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Neural network credit scoring model for micro enterprise financing in India

Author

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  • Sanjeev Mittal
  • Pankaj Gupta
  • K. Jain

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.

Suggested Citation

  • Sanjeev Mittal & Pankaj Gupta & K. Jain, 2011. "Neural network credit scoring model for micro enterprise financing in India," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 3(3), pages 224-242, October.
    • Handle: RePEc:eme:qrfmpp:v:3:y:2011:i:3:p:224-242
    DOI: 10.1108/17554171111176921
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    References listed on IDEAS

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    Cited by:

    1. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    2. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    3. Brad S. Trinkle & Amelia A. Baldwin, 2016. "Research Opportunities for Neural Networks: The Case for Credit," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 240-254, July.

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