Predicting bank failure: An improvement by implementing machine learning approach on classical financial ratios

Hong Hanh Le and Jean-Laurent Viviani ()
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Hong Hanh Le: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique

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Abstract: This research compares the accuracy of two approaches: traditional statistical techniques and machine learning techniques, which attempt to predict the failure of banks. A sample of 3000 US banks (1438 failures and 1562 active banks) is investigated by two traditional statistical approaches (Discriminant analysis and Logistic regression) and three machine learning approaches (Artificial neural network, Support Vector Machines and k-nearest neighbors). For each bank, data were collected for a 5-year period before they become inactive. 31 financial ratios extracted from bank financial reports covered 5 main aspects: Loan quality, Capital quality, Operations efficiency, Profitability and Liquidity. The empirical result reveals that the artificial neural network and k-nearest neighbor methods are the most accurate.

Keywords: Failure; prediction; Intelligent; techniques; Artificial; neural; network; Support; vector; machines; K-nearest; neighbors; US; banks (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (19)

Published in Research in International Business and Finance, 2018, 44, pp.16-25. ⟨10.1016/j.ribaf.2017.07.104⟩

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Journal Article: Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-01615106

DOI: 10.1016/j.ribaf.2017.07.104

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