Sas Institute Inc.

Fair Isaac Corporation

Ho Ming Luk

Anu Kumar Pathria

Revathi Subramanian

Paul Charles Dulany

Arati S Deo

Vijay Desai

Jean De Traversay

Louis S Biafore

Radu Drossu

Brian Lee Duke

Craig Nies

Kevin Chaowen Chen

William Szczuka

Andrew Engel

Pamela E Coates

Daniel J Dotson

Sean M Downs

Benjamin A Friesen

Chao-Wen (Kevin) Chen

Systems and methods are provided for operation upon data processing devices are provided for operating with a fraud detection system. As an example, a system and method can be configured for receiving, throughout a current day in real-time or near real-time, financial transaction data representative of financial transactions initiated by different entities. At multiple times throughout the day, a summarization of the financial transaction data (which has been received within a time period within the current day) is generated. The generated summarization is used to determine whether fraud has occurred with respect to a financial transaction contained in the received authorization data or with respect to a subsequently occurring financial transaction.

Computer-implemented multiple entity dynamic summarization systems and methods

Detection of insurance premium fraud is provided by a predictive model, which uses derived variables to assess the likelihood of fraud for each policy. The predictive model produces a score, which is a measure of the likelihood of premium fraud or abuse. The predictive model is included in a system that accepts policies to be considered for scoring, selects which policies are appropriate for scoring, stores data about the policies in a database, uses the data to derive variables for the model, and processes and outputs the model scores and related information. A rule-based analysis, which detects specific inconsistencies in the data that are indicative of premium fraud, may also be part of the system. The model scores and red-flag indicators from the rule-based analysis may be further processed to provide customized output for users.

Detection of insurance premium fraud or abuse using a predictive software system

Systems and methods for performing fraud detection. As an example, a system and method can be configured to build a set of predictive models to predict credit card or debit card fraud. A first predictive model is trained using a set of training data. A partitioning criterion is used to determine how to partition the training data into partitions. Another predictive model is trained using at least one of the partitions of training data in order to generate a second predictive model. The predictive models are combined for use in predicting credit card or debit card fraud.

Computer-implemented predictive model generation systems and methods

Computer implemented processes and software products generate profiles of entities, such as providers, clients, merchants and customers, and entities comprising interacting pairs of entities. The processes including deriving direct profiles from transaction data pertaining to an entity and enhancing the profile of one entity using the profile of another entity. Parallel and serial applications of the derive and enhance processes on various individual and multiple entites yields enhanced profiles that powerfully describe the interactions and relationship of the entities to each other, and between their members.

Cascaded profiles for multiple interacting entities

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San Diego, CA, United States of America

Ho Ming Luk