Audit Anomaly Detection based on Unsupervised Ensemble Learning

Abstract

This paper discusses the task of anomaly detection and localization from audit data. In auditing, anomaly detection is required in many situations and there exists a strong need for a method that automates the anomaly detection process. However, it is not trivial how to construct audit anomaly detection method due to three difficulties: (1) the method should be unsupervised as it is difficult to manually assign labels to large amounts of data; (2) it is required to conduct anomaly detection and localization simultaneously; (3) an audit data includes both categorical and numerical variables and they correlate strongly. We propose an audit anomaly detection method which solves the above-mentioned difficulties. The key ideas of the method are: (1) we decompose the anomaly detection problem into multiple scenarios, which consist of a few variables, and each scenario corresponds to localization; (2) we unify the localization scenarios by unsupervised ensemble learning which we propose here. We demonstrate effectiveness of the proposed method through the experimental results using anonymized audit data.