Credit risk management is a critical function for financial institutions, involving the assessment of borrowers' creditworthiness to minimize the risk of default. Traditional methods have been effective but often lack the predictive power and flexibility that modern machine learning (ML) models can provide. This thesis explores the potential improvements offered
