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Abstract

An artificial intelligence cross-channel fraud management system comprises a parallel arrangement of single-channel, fully trained fraud models that each integrate several artificial intelligence classifiers like neural networks, case based reasoning, decision trees, genetic algorithms, fuzzy logic, and rules and constraints. These are further integrated by the expert programmers and development system with smart agents and associated real-time profiling, recursive profiles, and long-term profiles. The trainable general payment fraud models are trained into channel specialists with channel-filtered supervised and unsupervised data to produce each channels payment fraud model. This then is applied by a commercial client to process real-time cross-channel transactions and authorization requests for fraud scores. A detection of fraud in one channel is used to immediately sensitize all the other fraud channel models to the involved accountholder. Low level, but broad spectrum fraud can be used to trigger all the accounts of a compromised accountholder or merchant data breach.

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