Cost-sensitive Fraud Detection

Cost-Sensitive Fraud Detection is a fraud detection approach that belongs to the class of classification methods in machine learning and is specifically distinguished by its explicit integration of asymmetric cost structures to minimize the total expected cost of misclassification, particularly in the underwriting context where false negatives and false positives entail unequal financial consequences.

38

Publications

2.4K

Citations

94

Authors

44

Institutions

Publications per year

2017–2026

22

Authors

94

  1. ED
    Ekrem Duman

    Özyeğin University, Türkiye

    3 publications · 441 citations

    3

    h-index

  2. PV
    Patrick Vorst

    Maastricht University, Netherlands

    2 publications · 83 citations

    2

    h-index

  3. MD
    Messod D. Beneish

    Indiana University, United States

    2 publications · 83 citations

    2

    h-index

  4. BO
    Björn Ottersten

    University of Luxembourg, Luxembourg

    2 publications · 188 citations

    2

    h-index

  5. AC
    Alejandro Correa Bahnsen

    University of Luxembourg, Luxembourg

    2 publications · 188 citations

    2

    h-index

1–5 of 94

Venues

  1. Expert Systems with Applications

    3 publications · 650 citations

    3

    h-index

  2. DOAJ (DOAJ: Directory of Open Access Journals)

    Repository · 2 publications · 40 citations

    2

    h-index

  3. Decision Sciences

    1 publications · 76 citations

    1

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 22 citations

    1

    h-index

  5. Scientific African

    1 publications · 54 citations

    1

    h-index

1–5