Fighting Accounting Fraud Through Forensic Data Analytics

39 Pages Posted: 22 May 2018

See all articles by Maria Jofre

Maria Jofre

University of Sydney Business School

Richard H. Gerlach

University of Sydney

Date Written: April 30, 2018


Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to improve the detection of accounting fraud via the implementation of several machine learning methods to better differentiate between fraud and non-fraud companies, and to further assist the task of examination within the riskier firms by evaluating relevant financial indicators. Out-of-sample results suggest there is a great potential in detecting falsified financial statements through statistical modelling and analysis of publicly available accounting information. The proposed methodology can be of assistance to public auditors and regulatory agencies as it facilitates auditing processes, and supports more targeted and effective examinations of accounting reports.

Keywords: Forensic Accounting, Accounting Fraud, Machine Learning, Corporate Regulation

JEL Classification: C02, C12, C14, C38, C52, C53, H83, M40, M41, M42, M48

Suggested Citation

Jofre, Maria and Gerlach, Richard H., Fighting Accounting Fraud Through Forensic Data Analytics (April 30, 2018). Available at SSRN: or

Maria Jofre (Contact Author)

University of Sydney Business School ( email )

Cnr. of Codrington and Rose Streets
Sydney, NSW 2006

Richard H. Gerlach

University of Sydney ( email )

Room 483, Building H04
University of Sydney
Sydney, NSW 2006
+ 612 9351 3944 (Phone)
+ 612 9351 6409 (Fax)


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