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A Machine Learning Approach for Micro-Credit Scoring

Author

Listed:
  • Apostolos Ampountolas

    (Department of Mathematics, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
    Boston University, Boston, MA 02215, USA
    These authors contributed equally to this work.)

  • Titus Nyarko Nde

    (African Institute for Mathematical Sciences (AIMS), Kigali P.O. Box 7150, Rwanda
    These authors contributed equally to this work.)

  • Paresh Date

    (Department of Mathematics, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK)

  • Corina Constantinescu

    (Department of Mathematical Sciences, Institute for Financial and Actuarial Mathematics, University of Liverpool, Liverpool L69 3BX, UK)

Abstract

In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various credit categories. We demonstrate that off-the-shelf multi-class classifiers such as random forest algorithms can perform this task very well, using readily available data about customers (such as age, occupation, and location). This presents inexpensive and reliable means to micro-lending institutions around the developing world with which to assess creditworthiness in the absence of credit history or central credit databases.

Suggested Citation

  • Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:3:p:50-:d:513405
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    References listed on IDEAS

    as
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    Cited by:

    1. Anil Kumar & Suneel Sharma & Mehregan Mahdavi, 2021. "Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review," Risks, MDPI, vol. 9(11), pages 1-15, October.
    2. Corina Constantinescu & Julia Eisenberg, 2021. "Special Issue “Interplay between Financial and Actuarial Mathematics”," Risks, MDPI, vol. 9(8), pages 1-3, July.
    3. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
    4. Christian Kurniawan & Xiyu Deng & Adhiraj Chakraborty & Assane Gueye & Niangjun Chen & Yorie Nakahira, 2022. "A Learning and Control Perspective for Microfinance," Papers 2207.12631, arXiv.org, revised Dec 2022.
    5. Guner Altan & Server Demirci, 2022. "Credit Scoring on Cash Flow Table with Machine Learning: XGBoost Approach," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 397-424, July.

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