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Credit Risk Assessment Using Decision Tree and Support Vector Machine Based Data Analytics

  • Abhijeet Guha RoyEmail author
  • Siddhaling Urolagin
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Credit risk assessment has become a growing necessity in the banking sector. Data mining techniques need to be deployed, in order to enable lenders to produce an efficient and objective estimation of a customer’s creditworthiness. The purpose of this paper is to propose a methodology that performs a two-level data processing using Random Forest and Support Vector Machine, to accurately pinpoint creditworthiness of the clients involved. The random forest will be utilized to create an accurate credit scoring model which will be further refined using the support vector machine. The proposed methodology will help achieve results with minimized false positives.

Keywords

Credit risk assessment Decision trees Random forest Support vector machine Data mining 

References

  1. 1.
    Kavitha, K.: Clustering loan applicants based on risk percentage using K-means clustering techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6(2), 162–166 (2016)Google Scholar
  2. 2.
    Sudhakar, M., Reddy, C.V.K.: Two step credit risk assessment model for retail bank loan applications using decision tree data mining technique. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 5(3), 705–718 (2016)Google Scholar
  3. 3.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan-Kaufmann Publishers, San Mateo, CA (1993)Google Scholar
  4. 4.
    Quinlan, J.: R, Discovering rules by induction from large collection of examples. In: Michie, D. (ed.) Expert Systems in the Micro Electronic Age. Edinburgh University Press, Edinburgh, UK (1979)Google Scholar
  5. 5.
    Han, J., Kamber, M., Pei, J.: Classification: basic concepts. In: Data Mining Concepts and Techniques, 3rd edn., pp. 330–350. Morgan-Kaufmann Publishers (2012)Google Scholar
  6. 6.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)Google Scholar
  7. 7.
    Han, J., Kamber, M., Pei, J.: Classification: advanced methods. In Data Mining Concepts and Techniques, 3rd edn., pp. 408–415. Morgan-Kaufmann Publishers (2012)Google Scholar
  8. 8.
    Tang, P.-N., Steinbach, M., Kumar, V.: Introduction to data mining. In: Classification: Alternative Techniques, Pearson Education, pp. 256–309 (2013)Google Scholar
  9. 9.
    Staglog (German Credit Data) Data Set. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data). Accessed 10 May 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.BITS Pilani Dubai CampusDubaiUAE

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