Machine Learning Applied to Banking Supervision a Literature Review
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- Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
- Alessi, Lucia & Detken, Carsten, 2018.
"Identifying excessive credit growth and leverage,"
Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
- Alessi, Lucia, 2014. "Identifying Excessive Credit Growth and Leverage," Financial Stability Review, European Central Bank, vol. 1.
- Detken, Carsten & Alessi, Lucia, 2014. "Identifying excessive credit growth and leverage," Working Paper Series 1723, European Central Bank.
- Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2019. "Predicting European bank stress tests: Survival of the fittest," Global Finance Journal, Elsevier, vol. 39(C), pages 44-57.
- Tongyu Wang & Shangmei Zhao & Guangxiang Zhu & Haitao Zheng, 2021. "A machine learning-based early warning system for systemic banking crises," Applied Economics, Taylor & Francis Journals, vol. 53(26), pages 2974-2992, June.
- Le, Hong Hanh & Viviani, Jean-Laurent, 2018.
"Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios,"
Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
- Hong Hanh Le & Jean-Laurent Viviani, 2018. "Predicting bank failure: An improvement by implementing machine learning approach on classical financial ratios," Post-Print halshs-01615106, HAL.
- Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
- Atanu Sengupta & Sanjoy De, 2020. "Review of Literature," India Studies in Business and Economics, in: Assessing Performance of Banks in India Fifty Years After Nationalization, chapter 0, pages 15-30, Springer.
- Kupiec, Paul H., 2018.
"On the accuracy of alternative approaches for calibrating bank stress test models,"
Journal of Financial Stability, Elsevier, vol. 38(C), pages 132-146.
- Paul H. Kupiec, 2018. "On the accuracy of alternative approaches for calibrating bank stress test models," AEI Economics Working Papers 980152, American Enterprise Institute.
- Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
- Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
- Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
- Lee, In & Shin, Yong Jae, 2020. "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Elsevier, vol. 63(2), pages 157-170.
- Pompella, Maurizio & Dicanio, Antonio, 2017. "Ratings based Inference and Credit Risk: Detecting likely-to-fail Banks with the PC-Mahalanobis Method," Economic Modelling, Elsevier, vol. 67(C), pages 34-44.
- Andrés Alonso & José Manuel Carbó, 2020. "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers 2032, Banco de España.
- Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
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- Guerra, Pedro & Castelli, Mauro & Côrte-Real, Nadine, 2022. "Machine learning for liquidity risk modelling: A supervisory perspective," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 175-187.
- Umut Turksen & Vladlena Benson & Bogdan Adamyk, 2024. "Legal implications of automated suspicious transaction monitoring: enhancing integrity of AI," Journal of Banking Regulation, Palgrave Macmillan, vol. 25(4), pages 359-377, December.
- Pejman Peykani & Mostafa Sargolzaei & Mohammad Hashem Botshekan & Camelia Oprean-Stan & Amir Takaloo, 2023. "Optimization of Asset and Liability Management of Banks with Minimum Possible Changes," Mathematics, MDPI, vol. 11(12), pages 1-24, June.
- Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.
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Keywords
banking; supervision; risk assessment; machine learning; EWS;All these keywords.
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