Rationalization of Deep Neural Networks in Credit Scoring
| dc.contributor.author | Dastile, Xolani Collen | |
| dc.contributor.supervisor | Celik, Turgay | |
| dc.date.accessioned | 2024-11-10T12:57:13Z | |
| dc.date.available | 2024-11-10T12:57:13Z | |
| dc.date.issued | 2023-07 | |
| dc.description | A thesis submitted in fulfillment for the degree of Doctor of Philosophy, to the Faculty of Science, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | Machine learning and deep learning, which are subfields of artificial intelligence, are undoubtedly pervasive and ubiquitous technologies of the 21st century. This is attributed to the enhanced processing power of computers, the exponential growth of datasets, and the ability to store the increasing datasets. Many companies are now starting to view their data as an asset, whereas previously, they viewed it as a by-product of business processes. In particular, banks have started to harness the power of deep learning techniques in their day-to-day operations; for example, chatbots that handle questions and answers about different products can be found on banks’ websites. One area that is key in the banking sector is the credit risk department. Credit risk is the risk of lending money to applicants and is measured using credit scoring techniques that profile applicants according to their risk. Deep learning techniques have the potential to identify and separate applicants based on their lending risk profiles. Nevertheless, a limitation arises when employing deep learning techniques in credit risk, stemming from the fact that these techniques lack the ability to provide explanations for their decisions or predictions. Hence, deep learning techniques are coined as non-transparent models. This thesis focuses on tackling the lack of transparency inherent in deep learning and machine learning techniques to render them suitable for adoption within the banking sector. Different statistical, classic machine learning, and deep learning models’ performances were compared qualitatively and quantitatively. The results showed that deep learning techniques outperform traditional machine learning models and statistical models. The predictions from deep learning techniques were explained using state-of-the-art explanation techniques. A novel model-agnostic explanation technique was also devised, and credit-scoring experts assessed its validity. This thesis has shown that different explanation techniques can be relied upon to explain predictions from deep learning and machine learning techniques. | |
| dc.description.sponsorship | Banking Sector Education and Training Authority (BANKSETA). | |
| dc.description.submitter | MMM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier | 0000-0003-4628-9420 | |
| dc.identifier.citation | Celik, Turgay. (2023). Rationalization of Deep Neural Networks in Credit Scoring. [PhD thesis, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/42298 | |
| dc.identifier.uri | https://hdl.handle.net/10539/42298 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Computer Science and Applied Mathematics | |
| dc.subject | Artificial Intelligence (AI) | |
| dc.subject | Deep Neural Networks | |
| dc.subject | Credit Scoring | |
| dc.subject | Explainability | |
| dc.subject | Counterfactual Explanations | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-9: Industry, innovation and infrastructure | |
| dc.title | Rationalization of Deep Neural Networks in Credit Scoring | |
| dc.type | Thesis |