A Machine Learning Approach for Micro-Credit Scoring

Ampountolas, Apostolos, Nyarko Nde, Titus, Date, Paresh and Constantinescu, Corina ORCID: 0000-0002-5219-3022 (2021) A Machine Learning Approach for Micro-Credit Scoring. RISKS, 9 (3). p. 50.

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Official URL: http://dx.doi.org/10.3390/risks9030050

Abstract

<jats:p>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.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	machine learning, micro-credit, micro-finance, credit risk, default probability, credit scoring, micro-lending
Depositing User:	Symplectic Admin
Date Deposited:	09 Mar 2021 14:29
Last Modified:	17 Mar 2024 18:01
DOI:	10.3390/risks9030050
Open Access URL:	https://www.mdpi.com/2227-9091/9/3/50
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3116854