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Using Supervised Learning Methods to Credit Score Informal Merchants
Access to working capital is a significant challenge for the informal retail sector, where better financial products are not easily accessible. This study aims to address this issue by developing data-driven credit scoring models for informal merchants using supervised learning methods. The study us...
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| Format: | Thesis |
| Language: | English |
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School of Economics
2024
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| _version_ | 1867613212261744640 |
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| access_status_str | Open Access |
| author | Kazimoto, Derick |
| author2 | Georg, Co-Pierre |
| author_browse | Georg, Co-Pierre Kazimoto, Derick |
| author_facet | Georg, Co-Pierre Kazimoto, Derick |
| author_sort | Kazimoto, Derick |
| collection | Thesis |
| description | Access to working capital is a significant challenge for the informal retail sector, where better financial products are not easily accessible. This study aims to address this issue by developing data-driven credit scoring models for informal merchants using supervised learning methods. The study uses data collected from Nomanini, a financial technology company that facilitates loans to informal merchants in Lesotho. The objective of the study is to help Nomanini develop accurate credit scoring models to reduce the risk of lending money to potential defaulters. Logistic regression and support vector machines were used as supervised learning methods to predict the default behavior of merchants. Six (6) logistic regression models and twelve (12) support vector machine models were evaluated based on their default predictive power. The best-performing model was a logistic regression model that used a merchant's credit history as the only feature, resulting in a Gini coefficient of 0.6143. The study's findings can help Nomanini determine the creditworthiness of merchants more accurately and reduce the risk of lending money to defaulters. This, in turn, can help increase access to working capital to support and grow small businesses in the informal sector. In conclusion, the study highlights the potential of using supervised learning methods to develop credit scoring models for informal merchants and contribute towards reducing the financial gap in the informal African economy. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/39587 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:33.381Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | School of Economics |
| publisherStr | School of Economics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/39587 Using Supervised Learning Methods to Credit Score Informal Merchants Kazimoto, Derick Georg, Co-Pierre Economics Access to working capital is a significant challenge for the informal retail sector, where better financial products are not easily accessible. This study aims to address this issue by developing data-driven credit scoring models for informal merchants using supervised learning methods. The study uses data collected from Nomanini, a financial technology company that facilitates loans to informal merchants in Lesotho. The objective of the study is to help Nomanini develop accurate credit scoring models to reduce the risk of lending money to potential defaulters. Logistic regression and support vector machines were used as supervised learning methods to predict the default behavior of merchants. Six (6) logistic regression models and twelve (12) support vector machine models were evaluated based on their default predictive power. The best-performing model was a logistic regression model that used a merchant's credit history as the only feature, resulting in a Gini coefficient of 0.6143. The study's findings can help Nomanini determine the creditworthiness of merchants more accurately and reduce the risk of lending money to defaulters. This, in turn, can help increase access to working capital to support and grow small businesses in the informal sector. In conclusion, the study highlights the potential of using supervised learning methods to develop credit scoring models for informal merchants and contribute towards reducing the financial gap in the informal African economy. 2024-05-06T13:58:49Z 2024-05-06T13:58:49Z 2023 2024-05-06T13:25:30Z Thesis / Dissertation Masters MPhil http://hdl.handle.net/11427/39587 eng application/pdf School of Economics Faculty of Commerce |
| spellingShingle | Economics Kazimoto, Derick Using Supervised Learning Methods to Credit Score Informal Merchants |
| thesis_degree_str | Master's |
| title | Using Supervised Learning Methods to Credit Score Informal Merchants |
| title_full | Using Supervised Learning Methods to Credit Score Informal Merchants |
| title_fullStr | Using Supervised Learning Methods to Credit Score Informal Merchants |
| title_full_unstemmed | Using Supervised Learning Methods to Credit Score Informal Merchants |
| title_short | Using Supervised Learning Methods to Credit Score Informal Merchants |
| title_sort | using supervised learning methods to credit score informal merchants |
| topic | Economics |
| url | http://hdl.handle.net/11427/39587 |
| work_keys_str_mv | AT kazimotoderick usingsupervisedlearningmethodstocreditscoreinformalmerchants |