• Skip to main content
  • Skip to primary sidebar
AAAI

AAAI

Association for the Advancement of Artificial Intelligence

    • AAAI

      AAAI

      Association for the Advancement of Artificial Intelligence

  • About AAAIAbout AAAI
    • News
    • Officers and Committees
    • Staff
    • Bylaws
    • Awards
      • Fellows Program
      • Classic Paper Award
      • Dissertation Award
      • Distinguished Service Award
      • Allen Newell Award
      • Outstanding Paper Award
      • AI for Humanity Award
      • Feigenbaum Prize
      • Patrick Henry Winston Outstanding Educator Award
      • Engelmore Award
      • AAAI ISEF Awards
      • Senior Member Status
      • Conference Awards
    • Partnerships
    • Resources
    • Mailing Lists
    • Past Presidential Addresses
    • AAAI 2025 Presidential Panel on the Future of AI Research
    • Presidential Panel on Long-Term AI Futures
    • Past Policy Reports
      • The Role of Intelligent Systems in the National Information Infrastructure (1995)
      • A Report to ARPA on Twenty-First Century Intelligent Systems (1994)
    • Logos
  • aaai-icon_ethics-diversity-line-yellowEthics & Diversity
  • Conference talk bubbleConferences & Symposia
    • AAAI Conference
    • AIES AAAI/ACM
    • AIIDE
    • EAAI
    • HCOMP
    • IAAI
    • ICWSM
    • Spring Symposia
    • Summer Symposia
    • Fall Symposia
    • Code of Conduct for Conferences and Events
  • PublicationsPublications
    • AI Magazine
    • Conference Proceedings
    • AAAI Publication Policies & Guidelines
    • Request to Reproduce Copyrighted Materials
    • Contribute
    • Order Proceedings
  • aaai-icon_ai-magazine-line-yellowAI Magazine
  • MembershipMembership
    • Member Login
    • Chapters

  • Career CenterAI Jobs
  • aaai-icon_ai-topics-line-yellowAITopics
  • aaai-icon_contact-line-yellowContact

  • Twitter
  • Facebook
  • LinkedIn
Home / Proceedings / AAAI Workshop Papers 1997 /

AI Approaches to Fraud Detection and Risk Management: Papers from the 1997 AAAI Workshop

Contents

  • 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, Gholamreza Nakhaeizadeh

    39

    PDF
  • JAM: Java Agents for Meta-Learning over Distributed Databases

    Salvatore Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee, David W. Fan, and Philip K. Chan

    91

    PDF
  • Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results

    Salvatore J. Stolfo, David W. Fan, Wenke Lee, Andreas L. Prodromidis, and Philip K. Chan

    83

    PDF
  • Risk Management in the Financial Services Industry: Through a Statistical Lens

    Til Schuermann

    78

    PDF
  • Intrusion Detection with Neural Networks

    Jake Ryan, Meng-Jang Lin, Risto Miikkulainen

    72

    PDF
  • Neuro-Fuzzy Approaches to Decision Making: An Application to Check Authorization from Incomplete Informatio

    V. K. Ramani, J. R. Echuaz, G. J. Vachtsevanos, and S. S. Kim

    64

    PDF
  • Analysis and Visualization of Classifier Performance with Nonuniform Class and Cost Distributions

    Foster Provost, Tom Fawcett

    57

    PDF
  • Learning Patterns from Unix Process Execution Traces for Intrusion Detection

    Wenke Lee, Salvatore J. Stolfo, and Philip K. Chan

    50

    PDF
  • Sequence Matching and Learning in Anomaly Detection for Computer Security

    Terran Lane, Carla E. Brodley

    43

    PDF
  • Preface

    Tom Fawcett

    1

    PDF
  • Prospective Assessment of AI Technologies for Fraud Detection: A Case Study

    David Jensen

    34

    PDF
  • Clustering and Prediction for Credit Line Optimization

    Ira J. Haimowitz, Henry Schwarz

    29

    PDF
  • Break Detection Systems

    Henry G. Goldberg, Ted E. Senator

    22

    PDF
  • Risk and Fraud in the Insurance Industry

    Barry Glasgow

    20

    PDF
  • Combining Data Mining and Machine Learning for Effective Fraud Detection

    Tom Fawcett, Foster Provost

    14

    PDF
  • Detecting Cellular Fraud Using Adaptive Prototypes

    Peter Burge, John Shawe-Taylor

    9

    PDF
  • A Multi-Agent Systems Approach for Fraud Detection in Personal Communication Systems

    Suhayya Abu-Hakima, Mansour Toloo, Tony White

    1

    PDF

Primary Sidebar