Credit Scoring

Credit scoring aims to predict the likelihood of loan default, enabling financial institutions to make informed lending decisions. Current research emphasizes improving prediction accuracy using ensemble methods combining tree-based models (like XGBoost and LightGBM) with deep learning architectures (like TabNet and neural networks), while simultaneously addressing ethical concerns such as bias and fairness through techniques like subgroup threshold optimization and distributionally robust optimization. This field is significant because advancements in credit scoring models directly impact access to credit, financial inclusion, and the overall stability of the financial system, with ongoing research focusing on explainability, handling missing data, and leveraging alternative data sources like network data and text analysis.