Research on Data Driven Personal Credit Default Prediction: A Comparative Study of Random Forest and XGBoost Models

Research Article
Open access

Research on Data Driven Personal Credit Default Prediction: A Comparative Study of Random Forest and XGBoost Models

Kexin Wang 1*
  • 1 School of Economics & Management, Northwest University, Xi’an, China    
  • *corresponding author 2021103236@stumail.nwu.edu.cn
AEMPS Vol.193
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-201-0
ISBN (Online): 978-1-80590-202-7

Abstract

With the rapid development of consumer finance, personal credit business is becoming increasingly active in the financial market, but it is also accompanied by significant credit risks.How to use big data methods to achieve efficient and accurate default prediction has become a core issue in the field of credit risk control.This article takes the Home Credit Default Risk dataset on the Kaggle platform as the research object, and uses data mining methods to systematically analyze the statistical correlation between external credit scores, borrower annual income, loan amounts, and other key features and default risk.By using Information Value (IV) and Kernel Density Estimation (KDE) methods to screen high discriminative force variables, and based on data distribution characteristics, a prediction model with Random Forest and Extreme Gradient Boosting Tree (XGBoost) as the core is constructed to compare its performance under precision, recall, F1 value, and AUC indicators.The results show that XGBoost has better recognition ability in imbalanced data scenarios, while random forests have more advantages in feature interpretability.The research results not only verify the effectiveness of the feature distribution driven model design, but also provide practical suggestions and theoretical support for financial institutions in pre loan risk screening and dynamic monitoring during loans.

Keywords:

Credit risk prediction, Data driven, Random forest, XGBoost

Wang,K. (2025). Research on Data Driven Personal Credit Default Prediction: A Comparative Study of Random Forest and XGBoost Models. Advances in Economics, Management and Political Sciences,193,30-37.
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References

[1]. Thomas, L. C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149–172.

[2]. Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Wiley.

[3]. Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465.

[4]. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD 16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

[5]. Li Ming, Wang Yan, Zhao Qian (2021).Research on Network Credit Default Prediction Based on Machine Learning. Financial Research, (4), 88-96

[6]. Zhou Yong (2020). Optimization analysis of credit risk control model under the background of financial technology. Southern Finance, (10), 54-60

[7]. Smith, J. A., & Doe, R. L. (2018). Application of Random Forests in Financial Risk Prediction: A Comparative Study. Journal of Financial Analytics and Risk Management, *12*(3), 45–67.

[8]. Brown, A. R., Lee, C. T., & Zhang, H. (2019). Optimizing XGBoost for Imbalanced Credit Data: A Case Study on Dynamic Threshold Adjustment. In Proceedings of the 36th International Conference on Machine Learning (ICML) (pp. 123–135). PMLR.

Cite this article

Wang,K. (2025). Research on Data Driven Personal Credit Default Prediction: A Comparative Study of Random Forest and XGBoost Models. Advances in Economics, Management and Political Sciences,193,30-37.

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 ICEMGD 2025 Symposium: Innovating in Management and Economic Development

ISBN：978-1-80590-201-0(Print) / 978-1-80590-202-7(Online)
Editor：Florian Marcel Nuţă Nuţă, Ahsan Ali Ashraf
Conference website: https://2025.icemgd.org/Lahore.html
Conference date: 23 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.193
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Thomas, L. C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149–172.

[2]. Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Wiley.

[3]. Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465.

[4]. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD 16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

[5]. Li Ming, Wang Yan, Zhao Qian (2021).Research on Network Credit Default Prediction Based on Machine Learning. Financial Research, (4), 88-96

[6]. Zhou Yong (2020). Optimization analysis of credit risk control model under the background of financial technology. Southern Finance, (10), 54-60

[7]. Smith, J. A., & Doe, R. L. (2018). Application of Random Forests in Financial Risk Prediction: A Comparative Study. Journal of Financial Analytics and Risk Management, *12*(3), 45–67.

[8]. Brown, A. R., Lee, C. T., & Zhang, H. (2019). Optimizing XGBoost for Imbalanced Credit Data: A Case Study on Dynamic Threshold Adjustment. In Proceedings of the 36th International Conference on Machine Learning (ICML) (pp. 123–135). PMLR.

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