A Machine Learning Approach for Micro-Credit Scoring
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 classi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/9/3/50 |
| _version_ | 1827698237562159104 |
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| author | Apostolos Ampountolas Titus Nyarko Nde Paresh Date Corina Constantinescu |
| author_facet | Apostolos Ampountolas Titus Nyarko Nde Paresh Date Corina Constantinescu |
| author_sort | Apostolos Ampountolas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:25:39Z |
| format | Article |
| id | doaj.art-ebac228d563a43f8b74d852cf1453412 |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-10T13:25:39Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-ebac228d563a43f8b74d852cf14534122023-11-21T09:42:46ZengMDPI AGRisks2227-90912021-03-01935010.3390/risks9030050A Machine Learning Approach for Micro-Credit ScoringApostolos Ampountolas0Titus Nyarko Nde1Paresh Date2Corina Constantinescu3Department of Mathematics, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UKAfrican Institute for Mathematical Sciences (AIMS), Kigali P.O. Box 7150, RwandaDepartment of Mathematics, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UKDepartment of Mathematical Sciences, Institute for Financial and Actuarial Mathematics, University of Liverpool, Liverpool L69 3BX, UKIn 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.https://www.mdpi.com/2227-9091/9/3/50machine learningmicro-creditmicro-financecredit riskdefault probabilitycredit scoring |
| spellingShingle | Apostolos Ampountolas Titus Nyarko Nde Paresh Date Corina Constantinescu A Machine Learning Approach for Micro-Credit Scoring Risks machine learning micro-credit micro-finance credit risk default probability credit scoring |
| title | A Machine Learning Approach for Micro-Credit Scoring |
| title_full | A Machine Learning Approach for Micro-Credit Scoring |
| title_fullStr | A Machine Learning Approach for Micro-Credit Scoring |
| title_full_unstemmed | A Machine Learning Approach for Micro-Credit Scoring |
| title_short | A Machine Learning Approach for Micro-Credit Scoring |
| title_sort | machine learning approach for micro credit scoring |
| topic | machine learning micro-credit micro-finance credit risk default probability credit scoring |
| url | https://www.mdpi.com/2227-9091/9/3/50 |
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