AI-Driven Forensic Accounting: A Hybrid Fraud Detection Framework Integrating Blockchain, Machine Learning, and ESG Risk Indicators

Financial fraud has become increasingly sophisticated as corporations expand digital operations, adopt blockchain-based systems, and intensify ESG reporting practices. Traditional forensic accounting techniques—reliant on manual audits and ratio-based analysis—are often unable to detect complex, multimodal fraud patterns. This study develops an AI-driven hybrid forensic accounting framework that integrates financial statement ratios, blockchain transaction anomalies, and ESG governance and sentiment indicators to enhance fraud detection. Using a simulated but realistic multimodal dataset of 2,000 firms, five supervised machine learning models are evaluated, with XGBoost achieving the highest predictive performance (F1-score = 0.80; ROC-AUC = 0.92). Comparative analyses reveal that blockchain-derived features improve detection accuracy by approximately eight percentage points, while ESG governance and sentiment indicators emerge as significant predictors of fraudulent behavior. The hybrid model significantly outperforms financial-only, blockchain-only, and ESG-only models, confirming that fraud is inherently multidimensional. Explainable AI (SHAP) further ensures interpretability without degrading model performance, strengthening transparency and forensic defensibility. The findings demonstrate that an integrated AI–Blockchain–ESG approach provides a robust, transparent, and scalable solution for modern fraud detection challenges and offers a theoretical and practical foundation for next-generation forensic accounting systems.