Unsupervised anomaly detection for internal auditing: Literature review and research agenda

Abstract

Auditing has to adapt to the growing amounts of data caused by digital transformation. One approach to address this and to test the full audit data population is to apply rules to the data. A disadvantage of this is that rules most likely only find errors, mistakes or deviations which were already anticipated by the auditor. Unsupervised anomaly detection can go beyond those capabilities and detect novel process deviations or new fraud attempts. We conducted a systematic review of existing studies which apply unsupervised anomaly detection in an auditing context. The results reveal that most of the studies develop an approach for only one specific dataset and do not address the integration into the audit process or how the results should be best presented to the auditor. We therefore develop a research agenda addressing both the generalizability of unsupervised anomaly detection in auditing and the preparation of results for auditors.