A Predictive Ordered Logistic Regression Model as a Tool for Quality Review of Control Risk Assessments

• In Event: Data Analytics

Abstract

External auditors, along with management, increasingly rely on control risk assessments conducted by internal auditors. Consequently, it is crucial to ensure the quality of such assessments and identify any irregular instances. Moreover, processing and prioritizing these outliers helps the auditors overcome the human limitations of dealing with information overload and direct their investigations towards the more suspicious cases, and consequently improve overall audit efficiency. In this field-study based paper, we examine and evaluate the quality of auditors’ judgment of business processes’ risk levels by applying an ordered logistic regression model to historic data procured from internal controls risk assessments of a multinational company. We identify anomalous cases where the auditor’s assessment does not conform to the value predicted by the model, and develop a methodology to prioritize these outliers. The results indicate that the model used in this study 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.

Authors

• Hussein Issa, Rutgers, The State University of New Jersey

• Alexander Kogan, Rutgers, The State University of New Jersey, Newark

• Miklos A Vasarhelyi, Rutgers, The State University of New Jersey, Newark