Abstract
External auditors and management increasingly rely on control risk assessments conducted by internal auditors. Consequently, it is crucial to ensure the quality of such assessments and identify irregular instances that deviate from the normal pattern of assessments. Moreover, processing and prioritizing a large number of outlying internal auditors' assessments can help their superiors as well as external auditors overcome the human limitations of dealing with information overload and direct their investigations toward the more suspicious cases, consequently improving overall audit efficiency. In this paper, we use historic data consisting of control risk assessments procured from the internal audit department of a multinational consumer products company. It is used to infer an ordered logistic regression model to provide a quality review of internal auditors' and business owners' assessments of internal controls. We identify anomalous cases where the assessment does not conform to the expected value and develop a methodology to prioritize these outliers. The results indicate that the proposed model can serve as a quality review tool, thus improving audit efficiency, as well as a learning tool that non-experts can employ to gain expert-like knowledge. Additionally, the proposed ranking metrics proved effective in helping the auditors focus their efforts on the more problematic audits.