Fair Isaac Corporation

Jehangir Athwal

Scott Michael Zoldi

Hua Li

Matthew Kennel

Xiang Yang Li

Larry Peranich

Uwe Mayer

Xinwei Xue

Sajama

Xinwai Xue

Matthew Bochner Kennel

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 system and method of detecting command and control behavior of malware on a client computer is disclosed. One or more DNS messages are monitored from one or more client computers to a DNS server to determine a risk that one or more client computers is communicating with a botnet. Real-time entity profiles are generated for at least one of each of the one or more client computers, DNS domain query names, resolved IP addresses of query domain names, client computer-query domain name pairs, pairs of query domain name and corresponding resolved IP address, or query domain name-IP address cliques based on each of the one or more DNS messages. Using the real-time entity profiles, a risk that any of the one or more client computers is infected by malware that utilizes DNS messages for command and control or illegitimate data transmission purposes is determined. One or more scores are generated representing probabilities that one or more client computers is infected by malware.

Cyber security adaptive analytics threat monitoring system and method

A system and method for detecting fraud is presented. A self-calibrating outlier model is hosted by a computing system. The self-calibrating outlier model receives transaction data representing transactions, and is configured to calculate transaction-based variables, profiles and calibration parameters, and to produce a score based on the transaction data according to the transaction-based variables, profiles and calibration parameters. An adaptive cascade model is also hosted by the computing system, and is configured to generate a secondary score for the transaction data based on profile information from the variables and/or profiles calculated by the self-calibrating outlier model, and based on a comparison with labeled transactions from a human analyst of historical transaction data.

Self-calibrating outlier model and adaptive cascade model for fraud detection

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San Diego, CA, United States of America

Jehangir Athwal