Skip to main content
View expanded cover

International Conference on Collaborative Computing: Networking, Applications and Worksharing

CollaborateCom 2016: Collaborate Computing: Networking, Applications and Worksharing pp 16–26Cite as

A Novel Hybrid Data Mining Framework for Credit Evaluation

  • 1559 Accesses

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

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-59288-6_2
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-59288-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.00
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.

Notes

  1. 1.

    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 

Download references

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.

Author information

Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Yatao Yang, Zibin Zheng, Chunzhen Huang & Kunmin Li

  2. Collaborative Innovation Center of High Performance Computing, National University of Defense Technology, Changsha, 410073, China

    Zibin Zheng

  3. Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau SAR

    Hong-Ning Dai

Authors
  1. Yatao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zibin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chunzhen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kunmin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hong-Ning Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong-Ning Dai .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Ao Zhou

Rights and permissions

Reprints and Permissions

Copyright information

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

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59287-9

  • Online ISBN: 978-3-319-59288-6

  • eBook Packages: Computer ScienceComputer Science (R0)