AI Approaches to Fraud Detection and Risk Management
Papers from the AAAI Workshop
Tom Fawcett, Program Chair
Technical Report WS-97-07 published by The AAAI Press, Menlo Park, California
This technical report is also available in book and CD format.
Please Note: Abstracts are linked to individual titles, and will appear in a separate browser window. Full-text versions of the papers are linked to the abstract text. Access to full text may be restricted to AAAI members. PDF file sizes may be large!
Contents
Preface / 1
Tom Fawcett
A Multi-Agent Systems Approach for Fraud Detection in Personal Communication Systems / 1
Suhayya Abu-Hakima, Mansour Toloo and Tony White (National Research Council of Canada)
Detecting Cellular Fraud Using Adaptive Prototypes / 9
Peter Burge and John Shawe-Taylor (Royal Holloway University of London)
Combining Data Mining and Machine Learning for Effective Fraud Detection / 14
Tom Fawcett and Foster Provost (NYNEX Science and Technology)
Risk and Fraud in the Insurance Industry / 20
Barry Glasgow (Metropolitan Life Insurance Co.)
Break Detection Systems / 22
Henry G. Goldberg and Ted E. Senator (NASD Regulation, Inc.)
Clustering and Prediction for Credit Line Optimization / 29
Ira J. Haimowitz and Henry Schwarz (GE Corporate R&D)
Prospective Assessment of AI Technologies for Fraud Detection: A Case Study / 34
David Jensen (University of Massachusetts at Amherst)
The Effect of Alternate Scaling Approaches on the Performance of Different Supervised Learning Algorithms. An Empirical Study in the Case of Credit Scoring / 39
Harald Kauderer and Gholamreza Nakhaeizadeh (Daimler-Benz AG)
Sequence Matching and Learning in Anomaly Detection for Computer Security / 43
Terran Lane and Carla E. Brodley (Purdue University)
Learning Patterns from Unix Process Execution Traces for Intrusion Detection / 50
Wenke Lee, Salvatore J. Stolfo (Columbia University) and Philip K. Chan (Florida Institute of Technology)
Analysis and Visualization of Classifier Performance with Nonuniform Class and Cost Distributions / 57
Foster Provost and Tom Fawcett (NYNEX Science and Technology)
Neuro-Fuzzy Approaches to Decision Making: An Application to Check Authorization from Incomplete Information / 64
V. K. Ramani (GE Corporate R & D), J. R. Echuaz (University of Puerto Rico), G. J. Vachtsevanos and S. S. Kim (Georgia Institute of Technology)
Intrusion Detection with Neural Networks / 72
Jake Ryan, Meng-Jang Lin and Risto Miikkulainen (University of Texas at Austin)
Risk Management in the Financial Services Industry: Through a Statistical Lens / 78
Til Schuermann (Oliver, Wyman & Company)
Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results / 83
Salvatore J. Stolfo, David W. Fan, Wenke Lee, Andreas L. Prodromidis (Columbia University) and Philip K. Chan (Florida Institute of Technology)
JAM: Java Agents for Meta-Learning over Distributed Databases / 9
Salvatore Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee, David W. Fan (Columbia University) and Philip K. Chan (Florida Institute of Technology)
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