Forensic analytics using cluster analysis: Detecting anomalies in data. Issue 2 (15th January 2021)

• Record Type: Journal Article
• Authors: Goh, Clarence; Lee, Benjamin; Pan, Gary; Seow, Poh Sun
• Is Part Of: Journal of corporate accounting & finance. Volume 32:Issue 2(2021)
• Page Start: 154
• Page End: 161
• Publication Date: 2021-01-15
• Subjects: forensic accounting -- data analytics -- clustering -- tableau; Accounting -- Periodicals; Accounting -- Law and legislation -- United States -- Periodicals; Corporations -- United States -- Finance -- Periodicals; Corporations -- United States -- Accounting -- Periodicals; 657
• DOI: 10.1002/jcaf.22486
• Languages: English
• ISSNs: 1044-8136

Abstract

Abstract: Cluster analysis is a data analytics technique that can help forensic accountants effectively detect anomalies in complex financial datasets. This article provides a description of clustering analysis, discusses how it can be implemented to detect anomalies in data, and illustrates its use through a worked example using the Tableau software.