Fighting Accounting Fraud Through Forensic Data Analytics

TL;DR

This paper explores the use of machine learning techniques to improve the detection of accounting fraud by analyzing financial data, aiming to assist auditors and regulators in identifying fraudulent activities more effectively.

Contribution

It introduces a machine learning-based methodology for detecting accounting fraud and evaluating financial indicators to differentiate fraudulent from legitimate companies.

Findings

Machine learning models show high potential in fraud detection.

Statistical analysis of financial data can identify suspicious patterns.

Methodology supports targeted audits and regulatory oversight.

Abstract

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.