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Agent-Based Distributed Learning Applied to Fraud Detection

Prodromidis, Andreas L.; Stolfo, Salvatore

Inductive learning and classification techniques have been applied in many problems in diverse areas. In this paper we describe an AI-based approach that combines inductive learning algorithms and meta-learning methods as a means to compute accurate classification models for detecting electronic fraud. Inductive learning algorithms are used to compute detectors of anomalous or errant behavior over inherently distributed data sets and meta-learning methods integrate their collective knowledge into higher level classification models or "meta-classifiers". By supporting the exchange of models or "classifier agents" among data sites, our approach facilitates the cooperation between financial organizations and provides unified and cross-institution protection mechanisms against fraudulent transactions. Through experiments performed on actual credit card transaction data supplied by two different financial institutions, we evaluate this approach and we demonstrate its utility.

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More About This Work

Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-014-99
Published Here
April 21, 2011
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