Research on Financial Loan Default Prediction Based on Multi-Model Ensemble and Custom Thresholds - Chen, Jialun (Transactions on Computer Science and Intelligent Systems Research, 2024)
Loan defaults pose significant threats to financial institutions’ financial stability and reputation. Although existing risk assessment models have addressed this issue to some extent, they exhibit significant limitations when dealing with large-scale, high-dimensional data. Therefore, developing an advanced model that can predict loan defaults with higher accuracy is crucial. This paper aims to optimize loan default prediction by combining innovative algorithms and models to enhance the risk management capabilities of financial institutions and reduce economic losses. This study proposes a loan default prediction model based on the LendingClub dataset. The model integrates multiple machine learning algorithms, including Logistic Regression, Random Forest, Gradient Boosting, LightGBM, and CatBoost, as well as ensemble learning methods, aiming to improve the prediction accuracy and stability of the model. Through a comprehensive analysis of the model’s precision, recall, and custom evaluation metrics, this paper establishes an optimized comprehensive model, improving recall from 60% to 80% and precision from 28% to 29%. By optimizing thresholds, the model significantly enhances the identification of bad loans while balancing precision and recall, providing an effective solution for loan default prediction.