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Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans

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  • Bernard Dushimimana

    (College of Engineering, Carnegie Mellon University Africa, Kigali BP 6150, Rwanda)

  • Yvonne Wambui

    (College of Engineering, Carnegie Mellon University Africa, Kigali BP 6150, Rwanda)

  • Timothy Lubega

    (College of Engineering, Carnegie Mellon University Africa, Kigali BP 6150, Rwanda)

  • Patrick E. McSharry

    (College of Engineering, Carnegie Mellon University Africa, Kigali BP 6150, Rwanda
    African Center of Excellence in Data Science, University of Rwanda, Kigali BP 4285, Rwanda
    Oxford Man Institute of Quantitative Finance, Oxford University, Oxford OX2 6ED, UK)

Abstract

Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed. Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%. Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels of default and therefore allowing for larger volumes of customers.

Suggested Citation

  • Bernard Dushimimana & Yvonne Wambui & Timothy Lubega & Patrick E. McSharry, 2020. "Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans," JRFM, MDPI, vol. 13(8), pages 1-11, August.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:8:p:180-:d:398327
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    References listed on IDEAS

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    Cited by:

    1. Omar H. Fares & Irfan Butt & Seung Hwan Mark Lee, 2023. "Utilization of artificial intelligence in the banking sector: a systematic literature review," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 835-852, December.
    2. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.

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