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An evidence-based credit evaluation ensemble framework for online retail SMEs

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Abstract

The lack of standardized financial statements makes it difficult to determine the credit ratings of small and medium-sized enterprises (SMEs). Focusing on this problem, we construct an ensemble framework based on evidence theory. First, we change the sale amount to cash flow lift through a difference table. Then, we analyse consumer comments using the high-frequency lexical sentiment degree. Finally, we combine the two results with an orthogonal sum according to the principle of evidence theory. Based on this framework, we take an online candy company, “Da Bai Tu” in Tmall, as a case to illustrate the application of this framework. Based on experiments with 50 candy SMEs, the degree scores of the framework and Tmall stores are consistent in a one-way ANOVA. The framework effectively combines objective sales records and subjective comments; thus, it can solve the difficulty in credit evaluation for SMEs.

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References

  1. Abellán J, Castellano JG (2017) A comparative study on base classifiers in ensemble methods for credit scoring. Expert Syst Appl 73:1–10

    Article  Google Scholar 

  2. Abellán J, Mantas CJ (2014) Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Syst Appl 41(8):3825–3830

    Article  Google Scholar 

  3. Adams M, Hense AV, ter Hofstede A (2021) Extensible ontology-based views for business process models. Knowl Inf Syst 63(10):2763–2789

    Article  Google Scholar 

  4. Ala’Raj M, Abbod MF (2016) Classifiers consensus system approach for credit scoring. Knowl-Based Syst 104:89–105

    Article  Google Scholar 

  5. Altman EI (2018) A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies. J Credit Risk 14(4):1–34

    Article  Google Scholar 

  6. Bellotti T, Crook J (2009) Support vector machines for credit scoring and discovery of significant features. Expert Syst Appl 36(2):3302–3308

    Article  Google Scholar 

  7. Cassar G, Ittner CD, Cavalluzzo KS (2015) Alternative information sources and information asymmetry reduction: Evidence from small business debt. J Account Econ 59(2–3):242–263

    Article  Google Scholar 

  8. Chen S, Wang Y, Shi H, Zhang M, Lin Y (2018) Evidential reasoning with discrete belief structures. Inf Fusion 41:91–104

    Article  Google Scholar 

  9. Chuang C, Lin R (2009) Constructing a reassigning credit scoring model. Expert Syst Appl 36(2):1685–1694

    Article  Google Scholar 

  10. Du Y, Zhong J (2021) Generalized combination rule for evidential reasoning approach and Dempster–Shafer theory of evidence. Inf Sci 547:1201–1232

    Article  MathSciNet  MATH  Google Scholar 

  11. Finlay S (2010) Credit scoring for profitability objectives. Eur J Oper Res 202(2):528–537

    Article  MATH  Google Scholar 

  12. Gül S, Kabak Ö, Topcu I (2018) A multiple criteria credit rating approach utilizing social media data. Data Knowl Eng 116:80–99

    Article  Google Scholar 

  13. Guo G, Zhu F, Chen E, Liu Q, Wu L, Guan C (2016) From footprint to evidence: an exploratory study of mining social data for credit scoring. ACM Trans Web (TWEB) 10(4):1–38

    Article  Google Scholar 

  14. Han L, Han L, Zhao H (2013) Orthogonal support vector machine for credit scoring. Eng Appl Artif Intell 26(2):848–862

    Article  Google Scholar 

  15. Harris T (2015) Credit scoring using the clustered support vector machine. Expert Syst Appl 42(2):741–750

    Article  Google Scholar 

  16. Hou W, Wang X, Zhang H, Wang J, Li L (2020) A novel dynamic ensemble selection classifier for an imbalanced data set: an application for credit risk assessment. Knowl-Based Syst 208:106462

    Article  Google Scholar 

  17. Huang C, Chen M, Wang C (2007) Credit scoring with a data mining approach based on support vector machines. Expert Syst Appl 33(4):847–856

    Article  Google Scholar 

  18. Hussain SF, Bashir S (2016) Co-clustering of multi-view datasets. Knowl Inf Syst 47(3):545–570

    Article  Google Scholar 

  19. Kim H, Arguello J (2017) Evaluation of features to predict the usefulness of online reviews. In: Proceedings of the 80th Annual Meeting of the Association of Information Science and Technology (ASIST'17)

  20. Kulkarni SV, Dhage SN (2019) Advanced credit score calculation using social media and machine learning. J Intell Fuzzy Syst 36(3):2373–2380

    Article  Google Scholar 

  21. Lefevre E, Colot O, Vannoorenberghe P (2002) Belief function combination and conflict management. Inf Fusion 3(2):149–162

    Article  Google Scholar 

  22. Lessmann S, Baesens B, Seow H, Thomas LC (2015) Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur J Oper Res 247(1):124–136

    Article  MATH  Google Scholar 

  23. Liang D, Tsai C, Dai A, Eberle W (2018) A novel classifier ensemble approach for financial distress prediction. Knowl Inf Syst 54(2):437–462

    Article  Google Scholar 

  24. Liu DH, Li J, Du B, Chang J, Gao R, Wu YJ (2021) A hybrid neural network approach to combine textual information and rating information for item recommendation. Knowl Inf Syst 63(3):621–646

    Article  Google Scholar 

  25. Liu D, Wang S, Tomovic MM, Zhang C (2020) An evidence theory based model fusion method for degradation modeling and statistical analysis. Inf Sci 532:33–60

    Article  MathSciNet  MATH  Google Scholar 

  26. Ma W, Jiang Y, Luo X (2019) A flexible rule for evidential combination in Dempster–Shafer theory of evidence. Appl Soft Comput 85:105512

    Article  Google Scholar 

  27. Maldonado S, Pérez J, Bravo C (2017) Cost-based feature selection for support vector machines: an application in credit scoring. Eur J Oper Res 261(2):656–665

    Article  MathSciNet  MATH  Google Scholar 

  28. Marqués AI, García V, Sánchez JS (2012) Exploring the behaviour of base classifiers in credit scoring ensembles. Expert Syst Appl 39(11):10244–10250

    Article  Google Scholar 

  29. Ong C, Huang J, Tzeng G (2005) Building credit scoring models using genetic programming. Expert Syst Appl 29(1):41–47

    Article  Google Scholar 

  30. Paleologo G, Elisseeff A, Antonini G (2010) Subagging for credit scoring models. Eur J Oper Res 201(2):490–499

    Article  Google Scholar 

  31. Serrano-Cinca C, Gutiérrez-Nieto B (2016) The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decis Support Syst 89:113–122

    Article  Google Scholar 

  32. Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton, p c1976

    Book  Google Scholar 

  33. Šušteršič M, Mramor D, Zupan J (2009) Consumer credit scoring models with limited data. Expert Syst Appl 36(3):4736–4744

    Article  Google Scholar 

  34. Teng G, He C, Xiao J, Jiang X (2013) Customer credit scoring based on HMM/GMDH hybrid model. Knowl Inf Syst 36(3):731–747

    Article  Google Scholar 

  35. 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 

  36. Verbraken T, Bravo C, Weber R, Baesens B (2014) Development and application of consumer credit scoring models using profit-based classification measures. Eur J Oper Res 238(2):505–513

    Article  MathSciNet  MATH  Google Scholar 

  37. Wang G, Hao J, Ma J, Jiang H (2011) A comparative assessment of ensemble learning for credit scoring. Expert Syst Appl 38(1):223–230

    Article  Google Scholar 

  38. Wang G, Ma J, Huang L, Xu K (2012) Two credit scoring models based on dual strategy ensemble trees. Knowl-Based Syst 26:61–68

    Article  Google Scholar 

  39. Xia Y, Liu N, Liu C, Li Y (2017) A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst Appl 78:225–241

    Article  Google Scholar 

  40. Xiao Z, Xiao H, Wang Y (2016) Ensemble classification based on supervised clustering for credit scoring. Appl Soft Comput 43:73–86

    Article  Google Scholar 

  41. Xiao J, Zhou X, Zhong Y, Xie L, Gu X, Liu D (2020) Cost-sensitive semi-supervised selective ensemble model for customer credit scoring. Knowl-Based Syst 189:105118

    Article  Google Scholar 

  42. Yager RR (2011) On the fusion of imprecise uncertainty measures using belief structures. Inf Sci 181(15):3199–3209

    Article  MathSciNet  MATH  Google Scholar 

  43. Zhang H, Petitjean F, Buntine W (2020) Bayesian network classifiers using ensembles and smoothing. Knowl Inf Syst 62(9):3457–3480

    Article  Google Scholar 

  44. Zhang D, Zhou X, Leung SCH, Zheng J (2010) Vertical bagging decision trees model for credit scoring. Expert Syst Appl 37(12):7838–7843

    Article  Google Scholar 

  45. Zhao Z, Xu S, Kang BH, Kabir MMJ, Liu Y, Wasinger R (2015) Investigation and improvement of multi-layer perception neural networks for credit scoring. Expert Syst Appl 42(7):3508–3516

    Article  Google Scholar 

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant No. 72101279), the Visiting Scholar Grant Program of China Scholarship Council for Han (No. 201806495014) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 100081, China

    Lu Han & Shuting Li

  2. School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA

    Arcot Rajasekar

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  1. Lu Han
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  2. Arcot Rajasekar
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  3. Shuting Li
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Correspondence to Lu Han.

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Han, L., Rajasekar, A. & Li, S. An evidence-based credit evaluation ensemble framework for online retail SMEs. Knowl Inf Syst 64, 1603–1623 (2022). https://doi.org/10.1007/s10115-022-01682-9

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