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A Novel Hybrid Data Mining Framework for Credit Evaluation

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 201)

Abstract

Internet loan business has received extensive attentions recently. How to provide lenders with accurate credit scoring profiles of borrowers becomes a challenge due to the tremendous amount of loan requests and the limited information of borrowers. However, existing approaches are not suitable to Internet loan business due to the unique features of individual credit data. In this paper, we propose a unified data mining framework consisting of feature transformation, feature selection and hybrid model to solve the above challenges. Extensive experiment results on realistic datasets show that our proposed framework is an effective solution.

Keywords

  • Credit evaluation
  • Data mining
  • Internet finance

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Notes

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    http://www.cashbus.com/.

References

  1. Angelini, E., di Tollo, G., Roli, A.: A neural network approach for credit risk evaluation. Q. Rev. Econ. Finan. 48(4), 733–755 (2008)

    CrossRef  Google Scholar 

  2. Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. arXiv preprint arXiv:1603.02754 (2016)

  3. Chen, Y.W., Lin, C.J.: Combining svms with various feature selection strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction, pp. 315–324. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  4. Gray, J.B., Fan, G.: Classification tree analysis using TARGET. Comput. Stat. Data Anal. 52(3), 1362–1372 (2008)

    MathSciNet  CrossRef  MATH  Google Scholar 

  5. Hsieh, N.C., Hung, L.P.: A data driven ensemble classifier for credit scoring analysis. Expert Syst. Appl. 37(1), 534–545 (2010)

    MathSciNet  CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  7. Koutanaei, F.N., Sajedi, H., Khanbabaei, M.: A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. J. Retail. Consum. Serv. 27, 11–23 (2015)

    CrossRef  Google Scholar 

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

    CrossRef  MATH  Google Scholar 

  9. Pang, S.L., Gong, J.Z.: C5. 0 classification algorithm and application on individual credit evaluation of banks. Syst. Eng. Theory Pract. 29(12), 94–104 (2009)

    CrossRef  Google Scholar 

  10. Wang, Y., Wang, S., Lai, K.K.: A new fuzzy support vector machine to evaluate credit risk. IEEE Trans. Fuzzy Syst. 13(6), 820–831 (2005)

    CrossRef  Google Scholar 

  11. Yap, B.W., Ong, S.H., Husain, N.H.M.: Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Syst. Appl. 38(10), 13274–13283 (2011)

    CrossRef  Google Scholar 

  12. Yu, L., Wang, S., Lai, K.K.: Credit risk assessment with a multistage neural network ensemble learning approach. Expert Syst. Appl. 34(2), 1434–1444 (2008)

    CrossRef  Google Scholar 

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Acknowledgment

The work described in this paper was supported by the National Key Research and Development Program (2016YFB1000101), the National Natural Science Foundation of China under (61472338), the Fundamental Research Funds for the Central Universities, and Macao Science and Technology Development Fund under Grant No. 096/2013/A3.

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Correspondence to Hong-Ning Dai .

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Yang, Y., Zheng, Z., Huang, C., Li, K., Dai, HN. (2017). A Novel Hybrid Data Mining Framework for Credit Evaluation. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-59288-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59287-9

  • Online ISBN: 978-3-319-59288-6

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