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Predicting bank insolvencies using machine learning techniques

Citations

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Cited by:

  1. Petr Jakubik & Bogdan Gabriel Moinescu, 2023. "What is the optimal capital ratio implying a stable European banking system?," International Finance, Wiley Blackwell, vol. 26(3), pages 324-343, December.
  2. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.
  3. Taoushianis, Zenon, 2025. "Bankruptcy prediction with fractional polynomial transformation of financial ratios," European Journal of Operational Research, Elsevier, vol. 327(2), pages 690-702.
  4. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
  5. Citterio, Alberto & King, Timothy, 2023. "The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress," Finance Research Letters, Elsevier, vol. 51(C).
  6. Jiang, Cuiqing & Lyu, Ximei & Yuan, Yufei & Wang, Zhao & Ding, Yong, 2022. "Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1086-1099.
  7. Peng, Qiao & McKillop, Donal & Quinn, Barry & Liu, Kailong, 2025. "Modeling and predicting failure in US credit unions," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1237-1259.
  8. Aleksandra Szymura, 2022. "Risk Assessment of Polish Joint Stock Companies: Prediction of Penalties or Compensation Payments," Risks, MDPI, vol. 10(5), pages 1-22, May.
  9. Abdel Latef Anouze & Imad Bou-Hamad, 2021. "Inefficiency source tracking: evidence from data envelopment analysis and random forests," Annals of Operations Research, Springer, vol. 306(1), pages 273-293, November.
  10. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
  11. Ali Ben Mrad & Amine Lahiani & Salma Mefteh-Wali & Nada Mselmi, 2025. "Predicting bank inactivity: A comparative analysis of machine learning techniques for imbalanced data," Annals of Operations Research, Springer, vol. 351(1), pages 937-963, August.
  12. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
  13. Evžen Kočenda & Ichiro Iwasaki, 2022. "Bank survival around the world: A meta‐analytic review," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 108-156, February.
  14. Bank for International Settlements, 2025. "Lessons on supervisory effectiveness - a literature review," BCBS Working Papers 45, Bank for International Settlements.
  15. Wang, Bo & Yan, Ruolan & Chen, Yang, 2025. "Predicting abnormal capital flow episodes with machine learning methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 103(C).
  16. Mohamed Elhoseny & Noura Metawa & Gabor Sztano & Ibrahim M. El-hasnony, 2025. "Deep Learning-Based Model for Financial Distress Prediction," Annals of Operations Research, Springer, vol. 345(2), pages 885-907, February.
  17. Yavuz GÜL & Serpil ALTINIRMAK, 2025. "Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 10(1), pages 107-126.
  18. Owoo, Natalia & Odei-Mensah, Jones, 2025. "Hierarchical clustering-based early warning model for predicting bank failures: Insights from Ghana's financial sector reforms (2017–2019)," Research in International Business and Finance, Elsevier, vol. 77(PB).
  19. Florin-Sebastian Duma & Rodica-Ioana Lung, 2026. "Short-term prediction of the Romanian stock market benchmark index using genetic programming," Digital Finance, Springer, vol. 8(2), pages 1-24, June.
  20. Katsafados, Apostolos G. & Leledakis, George N. & Panagiotou, Nikolaos P. & Pyrgiotakis, Emmanouil G., 2024. "Can central bankers’ talk predict bank stock returns? A machine learning approach," MPRA Paper 122899, University Library of Munich, Germany.
  21. Blanco-Oliver Antonio & Lara-Rubio Juan & Irimia-Diéguez Ana & Liébana-Cabanillas Francisco, 2024. "Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
  22. Durand, Pierre & Le Quang, Gaëtan, 2022. "Banks to basics! Why banking regulation should focus on equity," European Journal of Operational Research, Elsevier, vol. 301(1), pages 349-372.
  23. Ruize Gao & Shaoze Cui & Yu Wang & Wei Xu, 2025. "Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
  24. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
  25. Jahanzaib Alvi & Imtiaz Arif, 2025. "Credit Scorecards & Forecasting Default Events – A Novel Story of Non-financial Listed Companies in Pakistan," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 32(4), pages 1459-1485, December.
  26. Angilella, Silvia & Doumpos, Michalis & Pappalardo, Maria Rosaria & Zopounidis, Constantin, 2024. "Assessing the performance of banks through an improved sigma-mu multicriteria analysis approach," Omega, Elsevier, vol. 127(C).
  27. João Gabriel Moraes Souza & Daniel Tavares Castro & Yaohao Peng & Ivan Ricardo Gartner, 2024. "A Machine Learning-Based Analysis on the Causality of Financial Stress in Banking Institutions," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1857-1890, September.
  28. Philipp Borchert & Kristof Coussement & Arno de Caigny & Jochen de Weerdt, 2023. "Extending business failure prediction models with textual website content using deep learning," Post-Print hal-03976762, HAL.
  29. Wei Miao & Jad Beyhum & Jonas Striaukas & Ingrid Van Keilegom, 2025. "High-dimensional censored MIDAS logistic regression for corporate survival forecasting," Papers 2502.09740, arXiv.org, revised Feb 2026.
  30. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
  31. Aykut Ekinci & Safa Sen, 2024. "Forecasting Bank Failure in the U.S.: A Cost-Sensitive Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3161-3179, December.
  32. Xi, Haomeng & Wang, Jizhou, 2024. "Social governance, family happiness, and financial inclusion," Finance Research Letters, Elsevier, vol. 61(C).
  33. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
  34. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
  35. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
  36. Do, Quang Hung, 2024. "Predicting Efficiency of Commercial Banks in Vietnam: A DEA and Machine Learning Approach," Advances in Decision Sciences, Asia University, Taiwan, vol. 28(4), pages 120-143.
  37. Liu, Wanan & Zou, Yao & Liu, Baokang & Tao, Jiacheng & Lan, Xingyu & Xia, Meng, 2026. "Explainable adaptive ensemble learning with imbalance mitigation for manufacturing sector financial risk warning," Chaos, Solitons & Fractals, Elsevier, vol. 202(P2).
  38. Jakubik, Petr & Moinescu, Bogdan Gabriel, 2025. "Tailored microprudential recommendations for bank profit retention using a risk tolerance framework," International Review of Economics & Finance, Elsevier, vol. 98(C).
  39. Rui Luan & Ping Xu, 2024. "Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization," Information Resources Management Journal (IRMJ), IGI Global Scientific Publishing, vol. 37(1), pages 1-21, January.
  40. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
  41. Xinlin Wang & Zs'ofia Kraussl & Mats Brorsson, 2024. "Datasets for Advanced Bankruptcy Prediction: A survey and Taxonomy," Papers 2411.01928, arXiv.org.
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