Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios

Citations

Cited by:

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8. Samuel Opoku & Kingsley Opoku Appiah & Prince Gyimah, 2024. "Can We Predict the Financial Distress of Banks in Sub-Saharan Africa?," SAGE Open, , vol. 14(3), pages 21582440241, August.
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14. Dominika Gajdosikova & Jakub Michulek, 2025. "Artificial Intelligence Models for Bankruptcy Prediction in Agriculture: Comparing the Performance of Artificial Neural Networks and Decision Trees," Agriculture, MDPI, vol. 15(10), pages 1-29, May.
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16. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
17. Buckmann, Marcus & Gallego Marquez, Paula & Gimpelewicz, Mariana & Kapadia, Sujit & Rismanchi, Katie, 2021. "The more the merrier? Evidence from the global financial crisis on the value of multiple requirements in bank regulation," Bank of England working papers 905, Bank of England.
18. Khan, Haroon ur Rashid & Bin Khidmat, Waqas & Hammouda, Amira & Muhammad, Tufail, 2023. "Machine learning in the boardroom: Gender diversity prediction using boosting and undersampling methods," Research in International Business and Finance, Elsevier, vol. 66(C).
19. Pham, Xuan T.T. & Ho, Tin H., 2021. "Using boosting algorithms to predict bank failure: An untold story," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 40-54.
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21. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
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24. Cullen F. Goenner, 2020. "Uncertain times and early predictions of bank failure," The Financial Review, Eastern Finance Association, vol. 55(4), pages 583-601, November.
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