Modelling Credit Default in Microfinance—An Indian Case Study

P. K. Viswanathan and S. K. Shanthi

Journal of Emerging Market Finance, 2017, vol. 16, issue 3, 246-258

Abstract: Credit score models have been successfully applied in a traditional credit card industry and by mortgage firms to determine defaulting customer from the non-defaulting customer. In the light of growing competition in the microfinance industry, over-indebtedness and other factors, the industry has come under increased regulatory supervision. Our study provides evidence from a large microfinance institutions (MFI) in India, and we have applied both the credit scoring method and neural network (NN) method and compared the results. In this article, we demonstrate the capability of credit scoring models for an Indian-based microfinance firm in terms of predicting default probability as well the relative importance of each of its associated drivers. A logistic regression model and NN have been used as the predictive analytic tools for sifting the key drivers of default.

Keywords: Logistic regression; probability of default; MFI; neural network (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:emffin:v:16:y:2017:i:3:p:246-258

DOI: 10.1177/0972652717722084

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