Advertisement

A Novel Hybrid Data Mining Framework for Credit Evaluation

  • Yatao Yang
  • Zibin Zheng
  • Chunzhen Huang
  • Kunmin Li
  • Hong-Ning DaiEmail author
Conference paper
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 

Notes

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.

References

  1. 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)CrossRefGoogle Scholar
  2. 2.
    Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. arXiv preprint arXiv:1603.02754 (2016)
  3. 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)CrossRefGoogle Scholar
  4. 4.
    Gray, J.B., Fan, G.: Classification tree analysis using TARGET. Comput. Stat. Data Anal. 52(3), 1362–1372 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Hsieh, N.C., Hung, L.P.: A data driven ensemble classifier for credit scoring analysis. Expert Syst. Appl. 37(1), 534–545 (2010)MathSciNetCrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefzbMATHGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Yatao Yang
    • 1
  • Zibin Zheng
    • 1
    • 2
  • Chunzhen Huang
    • 1
  • Kunmin Li
    • 1
  • Hong-Ning Dai
    • 3
    Email author
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Collaborative Innovation Center of High Performance ComputingNational University of Defense TechnologyChangshaChina
  3. 3.Faculty of Information TechnologyMacau University of Science and TechnologyTaipaMacau SAR

Personalised recommendations