Abstract—The failure and success of the Banking Industry depends largely on industry’s ability to properly evaluate credit risk. Credit Evaluation of any potential credit application has remained a challenge for Banks all over the world till today. This paper checks the applicability of one of the new integrated model on a sample data taken from Indian Banks. The integrated model is a combination model based on the techniques of Logistic Regression, Multilayer Perceptron Model, Radial Basis Neural Network, Support Vector Machine and Decision tree (C4.5) and compares the effectiveness of these techniques for credit approval process.
Index Terms—Credit evaluation, decision process, models.
S. U. Purohit is with Department of Mathematics, Kirti College and
Department of Technology and Mgmt, NMITD, Mumbai, India
V. Mahadevan is with Information Systems & eBusiness, Swinburne University of Technology, Melbourne, Australia. (e –mail: email@example.com)
A. N. Kulkarni is with College, Panvel Navi Mumbai (e-mail: firstname.lastname@example.org)
Cite: Seema U. Purohit, Venkatesh Mahadevan, and Anjali N. Kulkarni, "Credit Evaluation Model of Loan Proposals for Indian Banks," International Journal of Modeling and Optimization vol. 2, no. 4, pp. 529-534, 2012.