Application of Outlier Detection Methods in Audit Analytics

Fulcer, Kevin; Gu, Hanchi; Hu, Hanxin; Huang, Qing; Kogan, Alexander; Vasarhelyi, Miklos; Wei, Danyang; Young, Jimmy

Abstract

Audit transaction anomalies can be viewed as outliers. Unsupervised learning methods of outlier detection do not require outcome labels and enable auditors to discover possible problems based on observed transaction patterns. This study develops a framework for using outlier detection methods in audit selection and evaluates the proposed framework on real-world revenue subledger datasets. The results indicate that the proposed framework could facilitate the identification of relevant outlier detection algorithms and effectively select risky observations.

Citation

Fulcer, K., Gu, H., Hu, H., Huang, Q., Kogan, A., Vasarhelyi, M., Wei, D., & Young, J. (online). Application of Outlier Detection Methods in Audit Analytics. Accounting Horizons, https://doi.org/10.2308/HORIZONS-2023-071

• Journal Article Type: Article
• Acceptance Date: Dec 4, 2024
• Online Publication Date: Dec 31, 2024
• Deposit Date: Dec 6, 2024
• Publicly Available Date: Jan 8, 2025
• Journal: Accounting Horizons
• Print ISSN: 0888-7993
• Electronic ISSN: 1558-7975
• Publisher: American Accounting Association
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.2308/HORIZONS-2023-071
• Public URL: https://durham-repository.worktribe.com/output/3202193