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Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance

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Abstract

Supply chain finance (SCF) becomes more important for small- and medium-sized enterprises (SMEs) due to global credit crunch, supply chain financing woes and tightening credit criteria for corporate lending. Currently, predicting SME credit risk is significant for guaranteeing SCF in smooth operation. In this paper, we apply six methods, i.e., one individual machine learning (IML, i.e., decision tree) method, three ensemble machine learning methods [EML, i.e., bagging, boosting, and random subspace (RS)], and two integrated ensemble machine learning methods (IEML, i.e., RS–boosting and multi-boosting), to predict SMEs credit risk in SCF and compare the effectiveness and feasibility of six methods. In the experiment, we choose the quarterly financial and non-financial data of 48 listed SMEs from Small and Medium Enterprise Board of Shenzhen Stock Exchange, six listed core enterprises (CEs) from Shanghai Stock Exchange and three listed CEs from Shenzhen Stock Exchange during the period of 2012–2013 as the empirical samples. Experimental results reveal that the IEML methods acquire better performance than IML and EML method. In particular, RS–boosting is the best method to predict SMEs credit risk among six methods.

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References

  1. More D, Basu P (2013) Challenges of supply chain finance: a detailed study and a hierarchical model based on the experiences of an Indian firm. Bus Process Manag J 19(4):624–647

    Article  Google Scholar 

  2. Fairchild A (2005) Intelligent matching: integrating efficiencies in the financial supply chain. Supply Chain Manag Int J 10(4):244–248

    Article  Google Scholar 

  3. Knox A (2005) Electronic payment: the missing link in supply chain efficiency. J Financ Transform Mark Imperfection 14:16–18

    Google Scholar 

  4. Hofmann E, Belin O (eds) (2011) Supply chain finance solutions. Springer, Berlin

    Google Scholar 

  5. Seifert RW, Seifert D (2011) Financing the chain. Int Commer Rev 10(1):32–44

    Article  MATH  Google Scholar 

  6. Sopranzetti BJ (1999) Selling accounts receivable and the underinvestment problem. Q Rev Econ Finance 39(2):291–301

    Article  Google Scholar 

  7. Wuttke DA, Blome C, Henke M (2013) Focusing the financial flow of supply chains: an empirical investigation of financial supply chain management. Int J Prod Econ 145(2):773–789

    Article  Google Scholar 

  8. Wang G, Ma J (2011) Study of corporate credit risk prediction based on integrating boosting and random subspace. Expert Syst Appl 38(11):13871–13878

    Google Scholar 

  9. Huang Z, Chen H, Hsu CJ, Chen WH, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37(4):543–558

    Article  Google Scholar 

  10. Brill J (1998) The importance of credit scoring models in improving cash flow and collections. Bus Credit 100(1):16–17

    Google Scholar 

  11. Thomas LC (2000) A survey of credit and behavioral scoring: forecasting financial risks of lending to customers. Int J Forecast 16(2):149–172

    Article  Google Scholar 

  12. Jiang Y (2009) Credit scoring model based on the decision tree and the simulated annealing algorithm. In: 2009 World congress on computer science and information engineering. Los Angeles, CA, pp 18–22

  13. West D (2000) Neural network credit scoring models. Comput Oper Res 27(11):1131–1152

    Article  MATH  Google Scholar 

  14. Nanni L, Lumini A (2009) An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Syst Appl 36(2):3028–3033

    Article  Google Scholar 

  15. Tsai C, Wu J (2008) Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst Appl 34(4):2639–2649

    Article  Google Scholar 

  16. Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40(2):159–196

    Article  Google Scholar 

  17. Zhou Z (ed) (2009) Ensemble. In: Encyclopedia of database systems. Springer, New York

  18. Hall MA, Smith LA (1998) Practical feature subset selection for machine learning. In: Computer science ’98 proceedings of the 21st Australasian computer science conference ACSC ’98, Berlin, pp 181–191

  19. Quinlan JR (ed) (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Francisco, CA

    Google Scholar 

  20. Maclin R, Opitz D (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198

    MATH  Google Scholar 

  21. Fu ZW, Golden BL, Lele S, Raghavan S, Wasil E (2006) Diversification for better classification trees. Comput Oper Res 33(11):3185–3202

    Article  MATH  Google Scholar 

  22. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MATH  Google Scholar 

  23. Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36(1–2):105–139

    Article  Google Scholar 

  24. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Thirteenth international conference on machine learning, San Francisco, pp 148–156

  25. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844

    Article  Google Scholar 

  26. Xiong X, Ma J, Zhao W (2009) Credit risk analysis of supply chain finance. Nankai Bus Rev 12(4):92–98

    Google Scholar 

  27. Powers DMW (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. J Mach Learn Technol 2(1):37–63

    MathSciNet  Google Scholar 

  28. Hosmer JDW, Lemeshow S, Sturdivant RX (eds) (2013) Applied logistic regression. Wiley, Hoboken, NJ

    MATH  Google Scholar 

  29. Yap BW, Ong SH, Husain NHM (2011) Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Syst Appl 38(10):13274–13283

    Article  Google Scholar 

  30. Kürüm E, Yildirak K, Weber GW (2012) A classification problem of credit risk rating investigated and solved by optimization of ROC curve. Cent Eur J Oper Res 20(3):529–557

    Article  MathSciNet  MATH  Google Scholar 

  31. Bekhet HA, Eletter SFK (2014) Credit risk assessment model for Jordanian commercial banks: neural scoring approach. Rev Dev Finance 4(1):20–28

    Article  Google Scholar 

  32. Alexander F, Daniel M (2007) Measuring word alignment quality for statistical machine translation. Comput Linguist 33(3):293–303

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant Nos. 71373072 and 71501066; the China Scholarship Council under Grant No. 201506135022; Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20130161110031; and Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant No. 71221001.

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Authors and Affiliations

  1. College of Business Administration, Hunan University, Changsha, 410082, China

    You Zhu, Chi Xie, Gang-Jin Wang & Xin-Guo Yan

  2. Center of Finance and Investment Management, Hunan University, Changsha, 410082, China

    Chi Xie & Gang-Jin Wang

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  1. You Zhu
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  2. Chi Xie
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  3. Gang-Jin Wang
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  4. Xin-Guo Yan
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Correspondence to Chi Xie.

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Zhu, Y., Xie, C., Wang, GJ. et al. Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance. Neural Comput & Applic 28 (Suppl 1), 41–50 (2017). https://doi.org/10.1007/s00521-016-2304-x

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  • DOI: https://doi.org/10.1007/s00521-016-2304-x

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