A Machine Learning Approach for Micro-Credit Scoring

Apostolos Ampountolas, Titus Nyarko Nde, Paresh Date and Corina Constantinescu
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Apostolos Ampountolas: Department of Mathematics, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
Titus Nyarko Nde: African Institute for Mathematical Sciences (AIMS), Kigali P.O. Box 7150, Rwanda
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

Risks, 2021, vol. 9, issue 3, 1-20

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.

Keywords: machine learning; micro-credit; micro-finance; credit risk; default probability; credit scoring; micro-lending (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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