2025/11/04 by Majumdar, Chitro, Scandizzo, Sergio, Mahanta, Ratanlal +2
Business, Management and Accounting · Computer Science · Economics, Econometrics and Finance · #Credit Risk and Financial Regulations #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.2511.02593
openalex publication_date 2025/11/04 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28
We introduce Omega2, a Large Language Model-driven framework for corporate credit scoring that combines structured financial data with advanced machine learning to improve predictive reliability and interpretability. Our study evaluates Omega2 on a multi-agency dataset of 7,800 corporate credit ratings drawn from Moody's, Standard & Poor's, Fitch, and Egan-Jones, each containing detailed firm-level financial indicators such as leverage, profitability, and liquidity ratios. The system integrates CatBoost, LightGBM, and XGBoost models optimized through Bayesian search under temporal validation to ensure forward-looking and reproducible results. Omega2 achieved a mean test AUC above 0.93 across agencies, confirming its ability to generalize across rating systems and maintain temporal consistency. These results show that combining language-based reasoning with quantitative learning creates a transparent and institution-grade foundation for reliable corporate credit-risk assessment.