Outlier Detection in Auditing: Integrating Unsupervised Learning within a Multilevel Framework for General Ledger Analysis

Author:

Wei Danyang12ORCID,Cho Soohyun2ORCID,Vasarhelyi Miklos A.2ORCID,Te-Wierik Liam3

Affiliation:

1. Durham University

2. Rutgers, The State University of New Jersey

3. Grant Thornton Australia

Abstract

ABSTRACT Auditors traditionally use sampling techniques to examine general ledger (GL) data, which suffer from sampling risks. Hence, recent research proposes full-population testing techniques, such as suspicion scoring, which rely on auditors’ judgment to recognize possible risk factors and develop corresponding risk filters to identify abnormal transactions. Thus, when auditors miss potential problems, the related transactions are not likely to be identified. This paper uses unsupervised outlier detection methods, which require no prior knowledge about outliers in a dataset, to identify outliers in GL data and tests whether auditors can gain new insights from those identified outliers. A framework called the Multilevel Outlier Detection Framework (MODF) is proposed to identify outliers at the transaction level, account level, and combination-by-variable level. Experiments with one real and one synthetic GL dataset demonstrate that the MODF can help auditors to gain new insights about GL data. Data Availability: The real dataset used in the experiment is not publicly available due to privacy policies. JEL Classifications: M410, M42.

Publisher

American Accounting Association

Reference40 articles.

1. Alawadhi, A. 2015. The application of data visualization in auditing. Doctoral dissertation, Rutgers, The State University of New Jersey, Newark. https://doi.org/doi:10.7282/T3GQ70MD

2. A review of local outlier factor algorithms for outlier detection in Big Data streams;Alghushairy,;Big Data and Cognitive Computing,2020

3. American Institute of Certified Public Accountants (AICPA). 2017. Guide to Audit Data Analytics. Durham, NC: AICPA. https://www.aicpa-cima.com/cpe-learning/publication/guide-to-audit-data-analytics

4. Is all that talk just noise? The information content of internet stock message boards;Antweiler,;The Journal of Finance,2004

5. The effectiveness of alternative risk assessment and program planning tools in a fraud setting;Asare,;Contemporary Accounting Research,2004

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3