PREDICTING BANK FAILURES: A NEURAL NETWORK APPROACH

Empirical results show that neural network is a competitive method among existing ones in assessing the likelihood of bank failures, especially in reducing type I misclassification rate.

Abstract

The purpose of this paper is to present a neural network approach to predicting bank failures and to compare it with existing prediction methods. The task of constructing a prediction model is cast as one of training a network with a set of bankruptcy cases. Empirical results show that neural network is a competitive method among existing ones in assessing the likelihood of bank failures, especially in reducing type I misclassification rate. Issues relating to the potential and limitations of neural network as a modeling tool are also addressed.