Bank failure prediction using an accurate and interpretable neural fuzzy inference system

Article type: Research Article

Authors: Wang, Di^a | Quek, Chai^{a; *} | Ng, Geok See^b

Affiliations: [a] School of Computer Engineering, Nanyang Technological University, Singapore 639798 | [b] School of Information Systems Technology & Design, Singapore University of Technology & Design, Singapore 138682

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Bank failure prediction is an important study for regulators in the banking industry because the failure of a bank leads to devastating consequences. If bank failures are correctly predicted, early warnings can be sent to the responsible authorities for precaution purposes. Therefore, a reliable bank failure prediction or early warning system is invaluable to avoid adverse repercussion effects on other banks and to prevent drastic confidence losses in the society. In this paper, we propose a novel self-organizing neural fuzzy inference system, which functions as an early warning system of bank failures. The system performs accurately based on the auto-generated fuzzy inference rule base. More importantly, the simplified rule base possesses a high level of interpretability, which makes it much easier for human users to comprehend. Three sets of experiments are conducted on a publicly available database, which consists of 3635 United States banks observed over a 21-year period. The experimental results of our proposed model are encouraging in terms of both accuracy and interpretability when benchmarked against other prediction models.

Keywords: Automatic forecasting, bank failure prediction, early warning system, interpretability, fuzzy neural networks

DOI: 10.3233/AIC-160702

Journal: AI Communications, vol. 29, no. 4, pp. 477-495, 2016