Enhancing ESG Risk Assessment with Litigation Signals: A Legal-AI Hybrid Approach for Detecting Latent Risks

Environmental, Social, and Governance (ESG) ratings are widely used for investment and regulatory decision-making, yet they often suffer from symbolic compliance and information asymmetry. To address these limitations, this study introduces a hybrid ESG risk assessment model that integrates court ruling data with traditional ESG ratings to detect latent sustainability risks. Using a dataset of 213 ESG-related U.S. court rulings from January 2023 to May 2025, we apply natural language processing (TF-IDF, Legal-BERT) and explainable AI (SHAP) techniques to extract structured features from legal texts. We construct and compare classification models—including Random Forest, XGBoost, and a Legal-BERT-based hybrid model—to predict firms’ litigation risk. The hybrid model significantly outperforms the baseline ESG-only model in all key metrics: F1-score (0.81), precision (0.79), recall (0.84), and AUC-ROC (0.87). SHAP analysis reveals that legal features such as regulatory sanctions and governance violations are the most influential predictors. This study demonstrates the empirical value of integrating adjudicated legal evidence into ESG modeling and offers a transparent, verifiable framework to enhance ESG risk evaluation and reduce information asymmetry in sustainability assessments.