Data Mining In Auditing

Data Mining in Auditing is a systematic analytical technique that applies computational algorithms to extract meaningful, previously unknown, and actionable patterns from large-scale financial and operational datasets to support continuous audit decision-making and anomaly detection.

170

Publications

12.2K

Citations

357

Authors

197

Institutions

Publications per year

2017–2026

85

Authors

357

  1. MA
    Miklos A. Vasarhelyi

    Rutgers, The State University of New Jersey, United States

    12 publications · 1.8K citations

    11

    h-index

  2. MJ
    Mieke Jans

    Hasselt University, Belgium

    9 publications · 567 citations

    9

    h-index

  3. WL
    Wenke Lee

    Georgia Institute of Technology, United States

    7 publications · 2.5K citations

    7

    h-index

  4. MA
    Michael Alles

    Rutgers, The State University of New Jersey, United States

    6 publications · 583 citations

    6

    h-index

  5. SJ
    Salvatore J. Stolfo

    Columbia University, United States

    5 publications · 2.4K citations

    5

    h-index

1–5 of 357

Venues

  1. 11

    h-index

  2. Journal of Emerging Technologies in Accounting

    7 publications · 306 citations

    7

    h-index

  3. SSRN Electronic Journal

    Repository · 7 publications · 181 citations

    7

    h-index

  4. Expert Systems with Applications

    5 publications · 156 citations

    5

    h-index

  5. 4

    h-index

1–5