Predictive Fraud Analytics

Predictive Fraud Analytics is a data-driven methodological framework within health care fraud detection that employs statistical modeling and machine learning techniques to identify anomalous patterns and forecast potential Medicare fraud prior to human investigation.

46

Publications

2K

Citations

138

Authors

68

Institutions

Publications per year

2017–2026

33

Authors

138

  1. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    4 publications · 229 citations

    5

    h-index

  2. JH
    John Hancock

    Florida Atlantic University, United States

    1 publications · 53 citations

    2

    h-index

  3. JV
    Jos van Hillegersberg

    University of Twente, Netherlands

    2 publications · 97 citations

    2

    h-index

  4. DT
    Dallas Thornton

    University of Twente, Netherlands

    2 publications · 97 citations

    2

    h-index

  5. RM
    Roland M. Mueller

    Berlin School of Economics and Law, Germany

    2 publications · 97 citations

    2

    h-index

1–5 of 138

Venues

  1. SN Computer Science

    1 publications · 56 citations

    2

    h-index

  2. Expert Systems with Applications

    2 publications · 56 citations

    2

    h-index

  3. Wireless Personal Communications

    1 publications · 32 citations

    1

    h-index

  4. Health Care Management Science

    HCMS · 1 publications · 169 citations

    1

    h-index

  5. Soft Computing

    1 publications · 25 citations

    1

    h-index

1–5