How Can Machine Learning be Used to Predict Credit Ratings?

Abstract

Credit ratings remain the backbone of global debt markets, yet their slow updates (“stickness”), methodological opacity and their frequent absence in the fast-growing private-credit market leave investors without timely, transparent risk signals. This thesis develops and tests a purely accounting data-driven, machine-learning framework that predicts what credit rating S&P would have given, and in turn, provides a “shadow” rating while remaining a practical and interpretable tool for professionals in the credit investment industry. Using a global dataset of firm-level financials, we train three models—Gradient Boosting Model (GBM), Random Forest (RF), and Neural Network (NN)—to map seven core accounting ratios to ’ 1-notch scale. Model interpretability is ensured through SHAP values, which quantifies the exact feature importance of each input as drivers of the predictions. Out-of-sample testing shows that the GBM correctly predicts the exact S&P rating 26 % of the time and 72% of the time within ± 1-notch, delivering a mean absolute error of 1.18 notches. Thus, it outperforms RF and NN and surpasses distribution-aware guessing and multivariate linear regression baselines. SHAP analysis confirm that the models' logic aligns with established credit theory, thereby converting a "black box" into a explainable prediction that can be refreshed quarterly, dramatically reducing the rating lag that undermines traditional agencies. More broadly, the thesis shows how the latest advances in data science can be woven into the long-standing craft of credit analysis, reconciling the quest for sharper accuracy with the market's need for clarity and trust. By turning raw financial data into real-time, explainable risk signals, it points toward a future in which credit rating is as adaptive and transparent as the markets it serves.