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

Bernard Dushimimana, Yvonne Wambui, Timothy Lubega and Patrick E. McSharry
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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

JRFM, 2020, vol. 13, issue 8, 1-11

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.

Keywords: financial inclusion; credit score; big data; machine learning; airtime (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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