Enhanced Credit Score Prediction Using Ensemble Deep Learning Model

TL;DR

A potent model capable of accurately determining credit score levels is developed by integrating Random Forest, XGBoost, and TabNet, and through the stacking technique in ensemble modeling, which surpasses the limitations of single models and significantly advances the precise credit score prediction.

Abstract

In contemporary economic society, credit scores are crucial for every participant. A robust credit evaluation system is essential for the profitability of core businesses such as credit cards, loans, and investments for commercial banks and the financial sector. This paper combines high-performance models like XGBoost and LightGBM, already widely used in modern banking systems, with the powerful TabNet model. We have developed a potent model capable of accurately determining credit score levels by integrating Random Forest, XGBoost, and TabNet, and through the stacking technique in ensemble modeling. This approach surpasses the limitations of single models and significantly advances the precise credit score prediction. In the following sections, we will explain the techniques we used and thoroughly validate our approach by comprehensively comparing a series of metrics such as Precision, Recall, F1, and AUC. By integrating Random Forest, XGBoost, and with the TabNet deep learning architecture, these models complement each other, demonstrating exceptionally strong overall performance.

Figures (4)

• Figure 1: Data Balance
• Figure 2: Data Distribution Before and After Noise Removal
• Figure 3: Before Outlier Removal
• Figure 4: After Outlier Removal