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http://dx.doi.org/10.13106/jafeb.2022.vol9.no8.0089

Credit Risk Evaluations of Online Retail Enterprises Using Support Vector Machines Ensemble: An Empirical Study from China  

LI, Xin (Henan University of Science and Technology)
XIA, Han (School of Business Administration, Henan Finance University)
Publication Information
The Journal of Asian Finance, Economics and Business / v.9, no.8, 2022 , pp. 89-97 More about this Journal
Abstract
The e-commerce market faces significant credit risks due to the complexity of the industry and information asymmetries. Therefore, credit risk has started to stymie the growth of e-commerce. However, there is no reliable system for evaluating the creditworthiness of e-commerce companies. Therefore, this paper constructs a credit risk evaluation index system that comprehensively considers the online and offline behavior of online retail enterprises, including 15 indicators that reflect online credit risk and 15 indicators that reflect offline credit risk. This paper establishes an integration method based on a fuzzy integral support vector machine, which takes the factor analysis results of the credit risk evaluation index system of online retail enterprises as the input and the credit risk evaluation results of online retail enterprises as the output. The classification results of each sub-classifier and the importance of each sub-classifier decision to the final decision have been taken into account in this method. Select the sample data of 1500 online retail loan customers from a bank to test the model. The empirical results demonstrate that the proposed method outperforms a single SVM and traditional SVMs aggregation technique via majority voting in terms of classification accuracy, which provides a basis for banks to establish a reliable evaluation system.
Keywords
Credit Evaluation; Support Vector Machines Ensemble; Fuzzy Integral; Bagging;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Abellan, J., & Castellano, J. G. (2017). A comparative study on base classifiers in ensemble methods for credit scoring. Expert Systems with Applications, 73, 1-10. https://doi.org/10.1016/j.eswa.2016.12.020   DOI
2 Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1), 105-139.   DOI
3 Isnurhadi, I., Adam, M., Sulastri, S., Andriana, I., & Muizzuddin, M. (2021). Bank capital, efficiency, and risk: Evidence from Islamic banks. Journal of Asian Finance, Economics, and Business, 8(1), 841-850. https://doi.org/10.13106/jafeb.2021.vol8.no1.841   DOI
4 Pham, H. N. (2021). How does internal control affect bank credit risk in Vietnam? A Bayesian analysis. Journal of Asian Finance, Economics, and Business, 8(1), 873-880. https://doi.org/10.13106/jafeb.2021.vol8.no1.873   DOI
5 Khashman, A. (2010). Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Systems with Applications, 37(9), 6233-6239. https://doi.org/10.1016/j.eswa.2010.02.101   DOI
6 Kwak, K. C., & Pedrycz, W. (2005). Face recognition: A study in information fusion using a fuzzy integral. Pattern Recognition Letters, 26(6), 719-733. https://doi.org/10.1016/j.patrec.2004.09.024   DOI
7 Manna, A. K., Cardenas-Barron, L. E., Dey, J. K., Mondal, S. K., Shaikh, A. A., Cespedes-Mota, A., & Trevino-Garza, G. (2022). A fuzzy imperfect production inventory model based on fuzzy differential and fuzzy integral method. Journal of Risk and Financial Management, 15(6), 239. https://doi.org/10.3390/jrfm15060239   DOI
8 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. https://doi.org/10.1016/j.eswa.2007.05.019   DOI
9 Abid, L. (2022). A logistic regression model for credit risk of companies in the service sector. International Research in Economics and Finance, 6(2), 1. https://doi.org/10.20849/iref.v6i2.1179   DOI
10 Moula, F. E., Guotai, C., & Abedin, M. Z. (2017). Credit default prediction modeling: An application of support vector machine. Risk Management, 19(2), 158-187. https://doi.org/10.1057/s41283-017-0016-x   DOI
11 Pham, T. B. D. (2022). The impact of foreign ownership on the credit risk of commercial banks in Vietnam: Before the context of participation in the CPTPP. Journal of Asian Finance, Economics, and Business, 9(5), 305-311. https://doi.org/10.13106/jafeb.2021.vol8.no3.0771   DOI
12 Srinivasan, V., & Kim, Y. H. (1987). Credit granting: A comparative analysis of classification procedures. Journal of Finance, 42(3), 665-681. https://doi.org/10.1111/j.1540-6261.1987.tb04576.x   DOI
13 Jiang, H., Ching, W. K., Yiu, K. F. C., & Qiu, Y. (2018). Stationary Mahalanobis kernel SVM for credit risk evaluation. Applied Soft Computing, 71, 407-417. https://doi.org/10.1016/j.asoc.2018.07.005   DOI
14 Le, T. T. D., & Diep, T. T. (2020). The effect of lending structure concentration on credit risk: The evidence of Vietnamese commercial banks. Journal of Asian Finance, Economics, and Business, 7(7), 59-72. https://doi.org/10.13106/jafeb.2020.vol7.no7.059   DOI
15 Li, Z. (2018). GBDT-SVM credit risk assessment model and empirical analysis of peer-to-peer borrowers under consideration of audit information. Open Journal of Business and Management, 06(2), 362-372. https://doi.org/10.4236/ojbm.2018.62026   DOI
16 Leonard, A. C., & Villiers, C. (2000). The nature of the end-user relationship in the development of electronic commerce applications. SIGCPR '00: Proceedings of the 2000 ACM SIGCPR Conference on Computer Personnel Research, Illinois, USA, April 2000 (pp. 86-92). https://doi.org/10.1145/333334.333360   DOI
17 Rizwan-ul-Hassan, Li, C., & Liu, Y. (2021). Online dynamic security assessment of wind integrated power system using SDAE with SVM ensemble boosting learner. International Journal of Electrical Power and Energy Systems, 125, 106429. https://doi.org/10.1016/j.ijepes.2020.106429   DOI
18 Liu, W., Fan, H., & Xia, M. (2022). Credit scoring is based on tree-enhanced gradient boosting decision trees. Expert Systems with Applications, 189, 116034. https://doi.org/10.1016/j.eswa.2021.116034   DOI
19 Naili, M., & Lahrichi, Y. (2022). The determinants of banks' credit risk: Review of the literature and future research agenda. International Journal of Finance and Economics, 27(1), 334-360. https://doi.org/10.1002/ijfe.2156   DOI
20 Yuan, Y., & Shaw, M. J. (1995). Induction of fuzzy decision trees. Fuzzy Sets and Systems, 69(2), 125-139. https://doi.org/10.1016/0165-0114(94)00229-Z   DOI
21 Zhao, J., & Li, B. (2022). Credit risk assessment of small and medium-sized enterprises in supply chain finance based on SVM and BP neural network. Neural Computing and Applications, 56, 1-11
22 Platt, J. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 10(3), 61-74.
23 Shen, F., Ma, X., Li, Z., Xu, Z., & Cai, D. (2018). An extended intuitionistic fuzzy topsis method based on a new distance measure with an application to credit risk evaluation. Information Sciences, 428, 105-119. https://doi.org/10.1016/j.ins.2017.10.045   DOI
24 Yao, G., Hu, X., & Wang, G. (2022). A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain. Expert Systems with Applications, 200, 117002. https://doi.org/10.1016/j.eswa.2022.117002   DOI