[
1. Addo, P.M., Guegan, D. and Hassani B. (2018). Credit Risk Analysis Using Machine and Deep Learning Models. Risks, 6: 38. https://doi.org/10.3390/risks602003810.3390/risks6020038
]Search in Google Scholar
[
2. Aldasoro, I., Gambacorta, L., Giudici P. and Leach T. (2020). Operational and cyber risks in the financial sector. Bank for International Settlements, BIS Working Papers No 840, February 2020
]Search in Google Scholar
[
3. Adrian, T. (2018). Risk Management and Regulation. International Monetary Fund, Departmental Paper No. 18/1310.5089/9781484343913.087
]Search in Google Scholar
[
4. Basel Committee on Banking Supervision (2000). Principles for the Management of Credit Risk, June 2006, Bank for International Settlements.
]Search in Google Scholar
[
5. Basel Committee on Banking Supervision (2006). International Convergence of Capital Measurement and Capital Standards – A revised Framework, June 2006, Bank for International Settlements.
]Search in Google Scholar
[
6. Basel Committee on Banking Supervision (2017). Basel III: Finalising post-crisis reforms, December 2017, Bank for International Settlements.
]Search in Google Scholar
[
7. Basel Committee on Banking Supervision (2019a). Basel III Monitoring Report, October 2019. Bank for International Settlements.
]Search in Google Scholar
[
8. Basel Committee on Banking Supervision (2019b). Seventeenth progress report on adoption of the Basel regulatory framework, October 2019. Bank for International Settlements.
]Search in Google Scholar
[
9. Basel Committee on Banking Supervision (2019c). The Basel Framework, Bank for International Settlements.
]Search in Google Scholar
[
10. Basel Committee on Banking Supervision (2020). “Governors and Heads of Supervision announce deferral of Basel III implementation to increase operational capacity of banks and supervisors to respond to Covid-19”, Press release, 27 March 2020, Retrieved from: https://www.bis.org/press/p200327.htm [Accessed 30 May 2020].
]Search in Google Scholar
[
11. European Central Bank (2018). ECB Guide to the internal liquidity adequacy assessment process (ILAAP), November 2018
]Search in Google Scholar
[
12. European Central Bank (2020). Supervisory Banking Statistics, Second quarter 2020, October 2020.
]Search in Google Scholar
[
13. Fabris, N., (2020). Financial stability and climate change, Journal of Central Banking Theory and Practice, Volume 9, No. 3: pp. 27-43, https://doi.org/10.2478/jcbtp-2020-003410.2478/jcbtp-2020-0034
]Search in Google Scholar
[
14. Financial Stability Board (2017). Artificial Intelligence and Machine Learning in Financial Services, Market Developments and Financial Stability Implications. November 2017, Retrieved from: http://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-service/ [Accessed 29 May 2020].
]Search in Google Scholar
[
15. Grubišić, Z., Kamenković, S. and Kaličanin, T. (2021). Comparative Analysis of the Banking Sector Competitiveness in Serbia and Montenegro, Journal of Central Banking Theory and Practice, Volume 10, No. 1: 75-91, https://doi.org/10.2478/jcbtp-2021-000410.2478/jcbtp-2021-0004
]Search in Google Scholar
[
16. Jacobs, M., (2018). The validation of machine-learning models for the stress testing of credit risk, Journal of Risk Management in Financial Institutions, Volume 11, number 3.10.20944/preprints201803.0157.v1
]Search in Google Scholar
[
17. Kolanovic, M. and Krishnamachari, R., (2017), “Big Data and AI Strategies: Machine Learning and Alternative Data Approach to Investing,” JP Morgan, May 2017, Retrieved from: https://faculty.sites.uci.edu/pjorion/files/2018/05/JPM-2017-MachineLearningInvestments.pdf [Accessed 18 October 2020].
]Search in Google Scholar
[
18. Leo, M., Sharma, S. and Maddulety, K. (2019). Machine Learning in Banking Risk Management: A Literature Review, Risks 7: 29. https://doi.org/10.3390/risks701002910.3390/risks7010029
]Search in Google Scholar
[
19. Lessmann, S., Baesens, B., Seow, H. and Thomas, L.C., (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research, European Journal of Operational Research, 2015, vol. 247, issue 1, 124-136. https://doi.org/10.1016/j.ejor.2015.05.03010.1016/j.ejor.2015.05.030
]Search in Google Scholar
[
20. Milojević, N., (2014). Optimal banking and other financial business for the economic growth of Serbia, Journal of Central Banking Theory and Practice, Volume 3, No. 2: 61-83, https://doi.org/10.2478/jcbtp-2014-001110.2478/jcbtp-2014-0011
]Search in Google Scholar
[
21. Milojević, N., (2016). Contemporary Challenges in the Banking Risk Management, Business Economics, Year X, No. 2/2016, Vol. XIX: 66-85, https://doi.org/10.5937/poseko10-1341810.5937/poseko10-13418
]Search in Google Scholar
[
22. Milojević, N. and Redzepagic, S. (2020a). Current Trends and Future Progress in the Banking Risk and Capital Management: Worldwide Experience and Republic of Serbia Case Study, Economic Analysis, 2020, Vol. 53, No. 2: 79-94, https://doi.org/10.28934/ea.20.53.2.pp79-94
]Search in Google Scholar
[
23. Milojević, N. and Redzepagic, S. (2020b). Prospects of the Artificial Intelligence and Machine Learning Application in the Banking Risk Management, Paper presented at the International Scientific Conference: Global Economic Trends – Challenges and Opportunities, November 30, 2020, Belgrade Banking Academy – Faculty of Banking, Belgrade, Published book of abstracts.
]Search in Google Scholar
[
24. Panagiotis, A., Efthymios, K., Anastasios-Taxiarchis, K. and Athanasios, P. (2020). GARCH Modelling of High-capitalization Cryptocurrencies' Impacts During Bearish Markets, Journal of Central Banking Theory and Practice, Volume 9, No. 3: pp. 87-106, https://doi.org/10.2478/jcbtp-2020-003810.2478/jcbtp-2020-0038
]Search in Google Scholar
