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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan-Kaufmann Publishers, San Mateo, CA (1993)
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)
Han, J., Kamber, M., Pei, J.: Classification: basic concepts. In: Data Mining Concepts and Techniques, 3rd edn., pp. 330–350. Morgan-Kaufmann Publishers (2012)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Han, J., Kamber, M., Pei, J.: Classification: advanced methods. In Data Mining Concepts and Techniques, 3rd edn., pp. 408–415. Morgan-Kaufmann Publishers (2012)
Tang, P.-N., Steinbach, M., Kumar, V.: Introduction to data mining. In: Classification: Alternative Techniques, Pearson Education, pp. 256–309 (2013)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Roy, A.G., Urolagin, S. (2019). Credit Risk Assessment Using Decision Tree and Support Vector Machine Based Data Analytics. In: Mateev, M., Poutziouris, P. (eds) Creative Business and Social Innovations for a Sustainable Future. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-01662-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-01662-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01661-6
Online ISBN: 978-3-030-01662-3
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)