Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89642
Title: Bank failure prediction using an accurate and interpretable neural fuzzy inference system
Authors: Wang, Di
Ng, Geok See
Quek, Chai
Keywords: DRNTU::Engineering::Computer science and engineering
Bank Failure Prediction
Automatic Forecasting
Issue Date: 2016
Source: Wang, D., Quek, C., & Ng, G. S. (2016). Bank failure prediction using an accurate and interpretable neural fuzzy inference system. AI Communications, 29(4), 477-495. doi:10.3233/AIC-160702
Series/Report no.: AI Communications
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.
URI: https://hdl.handle.net/10356/89642
http://hdl.handle.net/10220/47108
ISSN: 0921-7126
DOI: http://dx.doi.org/10.3233/AIC-160702
Rights: © 2016 IOS Press. This is the author created version of a work that has been peer reviewed and accepted for publication by AI Communications, IOS Press. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.3233/AIC-160702].
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
Appears in Collections:SCSE Journal Articles

Files in This Item:
File Description SizeFormat 
AIcom2016.pdf378.07 kBAdobe PDFThumbnail
View/Open

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.