Neural network credit scoring model for micro enterprise financing in India

Sanjeev Mittal, Pankaj Gupta and K. Jain

Qualitative Research in Financial Markets, 2011, vol. 3, issue 3, 224-242

Abstract: Purpose - 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. Design/methodology/approach - 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. Findings - 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. Originality/value - 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.

Keywords: Micro enterprises; Neural networks; Credit scoring model; Multilayer perceptron; Ability to pay; Credit risk; Small enterprise; Financial risk; Risk analysis (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:qrfmpp:v:3:y:2011:i:3:p:224-242

DOI: 10.1108/17554171111176921

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