Predicting Corporate Credit Ratings: An ML-Based Approach for Financial Sector Compliance

Accurate credit ratings are required for investment, financial stability, and regulatory compliance. Their dependence on the traditional rating systems, which prominently utilise manual evaluation and subjective evaluation, constrains them since they are deficient in terms of scalability, objectivity and consistency, especially in spheres of varied and variable financial scenes that are exhibited in South Asian countries. This paper describes a machine learning (ML) framework to make a prediction of corporate credit rating based on structured financial data in India and some of its neighbouring countries, such as Bangladesh, Sri Lanka, Nepal and Pakistan. Data of a wide range, consisting of above 2,800 firm-year observations, with 47 financial variables, were used to train and test several ML classifiers, such as Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting, and Artificial Neural Networks (ANN). The results showed that ANN and Gradient Boosting models performed better than the conventional classifiers with macro-averaged accuracies of 89.1 and 87.3, respectively. The findings varied across the countries, and the quality of the results was more accurate when the region was in a favourable geographical position, concerning information quality and benefited from suitable financial disclosure requirements. The statistical significance of the model difference was validated by means of using the Friedman and Nemenyi tests. The proposed solution demonstrates how AI-related credit assessment solutions can enhance the coverage, accuracy, and uniformity of ratings in the building of the financial marketplace. This plan allows intelligent credit risk strategies that are aligned with the South Asian financial environment to be implemented, and at the same time, it promotes data-driven compliance and decision-making.

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