Credit Scoring Model
Credit scoring models aim to predict the likelihood of loan default, guiding lending decisions and managing financial risk. Recent research emphasizes improving model accuracy and fairness through advanced techniques like graph neural networks, which leverage borrower relationships and network data to enhance predictive power, and by addressing sampling bias inherent in available datasets. A key focus is on developing responsible and interpretable models that mitigate bias, ensure equitable outcomes across different demographic groups, and provide transparency into the decision-making process. These advancements have significant implications for financial institutions, enabling more accurate risk assessment and promoting fairer lending practices.