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

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

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 reven...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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).

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