Banks Bankruptcy Risk Forecasting with Application of Computational Intelligence Methods

The problem of banks bankruptcy risk forecasting under uncertainty is considered. For its solution the application of computational intelligence methods fuzzy neural networks ANFIS, TSK and inductive modeling method FGMDH is suggested and explored. The experimental investigations were carried out and estimation of the efficiency of the suggested methods is performed at the problems of bankruptcy risk forecasting for Ukrainian banks. The comparative experiments with rating system CAMELS and matrix method were carried out. In general, the comparative analysis had shown that fuzzy forecasting methods and techniques give better results than conventional crisp methods for forecasting bankruptcy risk. The set of most relevant bank financial factors for bankruptcy risk forecasting was determined and estimated.