Audit Analytics in the Financial Industry

Jun Dai is an Assistant Professor at Southwestern University of Finance and Economics, China. She received her Ph.D. from Rutgers Business School in 2017, and she researches accounting information systems and continuous auditing. Miklos A. Vasarhelyi is the KPMG Distinguished Professor of Accounting Information Systems at Rutgers University, and serves as Director of the Rutgers Accounting Research Center (RARC) and Continuous Auditing & Reporting Lab (CAR Lab). Ann F. Medinets is an Associate Professor of Professional Practice at Rutgers University. Her paper "Say-on-Pay: Is Anybody Listening?" with Stephani Mason and Dan Palmon in the Multinational Finance Journal was featured on Harvard's Forum on Corporate Governance and Financial Regulation, and is among the top 10% of SSRN downloads.

In light of the accounting scandals and corporate failures since 2001, accounting researchers say, auditors are turning to the emerging techniques of audit analytics to extract and process data from a variety of sources in order to identify risks, collect evidence, and ultimately support decisions. They illustrate the process of applying audit analytics to solving audit problems in the financial industry, present case studies of discovering fraud risks and potential frauds in the credit card sector, demonstrate clustering techniques in the insurance sector and applying audit analytics to detect fraud/anomaly in the transitory system, and discuss assessing risk in the lawsuit and payment processes. Distributed in North America by Turpin Distribution.

• Introduction: What is Audit Analytics?; Jun Dai, Miklos A. Vasarhelyi, and Ann F. Medinets Part One: Audit Analytics Procedures 1. An Application of Exploratory Data Analysis in Auditing: Credit Card Retention Case; Qi Lui 2. Audit Analytics: A Field Study of Credit Card After-Sale Service Problem Detection at a Major Bank; Jun Dai, Paul Byrnes, Qi Liu, and Miklos A. Vasarhelyi Part Two: Analytics in Credit Card Audits 3. Automated Clustering: From Concept to Reality; Paul Byrnes 4. A Multi-Faceted Outlier Detection Scheme for Use in Clustering; Paul Byrnes 5. Are Customers Offered Appropriate Discounts? An Exploratory Study of Using Clustering Techniques in Internal Auditing; Jun Dai, Paul Byrnes, and Miklos A. Vasarhelyi 6. Predicting Credit Card Delinquency: An Application of the Decision Tree Technique; Ting Sun and Miklos A. Vasarhelyi Part Three: Analytics in Insurance Audits 7. Cluster Analysis for Anomaly Detection in Accounting; Sutapat Thiprungsri 8. Multi-Dimensional Approaches to Anomaly Detection: A Study of Insurance Claims; Basma Moharram Part Four: Audit Analytics in Transitory Systems 9. Development of an Anomaly Detection Model for a Bank's Transitory Account System; Yongbum Kim 10. Development of an Anomaly Detection Model for an Insurance Company's Wire Transfer System; Yongbum Kim Part Five: Audit Analytics for Lawsuit Risk Detection 11. A Legal Risk Prediction Model for Credit Cards; Feiqi Huang, Qi Liu, and Miklos A. Vasarhelyi Part Six: Audit Analytics in the Payment Process 12. Analyzing Payment Data and Its Process: A Bank Case; Karine Chandia and Miklos A. Vasarhelyi