MACHINE LEARNING IN CREDIT RISK ASSESSMENT: ADVANCES, CHALLENGES, AND IMPLICATIONS FOR EMERGING MARKETS

Credit risk assessment is a critical function for banks and financial institutions, as it directly influences financial stability, profitability, and access to credit. While traditional credit scoring approaches—such as rule-based systems and logistic regression—continue to be widely adopted due to their interpretability and alignment with regulatory and supervisory requirements, they are often constrained by linear assumptions and challenges in capturing complex, non-linear relationships in borrower data. The increasing availability of large-scale financial datasets, combined with advances in artificial intelligence, has led to growing adoption of machine learning (ML) techniques to enhance credit risk modeling. This paper provides a comprehensive review of ML applications in credit risk assessment, examining prominent model families—including tree-based ensembles (e.g., random forests), gradient boosting methods (e.g., XGBoost and LightGBM), and neural networks—alongside key performance metrics such as AUC-ROC and Precision-Recall curves. The review also addresses major implementation challenges, including data quality and bias, model explainability (through explainable AI techniques), fairness considerations, regulatory compliance (e.g., Basel frameworks and supervisory guidelines), and the need for robust governance, validation, and human oversight. Drawing on academic literature, industry practices, and regulatory publications (e.g., BIS Working Papers), the analysis demonstrates that ML models can deliver superior predictive accuracy and improved risk segmentation compared to traditional methods, particularly when leveraging richer or alternative data sources. Special attention is given to the opportunities and considerations for emerging markets, such as Uzbekistan, where ML-based approaches—supported by responsible use of alternative data—hold significant potential to promote financial inclusion for borrowers with limited traditional credit histories, provided that strong data governance, ethical safeguards, transparency, and alignment with local regulatory expectations are maintained.