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

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.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:umkn-dsp01.int.unisa.ac.za:10500/111
Date January 2007
CreatorsMoonasar, Viresh
PublisherUniversity of South Africa
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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