Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment

Nazário Augusto de Oliveira () and Leonardo Fernando Cruz Basso
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Nazário Augusto de Oliveira: Department of Business Administration—Strategic Finance, Mackenzie Presbyterian University (UPM), São Paulo 01302-907, Brazil
Leonardo Fernando Cruz Basso: Department of Business Administration—Strategic Finance, Mackenzie Presbyterian University (UPM), São Paulo 01302-907, Brazil

Risks, 2025, vol. 13, issue 6, 1-28

Abstract: Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. Using a seven-year dataset from S&P Capital IQ Pro, corporate credit ratings across 20 countries were analyzed, leveraging 51 financial and business risk variables. The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. Results indicate that Artificial Neural Networks (ANN) and GB consistently outperform traditional models, particularly in capturing non-linear relationships and complex interactions among predictive factors. This study advances financial risk management by demonstrating the efficacy of ML-driven credit rating systems, offering a more accurate, efficient, and scalable solution. Additionally, it provides practical insights for financial institutions aiming to enhance their risk assessment frameworks. Future research should explore alternative data sources, real-time analytics, and model explainability to facilitate regulatory adoption.

Keywords: machine learning; credit ratings; predictive modeling; financial risk assessment; risk management (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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