Financial assessment using neural networks

Corporate bankruptcy always brings about huge economic losses to management, stockholders, employees, customers, and others, together with a substantial social and economical cost to the nation. Therefore, a model predicting corporate failure would serve to reduce such losses by providing a pre-warning for decision makers. An early warning signal of probable failure will enable both management and investors to take preventive actions and shorten the length of time whereby losses are incurred. Thus, an accurate prediction of bankruptcy has become an important issue in finance. The study aims to apply Artificial Neural Networks (ANNs) technique for financial assessment of organizations and to evaluate bankruptcy conditions. This paper reviews the literature on Artificial Neural Network (ANN) and other important methods used for bankruptcy prediction, such as conventional statistical methods and soft computation methods, followed by a discussion of a systematic development process of ANN models. In this research, NN models with Back propagation learning algorithm are trained and tested using data from 50 organizations, the simulation results are encouraging, and the training and testing accuracy is over 97%.