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

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.