Bank failure prediction using an accurate and interpretable neural fuzzy inference system
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 ban...
| Κύριοι συγγραφείς: | , , |
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| Άλλοι συγγραφείς: | |
| Μορφή: | Journal Article |
| Γλώσσα: | English |
| Έκδοση: |
2018
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| Θέματα: | |
| Διαθέσιμο Online: | https://hdl.handle.net/10356/89642 http://hdl.handle.net/10220/47108 |
| _version_ | 1826126609972723712 |
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| author | Wang, Di Ng, Geok See Quek, Chai |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Wang, Di Ng, Geok See Quek, Chai |
| author_sort | Wang, Di |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:55:08Z |
| format | Journal Article |
| id | ntu-10356/89642 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:55:08Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/896422020-03-07T11:48:52Z Bank failure prediction using an accurate and interpretable neural fuzzy inference system Wang, Di Ng, Geok See Quek, Chai School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Bank Failure Prediction Automatic Forecasting 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. Accepted version 2018-12-19T08:38:14Z 2019-12-06T17:30:07Z 2018-12-19T08:38:14Z 2019-12-06T17:30:07Z 2016 2016 Journal Article 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 0921-7126 https://hdl.handle.net/10356/89642 http://hdl.handle.net/10220/47108 10.3233/AIC-160702 187590 en AI Communications © 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]. 19 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Bank Failure Prediction Automatic Forecasting Wang, Di Ng, Geok See Quek, Chai Bank failure prediction using an accurate and interpretable neural fuzzy inference system |
| title | Bank failure prediction using an accurate and interpretable neural fuzzy inference system |
| title_full | Bank failure prediction using an accurate and interpretable neural fuzzy inference system |
| title_fullStr | Bank failure prediction using an accurate and interpretable neural fuzzy inference system |
| title_full_unstemmed | Bank failure prediction using an accurate and interpretable neural fuzzy inference system |
| title_short | Bank failure prediction using an accurate and interpretable neural fuzzy inference system |
| title_sort | bank failure prediction using an accurate and interpretable neural fuzzy inference system |
| topic | DRNTU::Engineering::Computer science and engineering Bank Failure Prediction Automatic Forecasting |
| url | https://hdl.handle.net/10356/89642 http://hdl.handle.net/10220/47108 |
| work_keys_str_mv | AT wangdi bankfailurepredictionusinganaccurateandinterpretableneuralfuzzyinferencesystem AT nggeoksee bankfailurepredictionusinganaccurateandinterpretableneuralfuzzyinferencesystem AT quekchai bankfailurepredictionusinganaccurateandinterpretableneuralfuzzyinferencesystem |