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Explaining neural networks used for modeling credit risk
Thesis(MSc.)--Stellenbosch University, 2021.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2021
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| _version_ | 1867613815052435456 |
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| access_status_str | Open Access |
| author | Mohamed, Zhunaid |
| author2 | Visser, Willem |
| author_browse | Mohamed, Zhunaid Visser, Willem |
| author_facet | Visser, Willem Mohamed, Zhunaid |
| author_sort | Mohamed, Zhunaid |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis(MSc.)--Stellenbosch University, 2021. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/110117 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:42:07.859Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/110117 Explaining neural networks used for modeling credit risk Mohamed, Zhunaid Visser, Willem Herbst, B. M. Hoffman, McElory Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Computer Science. UCTD Machine Learning Neural networks (Computer science) Credit control -- Automation Thesis(MSc.)--Stellenbosch University, 2021. ENGLISH ABSTRACT: Calculating risk before providing loans is a common problem that credit companies face. The most common solution is credit employees manually assessing the risk of a client by reviewing their credit portfolios. This can be a slow process and is prone to human error. Recently credit companies have been adopting machine learning techniques in order to automate this process, however this has been limited to linear techniques due to interpretability being a strict requirement. Neural networks could provide significant improvements to the way credit risk is modeled, however these are still seen as black boxes. In this work we compare various techniques which claim to provide interpretability into these black boxes. We also use these techniques to provide explanations on a neural network trained on credit data that has been provided to us. The financial assistance of the National Research Foundation(NRF) towards this research is hereby ac- knowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF. Masters 2021-03-04T19:59:51Z 2021-04-21T14:41:11Z 2021-03-04T19:59:51Z 2021-04-21T14:41:11Z 2021-03 Thesis http://hdl.handle.net/10019.1/110117 en_ZA Stellenbosch University xi, 104 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | UCTD Machine Learning Neural networks (Computer science) Credit control -- Automation Mohamed, Zhunaid Explaining neural networks used for modeling credit risk |
| title | Explaining neural networks used for modeling credit risk |
| title_full | Explaining neural networks used for modeling credit risk |
| title_fullStr | Explaining neural networks used for modeling credit risk |
| title_full_unstemmed | Explaining neural networks used for modeling credit risk |
| title_short | Explaining neural networks used for modeling credit risk |
| title_sort | explaining neural networks used for modeling credit risk |
| topic | UCTD Machine Learning Neural networks (Computer science) Credit control -- Automation |
| url | http://hdl.handle.net/10019.1/110117 |
| work_keys_str_mv | AT mohamedzhunaid explainingneuralnetworksusedformodelingcreditrisk |