A Comparison Study on the Era of Internet Finance China Construction of Credit Scoring System Model

  • Hongjun Zeng Business School, Guangxi University, Nanning, China
Keywords: Credit Scoring System, Random Forest, Discriminate Analysis, Logistic Regression, Comparison Study.

Abstract

At present, China's Internet finance has flourished, showing a variety of business models and operating mechanisms. Through Internet technology, financial institutions can speed up business processing and bring users a better service experience. However, there are also problems such as credit risk and user fraud, and it is urgent to improve the level of risk control through credit scoring models. Because of this, this article uses the borrower data of a Chinese financial institution from January 2017 to June 2017 as the original data, and then uses the Spearman rank correlation test to screen out the variables with reliable explanatory power from the many variables of the sample data, and then Based on the variables selected, R 3.4.3 and SPSS 23.0 were used to construct a random forest model, discriminant analysis model, and logistic regression model. In general, different models perform differently under different sample characteristics, but the discriminant analysis has been better applicable. This paper compares the judgment accuracy of these three types of models and tries to establish a more effective financial credit scoring method, to solve the problem of constructing China's credit scoring system model under the current Internet financial background.

References

Allen, F., Qian, J., & Qian, M. (2007). China's Financial System: Past, Present, and Future, Available at SSRN: https://ssrn.com/abstract=978485.

Allen, L., DeLong, G., & Saunders, A. (2004). Issues in the credit risk modeling of retail markets, Journal of Banking & Finance,28(4),727-752.

Anonymous. (2017). Chinese Statistical Yearbook. Retrieved from http://www.stats.gov.cn/tjsj/ndsj/2017/indexeh.htm

Berger, A.N., & Udell, G.F. (2002). Small business credit availability and relationship lending: The importance of bank organisational structure,The economic journal,112(477),32-53.

Bikker, J.A., & Haaf, K. (2000). Measures of competition and concentration in the banking industry: a review of the literature,Economic & Financial Modelling,1-46.

Chen, H.C., & Chen, Y.C. (2010). A comparative study of discrimination methods for credit scoring, The 40th International Conference on Computers & Industrial Engineering.

Creemers, R. (2018). China's Social Credit System: An Evolving Practice of Control, Available at SSRN: https://ssrn.com/abstract=3175792 or http://dx.doi.org/10.2139/ssrn.3175792.

Cheng, C., & Shuyang, O. (2014). The status quo and problems of the building of china's social credit system and suggestions,International Business and Management,8(2),169-173.

Dhillon, G., & Torkzadeh, G. (2006). Value-focused assessment of information system security in organizations,Information Systems Journal,6(3),293-314.

Durand, D. (1941). Appendix B: Application of the Method of Discriminant Functions to the Good-and Bad-Loan Samples,NBER Chapters, in: Risk Elements in Consumer Instalment Financing, Technical Edition, 125-142, National Bureau of Economic Research, Inc.

Day, G.S., Shocker, A.D., & Srivastava, R.K. (1978).Customer-oriented approaches to identifying product-markets,Journal of marketing,43(4),8-19.

Desai, V.S., Crook, J.N., & Overstreet, G.A, Jr. (1996). A comparison of neural networks and linear scoring models in the credit union environment,European Journal of Operational Research,95(1),24-37.

Dorronsoro, J.R., Ginel, F., Sgnchez, C., & Cruz, C.S. (1997). Neural fraud detection in credit card operations, IEEE Transactions on Neural Networks,8(4),827-834.

Ennew, C.T., & Binks, M.R. (1999). Impact of participative service relationships on quality, satisfaction and retention: an exploratory study,Journal of business research,46(2),121-132.

Eisenbeis, R.A. (1977). Pitfalls in the application of discriminant analysis in business, finance, and economics,The Journal of Finance,32(3),875-900.

Hsieh, N.C., & Hung, L.P. (2010). A data driven ensemble classifier for credit scoring analysis,Expert systems with Applications,37(1),534-545.

Huang, C.L., Chen, M.C., & Wang, CJ. (2007). Credit scoring with a data mining approach based on support vector machines,Expert systems with applications,33(4),847-856.

Hoff, K., & Stiglitz, J.E. (1990). Introduction: Imperfect information and rural credit markets: Puzzles and policy perspectives,The world bank economic review,4(3),235-250.

Hand, D.J., & Henley, W.E. (1997). Statistical classification methods in consumer credit scoring: a review,Journal of the Royal Statistical Society,160(3),523-547.

Han, K., Lee, Y., & Park, C. (2013). Legal frameworks and credit information systems in China, Korea, and S ingapore,Asian-Pacific Economic Literature,27(1),147-155.

Huang, Z., Lei, Y., & Shen, S. (2016). China’s personal credit reporting system in the internet finance era: challenges and opportunities,China Economic Journal,9(3),288-303.

Hu, Y., & Ge, Z. (2018). The Development Dilemma and Countermeasures of China's Personal Credit Industry in the Era of Large Data,ATCI 2018:International Conference on Applications and Techniques in Cyber Security and Intelligence,1023-1030.

Kostka, G. (2019). China's social credit systems and public opinion: Explaining high levels of approval,New Media & Society,21(7),1565-1593.

Lin, Z., Whinston, A.B., & Fan, S. (2015). Harnessing Internet finance with innovative cyber credit management, Financial Innovation,5,DOI:10.1186/s40854-015-0004-7.

Lachenbruch, P.A., & Goldstein, M. (1979). Discriminant analysis,Biometrics,35(1),69-85.

Li, M. (2017). Comparative data mining analysis of personal credit scoring models,Times Finance,23(6),295+298.

Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey,IEEE Communications Surveys & Tutorials,20(4),2595-2621.

Nwana, H.S. (1996). Software agents: An overview,The knowledge engineering review,11(3),205-244.

Ripley, B.D. (1994). Neural networks and related methods for classification,Journal of the Royal Statistical Society: Series B(Methodological),56(3),409-437.

Stiglitz, J.E. (1993). The role of the state in financial markets,The World Bank Economic Review,7(1),19-52.

Sachs, T., Tiong, R., & Wang, S. Qian. (2007). Analysis of political risks and opportunities in public private partnerships (PPP) in China and selected Asian countries: Survey results,Chinese Management Studies,1(2),126-148.

Shi, X., & He, X. (2015). The Study of Skew-logistic Model and Its Application in Credit Scoring,Journal of Applied Statistics and Management,34(6),1048-1056.

Sohn, S.Y., Kim, D.H., & Yoon, J.H. (2016). Technology credit scoring model with fuzzy logistic regression,Applied Soft Computing,43,150-158.

Su, H. (2018). The research of personal credit risk assessment based on random forest model(Master’s thesis, Hunan University, Changsha, China). Retrieved from http://www.hnu.edu.cn/.

Shi, Q. (2005). Research on a Mixed Two-Phase Personal Credit Scoring Model Based on Neural Network-Logistic Regression,Statistical Research,19(5),45-49,DOI:10.19343/j.cnki.11-1302/c.2005.05.011.

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.

West, D. (2000). Neural network credit scoring models,Computers & Operations Research,27(11-12),1131-1152.

Wiginton, J.C. (1980). A note on the comparison of logit and discriminant models of consumer credit behavior,Journal of Financial and Quantitative Analysis,15(3),757-770.

Wu, W. (2008). Dimensions of social capital and firm competitiveness improvement: The mediating role of information sharing,Journal of management studies,45(1),122-146.

Wind, Y. (1978). Issues and Advances in Segmentation Research,Journal of Marketing Research,15(3),317-337.

Xia, Y., Liu, C., Da, B., & Xie, F. (2018). A novel heterogeneous ensemble credit scoring model based on bstacking approach,Expert Systems with Applications,93,183-199.

Yu, L., Wang, S., & Lai, K. (2009). An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support,The case of credit scoring,European journal of operational research,95(3),942-959.

Yang, C. (2018). Multi-dimensional Optimal Selection Strategy for Credit Evaluation Methods,Statistics & Decision,34(21),80-85,DOI:10.13546/j.cnki.tjyjc.2018.21.019.

Yip, G.S., & McKern, B. (2016). China's next strategic advantage: From imitation to innovation,The MIT Press.

Published
2020-01-08
How to Cite
Zeng, H. (2020). A Comparison Study on the Era of Internet Finance China Construction of Credit Scoring System Model. Bangladesh Journal of Multidisciplinary Scientific Research, 2(1), 1-22. https://doi.org/10.46281/bjmsr.v2i1.453
Section
Research Paper/Theoretical Paper/Review Paper/Short Communication Paper