Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Large Language Model for Corporate Credit Scoring

Chitro Majumdar, Sergio Scandizzo, Ratanlal Mahanta, Avradip Mandal, Swarnendu Bhattacharjee

TL;DR

Omega^2 addresses the need for accurate, interpretable corporate credit scoring that remains robust over time and across rating agencies. It combines language-based reasoning with gradient-boosting models through a Resource Augmented Generation framework anchored by a vector store and a knowledge graph, optimized under temporal validation. The approach achieves mean test AUCs above $0.93$ across Moody’s, S&P, Fitch, and Egan-Jones and delivers interpretable feature attributions via SHAP, with regression $R^2$ around $0.60$ on unseen data. The study provides a reproducible, open-source blueprint for institution-grade AI in credit risk that generalizes across agencies and time.

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.

A Large Language Model for Corporate Credit Scoring

TL;DR

Omega^2 addresses the need for accurate, interpretable corporate credit scoring that remains robust over time and across rating agencies. It combines language-based reasoning with gradient-boosting models through a Resource Augmented Generation framework anchored by a vector store and a knowledge graph, optimized under temporal validation. The approach achieves mean test AUCs above across Moody’s, S&P, Fitch, and Egan-Jones and delivers interpretable feature attributions via SHAP, with regression around on unseen data. The study provides a reproducible, open-source blueprint for institution-grade AI in credit risk that generalizes across agencies and time.

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.

Paper Structure

This paper contains 24 sections, 4 equations, 10 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. The Omega$^2$ (i.e. RsRL empowered LLM Omega-square) platform
  4. Vector Database
  5. Knowledge Graph
  6. Integration via Resource Augmented Generation (RAG)
  7. Quantitative Models
  8. Scalability and Efficiency
  9. Customization and Adaptability
  10. Methodology
  11. Data summary and exploratory statistics
  12. Data preprocessing and cleaning
  13. Target construction and justification
  14. Temporal partitioning and validation protocol
  15. Model selection, training, and hyperparameter optimization
  16. ...and 9 more sections

Figures (10)

  • Figure 1: Architecture of Omega$^2$
  • Figure 2: Corporate credit scores
  • Figure 3: Classification accuracy comparison across rating agencies under the Omega2 pipeline.
  • Figure 4: AUC-ROC stability between training and test splits, reflecting temporal generalization in the Omega2 classification models.
  • Figure 5: Regression model performance showing Train--Test $R^{2}$ correlation across rating agencies.
  • ...and 5 more figures