Artificial Intelligence (AI)-Enabled Credit Scoring in Banking and Fintech : In Search of Maximum Prediction Accuracy

Artificial intelligence (AI) and machine learning (ML) are having a transformative impact on the financial industry, with significant implications for credit risk management. Accurate predictions in credit risk help mitigate potential financial losses due to defaults. This not only strengthens the competitive positioning of individual institutions, but also enhances the overall stability of the financial system. In search of maximum prediction accuracy, we have trained different configurations of a stacked ensemble model that combines the most powerful baseline models into a so-called super learner. The four models used as a baseline in this study were Logistic Regression, Random Forest, Gradient Boosting, and Deep Learning. The experiment utilized three real-world credit risk datasets. Ultimately, we derive and present explicit steps to optimize prediction accuracy in credit scoring and signpost prospective research trajectories