Research and Analysis of Credit Default Prediction Based on Machine Learning

Research Article
Open access

Research and Analysis of Credit Default Prediction Based on Machine Learning

Zhen Chen 1*
  • 1 School of Economics, Guangdong Ocean University, Zhanjiang, China    
  • *corresponding author chenzhenzhen@stu.gdou.edu.cn
AEMPS Vol.170
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-019-1
ISBN (Online): 978-1-80590-020-7

Abstract

With the continuous expansion of credit scale and the complexity of financial risks, traditional credit default prediction models are difficult to meet the needs of accurate prediction of potential default users due to unbalanced data, low feature screening efficiency and insufficient interpretability. Based on the above problems, this paper uses the SMOTE method to deal with the problem of data imbalance, analyzes the correlation between the characteristic variables and the target variables, and finally compares the performance of the six models. Using AUC, Accuracy, precision, and recall as evaluation indicators, it was found that people who rented houses, had a history of default, had a high proportion of loans to income, had high interest rates, and had existing debts were more likely to default. In the experiment, the SMOTE-XGBoost combination has outstanding performance, which can solve the imbalance in the data set, capture more potential defaulting users, and provide a more effcient and accurate model for the financial industry.

Keywords:

Credit, XGBoost, SMOTE

Chen,Z. (2025). Research and Analysis of Credit Default Prediction Based on Machine Learning. Advances in Economics, Management and Political Sciences,170,7-16.
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References

[1]. LI Aihua,LIU Wanxin,CHEN Sifan & SHI Yong. Research on SMOTE-BO-XGBoost Integrated Credit Scoring Model for Unbalanced Data. Chinese Management Science, 1-10.doi:10.16381/j.cnki.issn1003-207x.2023.0635.

[2]. Khaoula Idbenjra, Kristof Coussement & Arno De Caigny. (2024). Investigating the beneficial impact of segmentation-based modelling for credit scoring. Decision Support Systems, 179, 114170-.

[3]. Zhang Qiong, Zhang Chang & (2025). Credit Risk Assessment of Commercial Bank Customers Based on Machine Learning. 365-374.

[4]. Mengyu Ren & Linghui Zhang. (2020). Research on Credit Risk Rating System of Bank of China. (eds.)

[5]. Shaoshu Li. (2024). Machine Learning in Credit Risk Forecasting — A Survey on Credit Risk Exposure.Accounting and Finance Research,13(2),

[6]. Xolani Dastile, Turgay Celik & Moshe Potsane. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing Journal, 91, 106263-106263.

[7]. Belma Ozturkkal & Ranik Raaen Wahlstrøm. (2024). Explaining Mortgage Defaults Using SHAP and LASSO.Computational Economics,(prepublish),1-35.

[8]. LIU Manhu. (2023). Research on Credit Default Risk Prediction of a Commercial Bank Based on LightGBM (Master's Thesis, Chongqing University). Master https://link.cnki.net/doi/10.27670/d.cnki.gcqdu.2023.003390doi:10.27670/d.cnki.gcqdu.2023.003390.

[9]. Ruilin Hu & Tianyang Luo. (2023).XGBoost-LSTM for Feature Selection and Predictions for the S&P 500 Financial Sector.(eds.) Proceedings of the 2nd International Conference on Financial Technology and Business Analysis(part3)(pp.214-222). Rotman Commerce, University of Toronto;School of Management and Economics, Chinese University of Hongkong Shenzhen;

[10]. Kianeh Kandi & Antonio García Dopico. (2025). Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms. Machine Learning and Knowledge Extraction, 7 (1), 20-20.

Cite this article

Chen,Z. (2025). Research and Analysis of Credit Default Prediction Based on Machine Learning. Advances in Economics, Management and Political Sciences,170,7-16.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 9th International Conference on Economic Management and Green Development

ISBN：978-1-80590-019-1(Print) / 978-1-80590-020-7(Online)
Editor：Florian Marcel Nuţă
Conference website: https://2025.icemgd.org/
Conference date: 26 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.170
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. LI Aihua,LIU Wanxin,CHEN Sifan & SHI Yong. Research on SMOTE-BO-XGBoost Integrated Credit Scoring Model for Unbalanced Data. Chinese Management Science, 1-10.doi:10.16381/j.cnki.issn1003-207x.2023.0635.

[2]. Khaoula Idbenjra, Kristof Coussement & Arno De Caigny. (2024). Investigating the beneficial impact of segmentation-based modelling for credit scoring. Decision Support Systems, 179, 114170-.

[3]. Zhang Qiong, Zhang Chang & (2025). Credit Risk Assessment of Commercial Bank Customers Based on Machine Learning. 365-374.

[4]. Mengyu Ren & Linghui Zhang. (2020). Research on Credit Risk Rating System of Bank of China. (eds.)

[5]. Shaoshu Li. (2024). Machine Learning in Credit Risk Forecasting — A Survey on Credit Risk Exposure.Accounting and Finance Research,13(2),

[6]. Xolani Dastile, Turgay Celik & Moshe Potsane. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing Journal, 91, 106263-106263.

[7]. Belma Ozturkkal & Ranik Raaen Wahlstrøm. (2024). Explaining Mortgage Defaults Using SHAP and LASSO.Computational Economics,(prepublish),1-35.

[8]. LIU Manhu. (2023). Research on Credit Default Risk Prediction of a Commercial Bank Based on LightGBM (Master's Thesis, Chongqing University). Master https://link.cnki.net/doi/10.27670/d.cnki.gcqdu.2023.003390doi:10.27670/d.cnki.gcqdu.2023.003390.

[9]. Ruilin Hu & Tianyang Luo. (2023).XGBoost-LSTM for Feature Selection and Predictions for the S&P 500 Financial Sector.(eds.) Proceedings of the 2nd International Conference on Financial Technology and Business Analysis(part3)(pp.214-222). Rotman Commerce, University of Toronto;School of Management and Economics, Chinese University of Hongkong Shenzhen;

[10]. Kianeh Kandi & Antonio García Dopico. (2025). Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms. Machine Learning and Knowledge Extraction, 7 (1), 20-20.

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