Papers

• A Multi-Agent Systems Approach for Fraud Detection in Personal Communication Systems
  Suhayya Abu-Hakima, Mansour Toloo and Tony White (National Research Council of Canada)

• Detecting Cellular Fraud Using Adaptive Prototypes
  Peter Burge and John Shawe-Taylor (Royal Holloway University of London)

• Combining Data Mining and Machine Learning for Effective Fraud Detection
  Tom Fawcett and Foster Provost (NYNEX Science and Technology)

• Risk and Fraud in the Insurance Industry
  Barry Glasgow (Metropolitan Life Insurance Co.)

• Break Detection Systems
  Henry G. Goldberg and Ted E. Senator (NASD Regulation, Inc.)

• Clustering and Prediction for Credit Line Optimization
  Ira J. Haimowitz and Henry Schwarz (GE Corporate R&D)

• Prospective Assessment of AI Technologies for Fraud Detection: A Case Study
  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
  Harald Kauderer and Gholamreza Nakhaeizadeh (Daimler-Benz AG)

• Sequence Matching and Learning in Anomaly Detection for Computer Security
  Terran Lane and Carla E. Brodley (Purdue University)

• Learning Patterns from Unix Process Execution Traces for Intrusion Detection
  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
  Foster Provost and Tom Fawcett (NYNEX Science and Technology)

• Neuro-Fuzzy Approaches to Decision Making: An Application to Check Authorization from Incomplete Information
  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
  Jake Ryan, Meng-Jang Lin and Risto Miikkulainen (University of Texas at Austin)

• Risk Management in the Financial Services Industry: Through a Statistical Lens
  Til Schuermann (Oliver, Wyman & Company)

• Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results
  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
  Salvatore Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee, David W. Fan (Columbia University) and Philip K. Chan (Florida Institute of Technology)