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Neural network credit scoring model for micro enterprise financing in India

Sanjeev Mittal (School of Management Studies, Guru Gobind Singh Indraprastha University, New Delhi, India)
Pankaj Gupta (Centre for Management Studies, Jamia Millia Islamia University, New Delhi, India)
K. Jain (Delhi Financial Corporation, New Delhi, India)

Qualitative Research in Financial Markets

ISSN: 1755-4179

Article publication date: 4 October 2011




Quantitative methods known as scoring models have been traditionally developed for credit granting decisions using statistical procedures. The purpose of this paper is to develop a non‐parametric credit scoring model for micro enterprises that are not maintaining balance sheets, and without having a track record of performance and other credit‐worthy parameters.


Multilayer perceptron procedure is used to evaluate credit reliability in three classes of risk, i.e. bad risk credit, foreclosed risk credit and good risk credit.


The development of a neural network model for micro enterprises facilitates bankers and financial institutions in credit granting decisions in an automatic manner in the Indian context.


This study applies comprehensive information on parameters of financial package prepared by Indian financial institutions and banks to micro enterprises to design a credit risk model. This model, instead of categorizing borrowers in terms of their “ability to pay”, attempts a solution to the unsolved problem of credit availability to micro enterprises in an Indian context, having no past performance track record.



Mittal, S., Gupta, P. and Jain, K. (2011), "Neural network credit scoring model for micro enterprise financing in India", Qualitative Research in Financial Markets, Vol. 3 No. 3, pp. 224-242.



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Copyright © 2011, Emerald Group Publishing Limited

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