Application of Outlier Detection Methods in Audit Analytics

We express our sincere gratitude to Phillip McCollough for his numerous critical contributions to this research. The author names are listed in alphabetical order by last name. Hanchi Gu is supported by the MOE Project of Key Research Institute of Humanities and Social Science in Universities [No. 22JJD790093], the 111 Project [B18033], and the Fundamental Research Funds for the Central Universities. The other authors of this publication have no conflicts of interest related to this research.

The opinions expressed are those of the authors and do not represent the opinions or positions of AICPA, Baker Tilly U.S., LLP, and our industry partners.

Kevin Fulcer, Baker Tilly U.S., LLP, Assurance Operations, Madison, WI, USA; Hanchi Gu, Shanghai University of Finance and Economics, Institute of Accounting and Finance, School of Accountancy, Shanghai, China; Hanxin Hu, Kean University, College of Business and Public Management, Department of Accounting and Finance, Union, NJ, USA; Qing Huang, Marshall University, Brad D. Smith Schools of Business, Department of Marketing, Management Information Systems, & Entrepreneurship, Huntington, WV, USA; Alexander Kogan and Miklos A. Vasarhelyi, Rutgers, The State University of New Jersey, Rutgers Business School, Accounting & Information Systems, Newark, NJ, USA; Danyang Wei, Durham University, Business School, Department of Accounting, Durham, United Kingdom; Jimmy Young, AICPA, Accounting and Auditing Innovation Department, St. Louis, MO, USA.

Editor’s note: Accepted by Vernon J. Richardson, under the Senior Editorship of D. Scott Showalter.