# Interpretable Credit Default Prediction with Ensemble Learning and SHAP

arXiv: https://arxiv.org/abs/2505.20815 · HTML: https://mlsanity.com/paper/2505.20815/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 2 of 2)

- [Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models](https://mlsanity.com/paper/2409.19128/) (2024)
- [Less is More: Data Pruning for Faster Adversarial Training](https://mlsanity.com/paper/2302.12366/) (2023)

## Built on by (most cited first, 1 of 1)

- [Gated Multimodal Graph Learning for Personalized Recommendation](https://mlsanity.com/paper/2506.00107/) (2025)

## Abstract (arXiv)

This study focuses on the problem of credit default prediction, builds a modeling framework based on machine learning, and conducts comparative experiments on a variety of mainstream classification algorithms. Through preprocessing, feature engineering, and model training of the Home Credit dataset, the performance of multiple models including logistic regression, random forest, XGBoost, LightGBM, etc. in terms of accuracy, precision, and recall is evaluated. The results show that the ensemble learning method has obvious advantages in predictive performance, especially in dealing with complex nonlinear relationships between features and data imbalance problems. It shows strong robustness. At the same time, the SHAP method is used to analyze the importance and dependency of features, and it is found that the external credit score variable plays a dominant role in model decision making, which helps to improve the model's interpretability and practical application value. The research results provide effective reference and technical support for the intelligent development of credit risk control systems.