-09-28 2021-10-26 Csidentity Corporation Identity security architecture systems and methods
US10699028B1 (en) 2017-09-28 2020-06-30 Csidentity Corporation Identity security architecture systems and methods
US11580259B1 (en) 2017-09-28 2023-02-14 Csidentity Corporation Identity security architecture systems and methods
US10896472B1 (en) 2017-11-14 2021-01-19 Csidentity Corporation Security and identity verification system and architecture
US11366884B2 (en) 2018-02-14 2022-06-21 American Express Travel Related Services Company, Inc. Authentication challenges based on fraud initiation requests
WO2019160597A1 (en) * 2018-02-14 2019-08-22 American Express Travel Related Services Company, Inc. Authentication challenges based on fraud initiation requests
US11620403B2 (en) 2019-01-11 2023-04-04 Experian Information Solutions, Inc. Systems and methods for secure data aggregation and computation

Also Published As

Publication number Publication date
US7991689B1 (en) 2011-08-02
US8001042B1 (en) 2011-08-16

Similar Documents

Publication Publication Date Title
US7991689B1 (en) Systems and methods for detecting bust out fraud using credit data
US11399029B2 (en) Database platform for realtime updating of user data from third party sources
US9251541B2 (en) System and method for automated detection of never-pay data sets
US8725613B1 (en) Systems and methods for early account score and notification
US20200118235A1 (en) System, method and computer program product for assessing risk of identity theft
US8781956B2 (en) Systems and methods for making structured reference credit decisions
US20100325035A1 (en) Fraud/risk bureau
US20030009426A1 (en) Methods and apparatus for protecting against credit card fraud, check fraud, and identity theft
US20140324677A1 (en) Method and system for detecting, monitoring and investigating first party fraud
US20120109802A1 (en) Verifying identity through use of an integrated risk assessment and management system
US20060248019A1 (en) Method and system to detect fraud using voice data
US20100305946A1 (en) Speaker verification-based fraud system for combined automated risk score with agent review and associated user interface
US8799122B1 (en) Method and system for user contributed aggregated fraud identification
US10825109B2 (en) Predicting entity outcomes using taxonomy classifications of transactions
US20120209760A1 (en) Risk identification system and judgmental review interface
US10937035B1 (en) Systems and methods for a multi-tiered fraud alert review
US20210110359A1 (en) Dynamic virtual resource management system
US20200082407A1 (en) Instant funds availablity risk assessment and real-time fraud alert system and method
US20150019401A1 (en) Integrated credit decision platform
US11869008B2 (en) Minimizing risks posed to online services
Cheney et al. Identity theft as a teachable moment
Lyu Can Income Verification and Its Change Mitigate Information Asymmetry?{Evidence from LendingClub

Legal Events

Date Code Title Description
STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION