Relational neural evolution approach to bank failure prediction

Relational neural networks as a concept offers a unique opportunity for improving classification accuracy by exploiting relational structure in data. The premise is that a relational classification technique, which uses information implicit in relationships, should classify more accurately than techniques that only examine objects in isolation. In this paper, we study the use of relational neural networks for predicting bank failure. Alongside classical financial ratios normally used as predictor variables, we introduced new relational variables for the network. The relational neural network structure, specified as a combination of feed forward and recurrent neural networks, is determined by bank data through neuro-evolution. We discuss empirical results comparing performance of the relational approach to standard propositional methods used for bank failure prediction.