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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/111 |
| Date | January 2007 |
| Creators | Moonasar, Viresh |
| Publisher | University of South Africa |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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