Audit Analytics: A Field Study of Credit Card After-Sale Service Problem Detection at a Major Bank

• In Event: Data Analytics

Abstract

Audit analytics is an emerging methodology that applies data analysis techniques to auditing. This approach offers several advantages when compared with traditional auditing methods, including scalability, capability of learning hidden patterns in non-linear datasets and dynamic datasets, and cost-effectiveness. In this research paper, an audit analytics protocol is initially developed for dealing with auditing issues. Subsequently, this protocol is used to detect real world credit card after-sale service problems at a major bank. This paper is the first to discuss the general protocol of audit analytics in addressing auditing issues, and our results show that the audit analytics protocol is an efficient way to identify audit relevant information that cannot be easily detected by internal auditors using traditional audit protocols.

Authors

• Jun Dai, Rutgers, The State University of New Jersey

• Paul Eric Byrnes, Rutgers, The State University of New Jersey

• Stephen Kozlowski, S.P. Kozlowski, CPA

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