Abstract

Credit scoring is vital for assessing borrowers' creditworthiness and managing risks in financial systems. Traditional credit scoring models often fail to capture non-linear relationships and handle high-dimensional data, leading to less accurate predictions. This research explores the application of advanced data mining algorithms, such as ensemble learning methods, neural networks, and hybrid models, for predicting default rates. Empirical findings reveal significant improvements in predictive accuracy and interpretability. Key takeaways emphasize the importance of effective preprocessing and feature engineering techniques in creating scalable and efficient credit scoring models