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Seow, P-S, Pan, G and Suwardy, T (2016)

Data Mining Journal Entries for Fraud Detection: A Replication of Debreceny and Gray's (2010) Techniques

Journal of Forensic and Investigative Accounting 8(3), pp. 501-514.

ISSN/ISBN: Not available at this time. DOI: Not available at this time.



Abstract: There is limited published research to detect financial statement fraud using digital analysis to analyse journal entry data. As far as we are aware, Debreceny and Grayís (2010) study is the first and only such study. In this study, we replicated and extended Debreceny and Grayís (2010) work by examining generalizability of their techniques beyond subjects from USA. Besides Chi-Square test, we also explored the use of mean absolute deviation method during digital analysis. We found Debreceny and Grayís (2010) techniques useful in facilitating cross-sectional analysis for journal entry data sets that are based on multiple organizations. Our results confirmed that their techniques offered a comprehensive and systematic way of applying digital analysis on journal entries in a new setting. Our analysis also found that researchers should not rely solely on Benfordís Law during digital analysis because of potential false alarms.


Bibtex:
@article {, AUTHOR = {Show, Poh-Sun and Pan, Gary and Suwardy, Themin}, TITLE = {Data Mining Journal Entries for Fraud Detection: A Replication of Debreceny and Gray's (2010) Techniques}, JOURNAL = {Journal of Forensic and Investigative Accounting}, YEAR = {2016}, VOLUME = {8}, NUMBER = {3}, PAGES = {501--514}, URL = { http://ink.library.smu.edu.sg/soa_research/1515}, }


Reference Type: Journal Article

Subject Area(s): Accounting