Default Prediction

Default prediction focuses on accurately forecasting the likelihood of loan defaults or other forms of financial non-payment, aiming to improve risk management and lending decisions. Current research emphasizes advanced machine learning techniques, including ensemble methods (combining multiple models like XGBoost and LightGBM), graph neural networks that leverage borrower interconnections, and neural networks tailored to handle imbalanced datasets and spatio-temporal dependencies. These improvements aim to enhance predictive accuracy, particularly for individuals with limited credit history, and address the economic consequences of misclassifications through techniques like cost-sensitive learning and Neyman-Pearson approaches. The field's impact extends to financial institutions, enabling more informed credit risk assessment and contributing to greater financial stability.