ABSTRACT

Credit risk evaluation is an important and interesting problem in financial analysis domain. Credit risk evaluation has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. Several techniques like expert systems, decision tree etc. have been used for credit rating. However, these method have limitations of knowledge bottleneck, slow learning etc. recently artificial neural networks (ANN) has been proposed as the while-box models for classifying creditors. In this research work, I try to apply the artificial neural networks (ANN) and its genetic algorithms to design a credit risk evaluation system to discriminate good creditors from bad ones. The efficiency of classification is evaluated in terms of classification errors calculated from the actual classification made by the credit officers. The result of the experiment shows how the application of an artificial neural network system can support the creditworthiness evaluation of borrowers.