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Article

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

1
College of Engineering, Carnegie Mellon University Africa, Kigali BP 6150, Rwanda
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African Center of Excellence in Data Science, University of Rwanda, Kigali BP 4285, Rwanda
3
Oxford Man Institute of Quantitative Finance, Oxford University, Oxford OX2 6ED, UK
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(8), 180; https://doi.org/10.3390/jrfm13080180
Received: 11 March 2020 / Revised: 26 July 2020 / Accepted: 31 July 2020 / Published: 13 August 2020
(This article belongs to the Collection Machine Learning Applications in Finance)
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. View Full-Text
Keywords: financial inclusion; credit score; big data; machine learning; airtime financial inclusion; credit score; big data; machine learning; airtime
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MDPI and ACS Style

Dushimimana, B.; Wambui, Y.; Lubega, T.; McSharry, P.E. Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans. J. Risk Financial Manag. 2020, 13, 180. https://doi.org/10.3390/jrfm13080180

AMA Style

Dushimimana B, Wambui Y, Lubega T, McSharry PE. Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans. Journal of Risk and Financial Management. 2020; 13(8):180. https://doi.org/10.3390/jrfm13080180

Chicago/Turabian Style

Dushimimana, Bernard, Yvonne Wambui, Timothy Lubega, and Patrick E. McSharry. 2020. "Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans" Journal of Risk and Financial Management 13, no. 8: 180. https://doi.org/10.3390/jrfm13080180

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