Predictive Fraud Modeling

Predictive Fraud Modeling is a computational method that belongs to the class of fraud detection techniques and is distinguished by its reliance on statistical and machine learning algorithms to identify the likelihood of fraudulent activities before they occur by analyzing historical and real-time data patterns.

221

Publications

15.9K

Citations

695

Authors

307

Institutions

Publications per year

2017–2026

150

Authors

695

  1. ML
    Mark Lokanan

    Royal Roads University, Canada

    4 publications · 157 citations

    4

    h-index

  2. CY
    Chun Yan

    Shandong University of Science and Technology, China

    3 publications · 202 citations

    3

    h-index

  3. ED
    Ekrem Duman

    Özyeğin University, Türkiye

    3 publications · 521 citations

    3

    h-index

  4. ML
    Maozhen Li

    Brunel University of London, United Kingdom

    3 publications · 202 citations

    3

    h-index

  5. YL
    Yaqi Li

    Shandong University of Science and Technology, China

    3 publications · 202 citations

    3

    h-index

1–5 of 695

Venues

  1. Expert Systems with Applications

    10 publications · 681 citations

    10

    h-index

  2. Procedia Computer Science

    6 publications · 809 citations

    6

    h-index

  3. Decision Support Systems

    5 publications · 2.7K citations

    5

    h-index

  4. arXiv (Cornell University)

    Repository · 4 publications · 221 citations

    4

    h-index

  5. IEEE Access

    4 publications · 148 citations

    4

    h-index

1–5