  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.
