Data mining journal entries for fraud detection: A replication of Debreceny and Gray's (2010) techniques

• Main Authors: SEOW, Poh Sun, PAN, Gary, SUWARDY, Themin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: fraud; journal entries; data mining; digital analysis; Benford’s Law; Accounting; Corporate Finance
• Online Access: https://ink.library.smu.edu.sg/soa_research/1515; https://ink.library.smu.edu.sg/context/soa_research/article/2542/viewcontent/1952036.pdf
• Institution: Singapore Management University

The alarming frequency of fraud occurrences suggests that corporations continue to face persistent threat of fraud (Cecchini et al., 2010a; Summers and Sweeney, 1998). According to Association of Certified Fraud Examiner (ACFE)’s 2014 Report, a typical organization may lose five percent of its revenue to fraud every year. As such, the consequences of fraud may impact the shareholders, creditors, auditors and the public’s confidence in the integrity of corporations’ financial systems (Rezaee, 2005).