Credit Decision

Credit decision-making, aiming to accurately assess borrower creditworthiness, is undergoing a transformation driven by machine learning. Research focuses on improving prediction accuracy and mitigating bias through advanced techniques like graph neural networks, ensemble methods, and explainable AI (XAI) approaches such as SHAP values, while addressing challenges like imbalanced datasets and limited data for "thin-file" borrowers. These advancements enhance both the efficiency and fairness of lending processes, impacting financial institutions' risk management and potentially promoting more equitable access to credit. The field also emphasizes responsible AI development, incorporating fairness metrics and transparency to build trust and comply with regulations.