Statistical Fraud Detection

Statistical Fraud Detection is a fraud detection method that employs statistical models and quantitative techniques to identify anomalous patterns and deviations from expected behavior in claims data, distinguishing it from rule-based or heuristic approaches by its reliance on probabilistic inference and data-driven hypothesis testing.

62

Publications

3.8K

Citations

167

Authors

90

Institutions

Publications per year

2017–2026

21

Authors

167

  1. LB
    Lucio Barabesi

    University of Siena, Italy

    4 publications · 138 citations

    4

    h-index

  2. AC
    Andrea Cerioli

    University of Parma, Italy

    4 publications · 138 citations

    4

    h-index

  3. DP
    Domenico Perrotta

    University of Parma, Italy

    4 publications · 138 citations

    4

    h-index

  4. AD
    Andreas Diekmann

    ETH Zurich, Switzerland

    3 publications · 273 citations

    3

    h-index

  5. AC
    Andrea Cerasa

    Joint Research Centre, Italy

    3 publications · 127 citations

    3

    h-index

1–5 of 167

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 167 citations

    3

    h-index

  2. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 209 citations

    2

    h-index

  3. Pharmaceutical Statistics

    2 publications · 36 citations

    2

    h-index

  4. German Economic Review

    GER · 2 publications · 191 citations

    2

    h-index

  5. Science

    2 publications · 25 citations

    2

    h-index

1–5