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Accounting fraud is a global concern representing a significant threat to thefinancial system stability due to the resulting diminishing of the marketconfidence and trust of regulatory authorities. Several tricks can be used tocommit accounting fraud, hence the need for non-static regulatory interventionsthat take into account different fraudulent patterns. Accordingly, this studyaims to improve the detection of accounting fraud via the implementation ofseveral machine learning methods to better differentiate between fraud andnon-fraud companies, and to further assist the task of examination within theriskier firms by evaluating relevant financial indicators. Out-of-sampleresults suggest there is a great potential in detecting falsified financialstatements through statistical modelling and analysis of publicly availableaccounting information. The proposed methodology can be of assistance to publicauditors and regulatory agencies as it facilitates auditing processes, andsupports more targeted and effective examinations of accounting reports.

Fighting Accounting Fraud Through Forensic Data Analytics