Fraud Pattern Mining

Fraud Pattern Mining is a data analytics methodology that identifies distinctive and statistically significant behavioral patterns indicative of fraudulent activities within transactional or operational datasets by applying computational techniques to distinguish anomalous, fraud-related sequences from legitimate ones.

292

Publications

21.1K

Citations

837

Authors

364

Institutions

Publications per year

2017–2026

151

Authors

837

  1. CJ
    Changjun Jiang

    Tongji University, China

    5 publications · 214 citations

    5

    h-index

  2. GL
    Guanjun Liu

    Tongji University, China

    5 publications · 214 citations

    5

    h-index

  3. CY
    Chungang Yan

    Tongji University, China

    4 publications · 202 citations

    4

    h-index

  4. OC
    Olivier Caelen

    Université Libre de Bruxelles, Belgium

    4 publications · 822 citations

    4

    h-index

  5. ED
    Ekrem Duman

    Özyeğin University, Türkiye

    4 publications · 816 citations

    4

    h-index

1–5 of 837

Venues

  1. Expert Systems with Applications

    16 publications · 3.3K citations

    16

    h-index

  2. Decision Support Systems

    8 publications · 3.4K citations

    8

    h-index

  3. IEEE Access

    7 publications · 287 citations

    7

    h-index

  4. International Journal of Computer Applications

    5 publications · 164 citations

    5

    h-index

  5. Procedia Computer Science

    4 publications · 470 citations

    4

    h-index

1–5