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Abstract

A method of fraud management follows several steps that each require the involvement and support of financial networks, secure servers, and proprietary databases. A selected variety of fraud classification algorithms are assembled together into a “jury” that includes neural networks, case based reasoning, decision trees, genetic algorithms, fuzzy logic, and rules and constraints. Operating parameters that matter specifically to each are extracted in parallel from the same records of historical transaction data, and that then is used to initialize a general payment fraud model. This is then converted into computer-program executable form for later execution on a third party computer system. These are further integrated by expert programmers and development system with smart agents and associated real-time profiling, recursive profiles, and long-term profiles. The trainable general payment fraud product is applied by a payments-processing client to screen real-time transactions and authorization requests for fraud.

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First Claim