Applicant Misrepresentation Detection

Applicant Misrepresentation Detection is a methodological framework within background screening that identifies deliberate falsifications or omissions in an applicant's credentials or statements through systematic validation and anomaly detection.

19

Publications

1.1K

Citations

72

Authors

48

Institutions

Publications per year

2017–2026

2

Authors

72

  1. MN
    Michael N. Wiggins

    University of Arkansas for Medical Sciences, United States

    2 publications · 45 citations

    2

    h-index

  2. RG
    Richard Griffith

    Swansea University, United Kingdom

    2 publications · 132 citations

    2

    h-index

  3. MH
    Mitchell H. Peterson

    Florida Institute of Technology, United States

    2 publications · 132 citations

    2

    h-index

  4. SM
    Scott M. Wright

    Johns Hopkins University, United States

    1 publications · 36 citations

    1

    h-index

  5. GC
    Grace Chang

    Harvard University, United States

    1 publications · 81 citations

    1

    h-index

1–5 of 72

Venues

  1. Archives of Ophthalmology

    1 publications · 29 citations

    1

    h-index

  2. Annals of Internal Medicine

    1 publications · 36 citations

    1

    h-index

  3. Urology

    1 publications · 25 citations

    1

    h-index

  4. American Journal of Roentgenology

    AJR · 1 publications · 43 citations

    1

    h-index

  5. Internet Interventions

    1 publications · 98 citations

    1

    h-index

1–5