Neural networks in bank insolvency prediction
Nov 30, 2009
DOI:
Published in: International Journal of Computer Science and Network Security
he current paper aims to predict bank insolvency before the bankruptcy using neural networks, to enable all parties to take remedial action. Artificial neural networks are widely used in finance and insurance problems. Artificial neural networks are used to predict the insolvency. The back propagation network and the Kohonen self-organizing map (SOM) are used as the representative types for supervised and unsupervised artificial neural networks respectively. The results of applying the artificial neural networks methodology to predict financial distress based upon selected financial ratios show abilities of the network to learn the patterns corresponding to financial distress of the bank. In all cases, the percent correctly classified in the simulation sample by the feed-forward back propagation network is above 92 percent. After simulate the SOM network the percent correctly classified is above 94 percent. In spite of the limited data used in this study, artificial neural networks show significant signs for providing early warning signals and solvency monitoring. In addition, it is obvious from the results that SOM gives better results than feed-forward back propagation network
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