Predictive Analytics For Fraud

Predictive Analytics for Fraud is a methodological framework within fraud risk assessment that employs statistical and machine learning techniques to identify patterns and forecast the likelihood of fraudulent activities based on historical and real-time data.

160

Publications

11.6K

Citations

408

Authors

202

Institutions

Publications per year

2017–2026

109

Authors

408

  1. ML
    Mark Lokanan

    Royal Roads University, Canada

    4 publications · 157 citations

    4

    h-index

  2. DC
    Dennis Collaris

    Eindhoven University of Technology, Netherlands

    2 publications · 22 citations

    2

    h-index

  3. WM
    Wei Min

    ETH Zurich, Switzerland

    2 publications · 60 citations

    2

    h-index

  4. JZ
    Jun Zhou

    Zhejiang Financial College, China

    2 publications · 72 citations

    2

    h-index

  5. JR
    Jürgen Repp

    Fraunhofer Institute for Secure Information Technology, Germany

    2 publications · 81 citations

    2

    h-index

1–5 of 408

Venues

  1. SSRN Electronic Journal

    Repository · 4 publications · 159 citations

    8

    h-index

  2. Expert Systems with Applications

    5 publications · 306 citations

    5

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 221 citations

    4

    h-index

  4. Decision Support Systems

    3 publications · 1.9K citations

    3

    h-index

  5. The Accounting Review

    2 publications · 278 citations

    3

    h-index

1–5