Credit Default Prediction Based on Blending Learning Model

Research Article
Open access

Credit Default Prediction Based on Blending Learning Model

Yaoxi Li 1 , Yuxuan Tian 2* , Jianan Zhuo 3
  • 1 Bellevue College    
  • 2 Northeastern University at Qinhuangdao    
  • 3 Dulwich College Suzhou    
  • *corresponding author 202113155@stu.neu.edu.cn
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230112
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

Currently, the preventions of credit default usually will be evaluated by user’s credit value before loaning from banks. However, for the loan user, who have no existing record of loaning and the situation of low credit value, it cannot precisely recognize the risk of credit default. After a credit default, the bank not only doesn’t get the signed compensation and principal in time, but also the debtor needs to bear the expensive corresponding late fees and credit costs. Therefore, reducing credit defaults can decline more burden of debtors and creditors. In this paper, the authors evaluate multiple machine learning models including algorithms belong to machine learning and deep learning, using blending model to boost the prediction effect and accuracy, while proposing an optimization design to further enhance the stability, accuracy and generalization capacity of proposed algorithm, so as to effectively decrease the credit default rate and the risk of bank loss in practice.

Keywords:

credit default, machine learning, deep learning, blending model

Li,Y.;Tian,Y.;Zhuo,J. (2023). Credit Default Prediction Based on Blending Learning Model. Applied and Computational Engineering,8,726-733.
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References

[1]. Ding, J., Huang, J., Li, Y., & Meng, M. (2019). Is there an effective reputation mechanism in peer-to-peer lending? Evidence from China. Finance Research Letters, 30, 208-215.

[2]. Caruso, G., Gattone, S. A., Fortuna, F., & Di Battista, T. (2021). Cluster Analysis for mixed data: An application to credit risk evaluation. Socio-Economic Planning Sciences, 73, 100850.

[3]. Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intelligent systems in accounting, finance and management, 18(2-3), 59-88.

[4]. Soui, M., Gasmi, I., Smiti, S., & Ghédira, K. (2019). Rule-based credit risk assessment model using multi-objective evolutionary algorithms. Expert systems with applications, 126, 144-157.

[5]. Sun, J., Lang, J., Fujita, H., & Li, H. (2018). Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Information Sciences, 425, 76-91.

[6]. Catal, C., Sevim, U., & Diri, B. (2011). Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm. Expert Systems with Applications, 38(3), 2347-2353.

[7]. Hamori, S., Kawai, M., Kume, T., Murakami, Y., & Watanabe, C. (2018). Ensemble learning or deep learning? Application to default risk analysis. Journal of Risk and Financial Management, 11(1), 12.

[8]. Moscatelli, M., Parlapiano, F., Narizzano, S., & Viggiano, G. (2020). Corporate default forecasting with machine learning. Expert Systems with Applications, 161, 113567.

[9]. Vlamis, P. (2007). Default risk of the UK real estate companies: is there a macro-economy effect?. The Journal of Economic Asymmetries, 4(2), 99-117.

[10]. Kotsiantis, S. B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39, 261-283.

[11]. Gai, K., Zhu, X., Li, H., Liu, K., & Wang, Z. (2017). Learning piece-wise linear models from large scale data for ad click prediction. arXiv preprint arXiv:1704.05194.

[12]. Biau, G., Devroye, L., & Lugosi, G. (2008). Consistency of random forests and other averaging classifiers. Journal of Machine Learning Research, 9(9), 1-9.

[13]. Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21.

Cite this article

Li,Y.;Tian,Y.;Zhuo,J. (2023). Credit Default Prediction Based on Blending Learning Model. Applied and Computational Engineering,8,726-733.

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 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Ding, J., Huang, J., Li, Y., & Meng, M. (2019). Is there an effective reputation mechanism in peer-to-peer lending? Evidence from China. Finance Research Letters, 30, 208-215.

[2]. Caruso, G., Gattone, S. A., Fortuna, F., & Di Battista, T. (2021). Cluster Analysis for mixed data: An application to credit risk evaluation. Socio-Economic Planning Sciences, 73, 100850.

[3]. Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intelligent systems in accounting, finance and management, 18(2-3), 59-88.

[4]. Soui, M., Gasmi, I., Smiti, S., & Ghédira, K. (2019). Rule-based credit risk assessment model using multi-objective evolutionary algorithms. Expert systems with applications, 126, 144-157.

[5]. Sun, J., Lang, J., Fujita, H., & Li, H. (2018). Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Information Sciences, 425, 76-91.

[6]. Catal, C., Sevim, U., & Diri, B. (2011). Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm. Expert Systems with Applications, 38(3), 2347-2353.

[7]. Hamori, S., Kawai, M., Kume, T., Murakami, Y., & Watanabe, C. (2018). Ensemble learning or deep learning? Application to default risk analysis. Journal of Risk and Financial Management, 11(1), 12.

[8]. Moscatelli, M., Parlapiano, F., Narizzano, S., & Viggiano, G. (2020). Corporate default forecasting with machine learning. Expert Systems with Applications, 161, 113567.

[9]. Vlamis, P. (2007). Default risk of the UK real estate companies: is there a macro-economy effect?. The Journal of Economic Asymmetries, 4(2), 99-117.

[10]. Kotsiantis, S. B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39, 261-283.

[11]. Gai, K., Zhu, X., Li, H., Liu, K., & Wang, Z. (2017). Learning piece-wise linear models from large scale data for ad click prediction. arXiv preprint arXiv:1704.05194.

[12]. Biau, G., Devroye, L., & Lugosi, G. (2008). Consistency of random forests and other averaging classifiers. Journal of Machine Learning Research, 9(9), 1-9.

[13]. Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21.

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