A Large Language Model for Corporate Credit Scoring
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908628122861568 |
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| author | Majumdar, Chitro Scandizzo, Sergio Mahanta, Ratanlal Mandal, Avradip Bhattacharjee, Swarnendu |
| author_facet | Majumdar, Chitro Scandizzo, Sergio Mahanta, Ratanlal Mandal, Avradip Bhattacharjee, Swarnendu |
| contents | 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 & 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Large Language Model for Corporate Credit Scoring Majumdar, Chitro Scandizzo, Sergio Mahanta, Ratanlal Mandal, Avradip Bhattacharjee, Swarnendu Machine Learning 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 & 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. |
| title | A Large Language Model for Corporate Credit Scoring |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.02593 |