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Modelling Credit Default in Microfinance—An Indian Case Study

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  • P. K. Viswanathan
  • S. K. Shanthi

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.

Suggested Citation

  • P. K. Viswanathan & S. K. Shanthi, 2017. "Modelling Credit Default in Microfinance—An Indian Case Study," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 16(3), pages 246-258, December.
    • Handle: RePEc:sae:emffin:v:16:y:2017:i:3:p:246-258
    DOI: 10.1177/0972652717722084
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    References listed on IDEAS

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    1. James R. Barth & Chen Lin & Clas Wihlborg (ed.), 2012. "Research Handbook on International Banking and Governance," Books, Edward Elgar Publishing, number 14045.
    2. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
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