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Agent-Based Distributed Learning Applied to Fraud Detection

Andreas L. Prodromidis; Salvatore Stolfo

Title:
Agent-Based Distributed Learning Applied to Fraud Detection
Author(s):
Prodromidis, Andreas L.
Stolfo, Salvatore
Date:
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-014-99
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
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.
Subject(s):
Computer science
Item views:
243
Metadata:
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