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