Prediction of financial strength ratings using machine learning and conventional techniques

Abstract

Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007-09 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks’ financial strength ratings (FSRs) group membership using machine learning and conventional techniques. Here we use five different statistical techniques, namely CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression. We also use three different evaluation criteria namely average correct classification rate, misclassification cost and gains charts. Our data is collected from Bankscope database for the Middle Eastern commercial banks by reference to the first decade in the 21st Century. Our findings show that when predicting bank FSRs during the period 2007-2009, discriminant analysis is surprisingly superior to all other techniques used in this paper. When only machine learning techniques are used, CHAID outperform other techniques. In addition, our findings highlight that when a random sample is used to predict bank FSRs, CART outperform all other techniques. Our evaluation criteria have confirmed our findings and both CART and discriminant analysis are superior to other techniques in predicting bank FSRs. This has implications for Middle Eastern banks as we would suggest that improving their bank FSR can improve their presence in the market.