Fair Isaac Corporation

Other

Fair Issac Corporation

Uwe Mayer

Scott Michael Zoldi

Larry Peranich

Michael A Lazarus

Theodore J Crooks

Shane De Zilwa

Anthony Vaiciulis

Matthias Blume

Vijay Krishna Narayanan

Jehangir Athwal

Sajama

A computer-based fraud detection system stores extracts of the payment and other activity of the payer as well as the payee by efficiently profiling their accounts, and also stores extracts of the overall global or segmented payment activities. This profile information is then used to effectively capture fraudulent activity. The fraud detection system accepts messages from the payment processing system or from the associated financial institution, and depending on the nature of the message will update its databases, or assess fraud risk by computing a score, or both. If a score is computed, the higher the score, the greater the likelihood that the transaction is fraudulent. Said score is based on automatically determined risky values of variables and is also be automatically scaled using self-calibrating analytics using the captured profile information. In a typical scenario the fraud detection system client sets a 'threshold score' above which the transactions are considered suspect enough to be investigated further or to trigger a payment decline outright. The solution is suitable for being installed at a financial institution, on the network, or some other institutional participant of the Faster Payments System (FPS), such as an access service provider to the FPS for corporate clients.

Fraud detection system for the faster payments system

A computer-implemented method includes receiving a new data record associated with a transaction, and generating, using an adaptive model executed by the computer, a score to represent a likelihood that the transaction is associated with fraud. The adaptive model employs feedback from one or more external data sources, the feedback containing information about one or more previous data records associated with fraud and non-fraud by at least one of the one or more external data sources. Further, the adaptive model uses the information about the one or more previous data records as input variables to update scoring parameters used to generate the score for the new data record.

Adaptive fraud detection

A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Automated entity identification for efficient profiling in an event probability prediction system

A system and method for protecting identity fraud are disclosed. A system includes a detection subsystem to identify applications and/or accounts at risk of identity fraud, and a disposition subsystem to process data provided by the detection system and to determine whether identity fraud exists in the applications and/or accounts. According to an implementation, one or more neural network models are defined, each neural network model being configured to handle a class of cases related to the subject and a specific data configuration describing a case of the class. The one or more neural network models are run to generate data requests about the subject's identity, and the data requests are passed to a detection system that monitor transactions associated with the subject. Additional data associated with the transactions is requested until a threshold certainty is achieved or until available data or models are exhausted.

Comprehensive identity protection system

A computer-implemented technique for fuzzy matching. This works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match.

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Encinitas, CA, United States of America

Uwe Mayer