Study of Personal Credit Risk Assessment Based on SVM

LI, Xin (College of Business Administration, Henan University of Science and Technology)
XIA, Han (College of Business Administration, Henan Finance University)

LI, Xin, & XIA, Han. (2022). Study of Personal Credit Risk Assessment Based on SVM. The Journal of Industrial Distribution & Business(JIDB), 13(10), 1-8, https://doi.org/https://doi.org/10.13106/jidb.2022.vol13.no10.1

복사

Abstract

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

keywords: Credit index, Credit evaluation, Support vector machines ensemble, customer to customer, bagging, Fuzzy integral

참고문헌

Abid, L. (2022). A Logistic Regression Model for Credit Ri sk of Companies in the Service Sector. International Res earch in Economics and Finance, 6(2), 1-10.

Anil Kumar, C. J., Raghavendra, B. K., & Raghavendra, S.(2022). A Credit Scoring Heterogeneous Ensemble Mode l Using Stacking and Voting. Indian Journal of Science and Technology, 15(7), 300-308.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. M achine learning, 20(3), 273-297.

Dou, J., Yunus, A. P., Bui, D. T., Merghadi, A., Sahana, M.,Zhu, Z., ... & Pham, B. T. (2020). Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning fram ework in a mountainous watershed, Japan. Landslides, 17(3), 641-658.

Hand, D. J., & Henley, W. E. (1997). Statistical classificatio n methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(3), 523-541.

Hu, Y., & Su, J. (2022). Research on Credit Risk Evaluatio n of Commercial Banks Based on Artificial Neural Net work Model. Procedia Computer Science, 199(1), 1168-1176.

Jadwal, P. K., Pathak, S., & Jain, S. (2022). Analysis of clustering algorithms for credit risk evaluation using multiple correspondence analysis. Microsystem Technologies,56(3), 1-7.

Kim, H. C., Pang, S., Je, H. M., Kim, D., & Bang, S. Y.(2003). Constructing support vector machine ensemble. Pattern recognition, 36(12), 2757-2767.

Khemakhem, S., & Boujelbene, Y. (2015). Credit risk prediction: A comparative study between discriminant analysis and the neural network approach. Accounting and Mana gement Information Systems, 14(1), 60-78.

10.

Kwak, K. C., & Pedrycz, W. (2005). Face recognition: A study in information fusion using fuzzy integral. Pattern Recognition Letters, 26(6), 719-733.

11.

Lai, R. K., Fan, C. Y., Huang, W. H., & Chang, P. C. (2009). Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Systems with Applications, 36(2), 3761-3773.

12.

Mahbobi, M., Kimiagari, S., & Vasudevan, M. (2021). Credit risk classification: an integrated predictive accuracy algorithm using artificial and deep neural networks. Annals of Operations Research,40(7), 1-29.

13.

Platt, J. (1999). Probabilistic outputs for support vector mach ines and comparisons to regularized likelihood method s. Advances in large margin classifiers, 10(3), 61-74.

14.

Roy, P. K., & Shaw, K. (2022). Developing a multi-criteria sustainable credit score system using fuzzy BWM and f uzzy TOPSIS. Environment, Development and Sustainability, 24(4), 5368-5399.

15.

Srinivasan, V., & Kim, Y. H. (1987). Credit granting: A comparative analysis of classification procedures. The Journal of Finance, 42(3), 665-681.

16.

Tsai, C. F., & Wu, J. W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert systems with applications, 34(4), 2639-2649.

17.

Wójcicka-Wójtowicz, A., & Piasecki, K. (2021). Application of the Oriented Fuzzy Numbers in Credit Risk Assessment. Mathematics, 9(5), 535-536.

18.

Xie, X., Hu, X., Xu, K., Wang, J., Shi, X., & Zhang, F. (2022). Evaluation of associated credit risk in supply chain based on trade credit risk contagion. Procedia Comput er Science, 199(1), 946-953.

19.

Zhang, L., Hu, H., & Zhang, D. (2015). A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation, 1(1), 1-21.

20.

Zhang, T., Fu, Q., Wang, H., Liu, F., Wang, H., & Han, L.(2021). Bagging-based machine learning algorithms for landslide susceptibility modeling. Natural hazards,49(4), 1-24.

21.

Zhao, J., & Li, B. (2022). Credit risk assessment of small a nd medium-sized enterprises in supply chain finance bas ed on SVM and BP neural network. Neural Computing and Applications,34(8), 12467-12478

바로가기메뉴

논문 상세

Vol.13 No.10

Study of Personal Credit Risk Assessment Based on SVM

Study of Personal Credit Risk Assessment Based on SVM

Abstract

참고문헌

The Journal of Industrial Distribution & Business(JIDB)