{"ID":2846240,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02593","arxiv_id":"2511.02593","title":"A Large Language Model for Corporate Credit Scoring","abstract":"We introduce Omega^2, 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 Omega^2 on a multi-agency dataset of 7,800 corporate credit ratings drawn from Moody's, Standard \u0026 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. Omega^2 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.","short_abstract":"We introduce Omega^2, 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 Omega^2 on a multi-agency dataset of 7,800 corporate credit ratings drawn from Moo...","url_abs":"https://arxiv.org/abs/2511.02593","url_pdf":"https://arxiv.org/pdf/2511.02593v1","authors":"[\"Chitro Majumdar\",\"Sergio Scandizzo\",\"Ratanlal Mahanta\",\"Avradip Mandal\",\"Swarnendu Bhattacharjee\"]","published":"2025-11-04T14:15:39Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}