Enhanced Credit Score Prediction Using Ensemble Deep Learning Model

TL;DR

This paper presents an ensemble deep learning approach combining Random Forest, XGBoost, and TabNet to improve credit score prediction accuracy, validated through comprehensive metric comparisons.

Contribution

It introduces a novel ensemble stacking model that integrates traditional machine learning and deep learning models for enhanced credit scoring accuracy.

Findings

The ensemble model outperforms individual models in accuracy metrics.

Combining models improves precision, recall, and F1 scores.

The approach significantly advances credit score prediction performance.

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.