Machine Learning-Based Detection of Financial Statement Fraud: Integrating Beneish Ratios, Linguistics, and Stock Volatility on the Indonesia Stock Exchange

Abstract

This research aimed to develop and evaluate a financial statement fraud (FSF) detection model for companies listed on the Indonesia Stock Exchange (IDX) during the 2024–2025 period using a machine learning approach. An ablation study design was employed to test 27 model combinations across three feature scenarios: Beneish M-Score financial ratios, linguistic features extracted from Management Discussion and Analysis (MD&A) texts using the InSet lexicon, and 30-day stock price volatility. Nine classification algorithms were evaluated using precision, recall, and F1-score metrics. The best-performing model combined financial ratios and linguistic features using Gradient Boosting, achieving an F1-score of 0.545. In contrast, the addition of stock price volatility as a feature did not improve model performance and instead reduced the classification ability of all tested algorithms. These findings indicate that the Indonesian capital market may have limited ability to anticipate indications of financial statement fraud before related information is publicly disclosed. This study concludes that integrating accounting and linguistic information is more effective than relying solely on financial ratios or incorporating market data. These findings contribute to the development of machine learning-based FSF detection literature in Indonesia and provide an alternative approach for auditors, investors, and regulators to enhance the effectiveness of early financial statement fraud detection.