AAAI-97 Workshop
AI Approaches to Fraud Detection and Risk Management


This workshop was held on Sunday, July 27 in conjunction with the AAAI-97 conference in Providence, Rhode Island.

Description

Fraud detection and risk management involve monitoring the behavior of populations of users in order to estimate, detect or avoid undesirable behavior. Undesirable behavior is a broad term including delinquency, fraud, intrusion and account defaulting. This workshop brought together researchers in these areas to discuss approaches and experiences in dealing with the critical issues:

Topics

Papers on the following areas were invited:

Credit/calling card fraud Computer/network intrusion
Internet transaction fraud Insurance fraud
Cellular fraud Insider trading
Credit rating/approval Prediction of delinquency/bad debt
Machine learning Neural networks
Probabilistic modeling Decision Theory
Genetic algorithms Knowledge discovery and data mining
Knowledge-based systems Statistical approaches

Accepted papers

Schedule

Working Notes

The working notes are available as a technical report from AAAI. Ordering information is here.

Organizing Committee

Tom Fawcett
NYNEX Science and Technology
(Now at HP Labs)
tom_fawcett@hp.com

Ira Haimowitz
GE Corporate Research and Development
haimowitz@crd.ge.com

Foster Provost
NYNEX Science and Technology
foster@nynexst.com

Sal Stolfo
Columbia University
stolfo@cs.columbia.edu

Other Sources of Information


Tom Fawcett / tom_fawcett@hp.com
Modified: 26-Jun-2001