Open Access
Issue
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
Article Number 04018
Number of page(s) 11
Section AI and Advanced Applications
DOI https://doi.org/10.1051/itmconf/20257004018
Published online 23 January 2025
  1. N. Milojević, S. Redzepagic, Prospects of artificial intelligence and machine learning application in banking risk management. J. Cent. Bank. Theory Pract. 10,3, 41-57 (2021). https://doi.org/10.2478/jcbtp-2021-0023 [CrossRef] [Google Scholar]
  2. A. Mashrur, W. Luo, N. A. Zaidi, A. Robles-Kelly, Machine learning for financial risk management: A survey. IEEE Access. 8, 203203-203223 (2020). https://doi.org/10.1109/ACCESS.2020.3036322 [Google Scholar]
  3. M. Leo, S. Sharma, K. Maddulety, Machine learning in banking risk management: A literature review. Risks, 7, 29 (2019). https://doi.org/10.3390/risks7010029 [CrossRef] [Google Scholar]
  4. N. Bussmann, P. Giudici, D. Marinelli, J. Papenbrock, Explainable machine learning in credit risk management. Comput. Econ. 57, 203-216 (2021). https://doi.org/10.1007/s10614-020-10042-0 [CrossRef] [Google Scholar]
  5. J. Mulvey, D. Rosenbaum, B. Shetty, Strategic financial risk management and operations research. Eur. J. Oper. Res. 97, 1-16 (1997). https://doi.org/10.1016/S0377-2217(96)00222-6 [CrossRef] [Google Scholar]
  6. S. Aziz, M. Dowling, AI and machine learning for risk management. Disrupting Finance, 33-50 (2019). https://doi.org/10.2139/ssrn.3201337 [CrossRef] [Google Scholar]
  7. G. D’Agostini, Bayesian inference in processing experimental data: principles and basic applications. Rep. Prog. Phys. 66, 1383-1420 (2003). https://doi.org/10.1088/0034-4885/66/9/201 [CrossRef] [Google Scholar]
  8. D. E. Rumelhart, B. Widrow, M. A. Lehr, The basic ideas in neural networks. Communications of the ACM. 37.3. 175247-175256 (1994). https://doi.org/10.1145/175247.175256 [Google Scholar]
  9. L. P. Kaelbling, M. L. Littman, A. W. Moore, Reinforcement learning: A survey. J. Artif. Intell. Res. 4, 237-285 (1996). https://doi.org/10.1613/jair.301 [CrossRef] [Google Scholar]
  10. E. M. H. Lin, E. W. Sun, M.-T. Yu, Behavioral data-driven analysis with Bayesian method for risk management of financial services. Int. J. Prod. Econ. 228, 107737 (2020). https://doi.org/10.1016/j.ijpe.2020.107737 [CrossRef] [Google Scholar]
  11. C. W. S. Chen, R. H. Gerlach, E. M. H. Lin, W. C. W. Lee, Bayesian forecasting for financial risk management, pre and post the global financial crisis. J. Forecast. 34.8, 661-687 (2012). https://doi.org/10.1002/for.1237 [CrossRef] [MathSciNet] [Google Scholar]
  12. Q. Feng, H. Chen, R. Jiang, Analysis of early warning of corporate financial risk via deep learning artificial neural network. Microprocess. Microsyst. 82, 104387 (2021). https://doi.org/10.1016/j.micpro.2021.104387 [CrossRef] [Google Scholar]
  13. X. Li, J. Wang, C. Yang, Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy. Neural Comput. Appl. 35, 2045-2058 (2023). https://doi.org/10.1007/s00521-022-07377-0 [CrossRef] [Google Scholar]
  14. A. Kim, Y. Yang, S. Lessmann, T. Ma, M. C. Sung, J. E. V. Johnson, Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting. Eur. J. Oper. Res. 283.1, 217-234 (2020). https://doi.org/10.1016/j.ejor.2019.11.007 [CrossRef] [Google Scholar]
  15. B. Wang, A financial risk identification model based on artificial intelligence. Wirel. Netw. 30, 4157-4165 (2024). https://doi.org/10.1007/s11276-021-02856-z [CrossRef] [Google Scholar]
  16. J. Duan, Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction. J. Franklin Inst. 356.8, 4716-4731 (2019). https://doi.org/10.1016/j.jfranklin.2019.01.046 [CrossRef] [MathSciNet] [Google Scholar]
  17. A. Khashman, Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Syst. Appl. 37.9, 6233-6239 (2010). https://doi.org/10.1016/j.eswa.2010.02.101 [CrossRef] [Google Scholar]
  18. H. Zhou, G. Sun, S. Fu, J. Liu, X. Zhou, J. Zhou, A big data mining approach of PSO- based BP neural network for financial risk management with IoT. IEEE Access. 7, 154035-154043 (2019). https://doi.org/10.1109/ACCESS.2019.2948949 [Google Scholar]
  19. A. Charpentier, R. Elie, C. Remlinger, Reinforcement learning in economics and finance. Comput. Econ. 62, 425-462 (2023). https://doi.org/10.1007/s10614-021-10119-4 [CrossRef] [Google Scholar]
  20. Z. Jiang, D. Xu, J. Liang, A deep reinforcement learning framework for the financial portfolio management problem. arXiv, 1706.10059 (2017). https://doi.org/10.48550/arXiv.1706.10059 [Google Scholar]
  21. Z. Shahbazi, Y. C. Byun, Machine learning-based analysis of cryptocurrency market financial risk management. IEEE Access, 10, 37848-37856 (2022). https://doi.org/10.1109/ACCESS.2022.3162858 [Google Scholar]
  22. S. Jaimungal, S. M. Pesenti, Y. S. Wang, H. Tatsat, Robust risk-aware reinforcement learning. SIAM J. Financial Mathematics, 12.4, 1234-1256 (2021). https://doi.org/10.48550/arXiv.2108.10403 [Google Scholar]
  23. D. Mhlanga, Financial inclusion in emerging economies: The application of machine learning and artificial intelligence in credit risk assessment. Int. J. Financial Stud. 9.3, 39 (2021). https://doi.org/10.3390/ijfs9030039 [CrossRef] [Google Scholar]
  24. M. Stolbov, M. Shchepeleva, Systemic risk, economic policy uncertainty and firm bankruptcies: Evidence from multivariate causal inference. SSRN Electron. J. 36, 1-36 (2019). https://doi.org/10.2139/ssrn.3502420 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.