A Large Language Model for Corporate Credit Scoring

TL;DR

Omega^2 is a novel large language model framework that integrates structured financial data with machine learning to enhance the accuracy and interpretability of corporate credit ratings across multiple agencies.

Contribution

The paper introduces Omega^2, a new LLM-based framework that combines financial data and advanced ML models for improved credit scoring reliability and transparency.

Findings

Achieved mean test AUC above 0.93 across agencies

Demonstrated generalization across different rating systems

Maintained temporal consistency in predictions

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 & 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.