The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study

Abstract In this paper, we present experimental results of fuzzy clustering and two self-organizing neural networks used as classification tools for identifying potentially failing banks. We first describe the distinctive characteristics of fuzzy clustering algorithm, which provides probability of the likelihood of bank failure. We then perform the comparison between the results of the closest hard partitioning of fuzzy clustering and of two self-organizing neural networks and present our results as the ranking structure of relative bankruptcy likelihood. Our findings indicate that both the fuzzy clustering and self-organizing neural networks are promising classification tools for identifying potentially failing banks.

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