Credit Risk Model

Credit risk models aim to predict the likelihood of borrowers defaulting on loans, a crucial task for financial institutions. Recent research emphasizes improving model accuracy and fairness through advanced techniques like joint models incorporating longitudinal and spatial data, and the application of machine learning algorithms such as transformers and XGBoost, often coupled with natural language processing of validation reports to identify and mitigate model flaws. These advancements are significant because they enhance the reliability of credit scoring, leading to more efficient lending practices and potentially reducing economic disparities by mitigating algorithmic bias.