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Credit risk analysis using artificial intelligence : evidence from a leading South African banking institution

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dc.contributor.author Moonasar, Viresh
dc.date.accessioned 2009-03-13T09:46:27Z
dc.date.available 2009-03-13T09:46:27Z
dc.date.issued 2007
dc.identifier.uri http://hdl.handle.net/10500/111
dc.description.abstract Credit risk analysis is an important topic in financial risk management. Financial institutions (e.g. commercial banks) that grant consumers credit need reliable models that can accurately detect and predict defaults. This research investigates the ability of artificial neural networks as a decision support system that can automatically detect and predict “bad” credit risks based on customers demographic, biographic and behavioural characteristics. The study focuses specifically on the learning vector quantization neural network algorithm. This thesis contains a short overview of credit scoring models, an introduction to artificial neural networks and their applications and presents the performance evaluation results of a credit risk detection model based on learning vector quantization networks. en_US
dc.language.iso en en_US
dc.publisher University of South Africa en_US
dc.subject Technologies for competitiveness en_US
dc.subject Technology en_US
dc.title Credit risk analysis using artificial intelligence : evidence from a leading South African banking institution en_US
dc.type Thesis en_US


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