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Credit scoring using machine learning : an application of deep learning

Dissertation (MSc (Financial Engineering))--University of Pretoria, 2021.

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Other Authors: Mare, Eben
Format: Thesis
Language:English
Published: University of Pretoria 2021
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access_status_str Open Access
author2 Mare, Eben
author_browse Mare, Eben
author_facet Mare, Eben
collection Thesis
dc_rights_str_mv © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Dissertation (MSc (Financial Engineering))--University of Pretoria, 2021.
format Thesis
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institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:37:30.755Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2021
publishDateRange 2021
publishDateSort 2021
publisher University of Pretoria
publisherStr University of Pretoria
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source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/80736 Credit scoring using machine learning : an application of deep learning Mare, Eben alanwrigglesworth5@gmail.com Wrigglesworth, Alan Financial Engineering Credit scoring Machine learning Deep learning Credit risk UCTD Dissertation (MSc (Financial Engineering))--University of Pretoria, 2021. Over the last couple of years, we have seen much advancement in mathematical analysis and computational capabilities. This advancement, coupled with the increased availability of big data, has made it possible to commoditise machines and enable them to act as risk managers and financial analysts. In this dissertation, we will briefly review machine learning and consumer credit risk/scoring. We look at different methods and models proposed in the literature and thoroughly explore the mathematical theory behind deep learning. We then apply this knowledge and other recent advancements in the field to build a fully connected feed-forward deep neural network using open source credit card default data from a large Taiwanese retail bank. Our deep neural network aims to improve upon other models proposed in the literature regarding accuracy and other metrics such as ROC-AUC, Cohen's Kappa, precision, recall and F1-score. We then conclude that deep neural networks are competitive in terms of performance compared to other machine learning models and outperform traditional models. We highlight the potential that deep learning has yet to achieve in finance and pay close attention to the hurdles faced in complexity, development costs and regulatory roadblocks. Mathematics and Applied Mathematics MSc (Financial Engineering) Unrestricted 2021-07-06T09:21:27Z 2021-07-06T09:21:27Z 2021-09 2021 Dissertation Wrigglesworth, A 2021, Credit scoring using machine learning: an application of deep learning, Masters Dissertation, University of Pretoria, Pretoria, viewed yymmdd http://hdl.handle.net/2263/80736 S2021 http://hdl.handle.net/2263/80736 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle Financial Engineering
Credit scoring
Machine learning
Deep learning
Credit risk
UCTD
Credit scoring using machine learning : an application of deep learning
title Credit scoring using machine learning : an application of deep learning
title_full Credit scoring using machine learning : an application of deep learning
title_fullStr Credit scoring using machine learning : an application of deep learning
title_full_unstemmed Credit scoring using machine learning : an application of deep learning
title_short Credit scoring using machine learning : an application of deep learning
title_sort credit scoring using machine learning an application of deep learning
topic Financial Engineering
Credit scoring
Machine learning
Deep learning
Credit risk
UCTD
url http://hdl.handle.net/2263/80736