Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/22410
Title: | A Machine Learning Approach for Micro-Credit Scoring |
Authors: | Date, P Ampountolas, A Constantinescu, C Nyarko Nde, T |
Advisors: | https:// creativecommons.org/licenses/by/ 4.0/ |
Keywords: | machine learning;micro-credit;micro-finance;credit risk;default probability;credit scoring;micro-lending |
Issue Date: | 2021 |
Publisher: | MDPI |
Citation: | Ampountolas, A., Nyarko Nde, T., Date, P. and Constantinescu, C. (2021) ‘A Machine Learning Approach for Micro-Credit Scoring’, Risks. MDPI AG, 9(3), 50, pp. 1-20. doi: 10.3390/risks9030050. |
Abstract: | © 2021 by the authors. 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. |
URI: | https://bura.brunel.ac.uk/handle/2438/22410 |
DOI: | https://doi.org/10.3390/risks9030050 |
Appears in Collections: | Dept of Mathematics Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | 642.37 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.