Accounting fraud is a significant threat to the financial system stability, as it can result in massive corporate collapses and diminish market confidence and trust in regulatory authorities. This study aims to identify signs of accounting fraud occurrence to be used to identify companies that are more likely to be manipulating financial statement reports and assist the task of examination within the riskier firms. A thorough forensic data analytic approach is proposed that includes all pertinent steps of a data-driven methodology. The approach includes financial ratio analysis, logistic regression models, and machine learning models to detect accounting fraud. The results suggest that this approach has great potential for detecting falsified accounting records and that machine learning models are particularly useful for detecting fraud due to their accuracy, interpretability, and cost-efficiency.