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http://bura.brunel.ac.uk/handle/2438/22410
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DC Field | Value | Language |
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dc.contributor.advisor | https:// creativecommons.org/licenses/by/ 4.0/ | - |
dc.contributor.author | Date, P | - |
dc.contributor.author | Ampountolas, A | - |
dc.contributor.author | Constantinescu, C | - |
dc.contributor.author | Nyarko Nde, T | - |
dc.date.accessioned | 2021-03-12T19:15:06Z | - |
dc.date.available | 2021-03-12T19:15:06Z | - |
dc.date.issued | 2021 | - |
dc.identifier.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. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22410 | - |
dc.description.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. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | - |
dc.subject | machine learning | en_US |
dc.subject | micro-credit | en_US |
dc.subject | micro-finance | en_US |
dc.subject | credit risk | en_US |
dc.subject | default probability | en_US |
dc.subject | credit scoring | en_US |
dc.subject | micro-lending | en_US |
dc.title | A Machine Learning Approach for Micro-Credit Scoring | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/risks9030050 | - |
dc.relation.isPartOf | Risks | - |
pubs.publication-status | Published | - |
pubs.volume | 9 | - |
dc.identifier.eissn | 2227-9091 | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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FullText.pdf | 642.37 kB | Adobe PDF | View/Open |
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