Adaptive Fraud Detection

Adaptive Fraud Detection is a dynamic fraud detection framework that continuously evolves its analytical models and decision rules in response to emerging fraud patterns, new transaction behaviors, and feedback signals, thereby distinguishing itself from static or rule-based systems through its capacity for real-time learning and system-wide adaptation.

48

Publications

2.2K

Citations

150

Authors

64

Institutions

Publications per year

2017–2026

32

Authors

150

  1. SA
    Stephen Adams

    University of Virginia, United States

    2 publications · 88 citations

    2

    h-index

  2. AS
    Ajeet Singh

    Guru Gobind Singh Indraprastha University, India

    2 publications · 44 citations

    2

    h-index

  3. PA
    Peter A. Beling

    University of Virginia, United States

    2 publications · 88 citations

    2

    h-index

  4. FA
    Farzana Anowar

    University of Regina, Canada

    1 publications · 28 citations

    1

    h-index

  5. MS
    M. Stephen Weatherford

    University of California, Santa Barbara, United States

    1 publications · 46 citations

    1

    h-index

1–5 of 150

Venues

  1. IEEE Access

    4 publications · 429 citations

    3

    h-index

  2. Computational Intelligence

    2 publications · 105 citations

    2

    h-index

  3. Expert Systems with Applications

    2 publications · 128 citations

    2

    h-index

  4. 1

    h-index

  5. Information Processing & Management

    1 publications · 32 citations

    1

    h-index

1–5