Explainable Detection Models

Explainable Detection Models is a detection framework in the domain of money laundering detection that belongs to the class of machine learning models designed for anomaly identification, distinguished by its intrinsic capacity to provide transparent, interpretable, and human-comprehensible justifications for its detection decisions.

56

Publications

5.3K

Citations

243

Authors

116

Institutions

Publications per year

2017–2026

50

Authors

243

  1. BV
    Bertie Vidgen

    University of Oxford, United Kingdom

    2 publications · 92 citations

    2

    h-index

  2. WY
    Wenjie Yin

    China Academy of Space Technology, China

    2 publications · 92 citations

    2

    h-index

  3. HR
    Hannah Rose Kirk

    University of Oxford, United Kingdom

    2 publications · 92 citations

    2

    h-index

  4. RK
    Rafał Kozik

    Bydgoszcz University of Science and Technology, Poland

    2 publications · 156 citations

    2

    h-index

  5. MP
    Marek Pawlicki

    Bydgoszcz University of Science and Technology, Poland

    2 publications · 156 citations

    2

    h-index

1–5 of 243

Venues

  1. IEEE Access

    5 publications · 546 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 103 citations

    3

    h-index

  3. Expert Systems with Applications

    3 publications · 155 citations

    2

    h-index

  4. Applied Sciences

    2 publications · 54 citations

    2

    h-index

  5. Scientific Reports

    2 publications · 412 citations

    2

    h-index

1–5