ARCHIVES
Original Article
Framework-Driven Development of Risk Management Products: Enhancing Customization, Compliance, and Feature Reuse
Nagabhushanam Bheemisetty1
1 Independent Researcher, USA
Published Online: January-April 2026
Pages: 616-623
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501073
References
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4. S. Chen, “Research on risk management in banking system,” Highlights in Business, Economics and Management, vol. 3, pp. 267–275, 2023, doi: 10.54097/hbem.v3i.4754.
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6. P. Guerra, M. Castelli, and N. Côrte-Real, “Machine learning for liquidity risk modelling: A supervisory perspective,” Economic Analysis and Policy, vol. 74, pp. 175–187, 2022, doi: 10.1016/j.eap.2022.02.001.
7. S. Johri, N. I. Qureshi, K. Mehta, B. Othman, S. Waghmare, and B. Pant, “A critical significance of using machine learning in strengthening financial risk management in banking firms,” in Proc. 2nd Int. Conf. Adv. Comput. Innovative Technol. Eng. (ICACITE), IEEE, 2022, pp. 1933–1937.
8. Mashrur, W. Luo, N. A. Zaidi, and A. Robles-Kelly, “Machine learning for financial risk management: A survey,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3036322.
9. H. Sadok, H. Mahboub, H. Chaibi, R. Saadane, and M. Wahbi, “Applications of artificial intelligence in finance: Prospects, limits and risks,” in Proc. Int. Conf. Digital Age and Technological Advances for Sustainable Development (ICDATA), IEEE, 2023, pp. 145–149, doi: 10.1109/ICDATA58816.2023.00034.
10. S. Shi, R. Tse, W. Luo, S. D'Addona, and G. Pau, “Machine learning-driven credit risk: A systemic review,” Neural Computing and Applications, Springer, 2022, doi: 10.1007/s00521-022-07472-2.
11. J. Xu and D. Yang, “Financial risk control model based on deep neural networks,” Scientific Programming, 2022, Art. no. 7253832, doi: 10.1155/2022/7253832.
12. S. Aziz and M. M. Dowling, “AI and machine learning for risk management,” SSRN Electronic Journal, 2018.
13. M. Jacobs Jr., “The validation of machine-learning models for the stress testing of credit risk,” Journal of Risk Management in Financial Institutions, vol. 11, pp. 218–243, 2018.
14. S. Kannan and K. Somasundaram, “Autoregressive-based outlier algorithm to detect money laundering activities,” Journal of Money Laundering Control, vol. 20, pp. 190–202, 2017.
15. M. P. Khrestina, D. I. Dorofeev, P. A. Kachurina, T. R. Usubaliev, and A. S. Dobrotvorskiy, “Development of algorithms for searching, analyzing and detecting fraudulent activities in the financial sphere,” European Research Studies Journal, vol. 20, pp. 484–498, 2017.
16. Wójcicka, “Neural networks vs. discriminant analysis in the assessment of default,” Electronic Economy, pp. 339–349, 2017.
17. W. Zhang, “Machine learning approaches to predicting company bankruptcy,” Journal of Financial Risk Management, vol. 6, pp. 364–374, 2017.
18. H. G. Zhang, C. W. Su, Y. Song, S. Qiu, R. Xiao, and F. Su, “Calculating value-at-risk for high-dimensional time series using a nonlinear random mapping model,” Economic Modelling, vol. 67, pp. 355–367, 2017.
2. Basel Committee on Banking Supervision (BCBS), “The Basel framework,” 2023. [Online]. Available: https://www.bis.org/basel_framework/. Accessed: Sep. 29, 2023.
3. S. Bhatore, L. Mohan, and Y. R. Reddy, “Machine learning techniques for credit risk evaluation: A systematic literature review,” J. Banking Financial Technol., vol. 4, no. 1, pp. 111–138, 2020, doi: 10.1007/s42786-020-00020-3.
4. S. Chen, “Research on risk management in banking system,” Highlights in Business, Economics and Management, vol. 3, pp. 267–275, 2023, doi: 10.54097/hbem.v3i.4754.
5. P. Giudici, M. Centurelli, and S. Turchetta, “Artificial intelligence risk measurement,” Expert Syst. Appl., vol. 235, Art. no. 121220, 2023, doi: 10.1016/j.eswa.2023.121220.
6. P. Guerra, M. Castelli, and N. Côrte-Real, “Machine learning for liquidity risk modelling: A supervisory perspective,” Economic Analysis and Policy, vol. 74, pp. 175–187, 2022, doi: 10.1016/j.eap.2022.02.001.
7. S. Johri, N. I. Qureshi, K. Mehta, B. Othman, S. Waghmare, and B. Pant, “A critical significance of using machine learning in strengthening financial risk management in banking firms,” in Proc. 2nd Int. Conf. Adv. Comput. Innovative Technol. Eng. (ICACITE), IEEE, 2022, pp. 1933–1937.
8. Mashrur, W. Luo, N. A. Zaidi, and A. Robles-Kelly, “Machine learning for financial risk management: A survey,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3036322.
9. H. Sadok, H. Mahboub, H. Chaibi, R. Saadane, and M. Wahbi, “Applications of artificial intelligence in finance: Prospects, limits and risks,” in Proc. Int. Conf. Digital Age and Technological Advances for Sustainable Development (ICDATA), IEEE, 2023, pp. 145–149, doi: 10.1109/ICDATA58816.2023.00034.
10. S. Shi, R. Tse, W. Luo, S. D'Addona, and G. Pau, “Machine learning-driven credit risk: A systemic review,” Neural Computing and Applications, Springer, 2022, doi: 10.1007/s00521-022-07472-2.
11. J. Xu and D. Yang, “Financial risk control model based on deep neural networks,” Scientific Programming, 2022, Art. no. 7253832, doi: 10.1155/2022/7253832.
12. S. Aziz and M. M. Dowling, “AI and machine learning for risk management,” SSRN Electronic Journal, 2018.
13. M. Jacobs Jr., “The validation of machine-learning models for the stress testing of credit risk,” Journal of Risk Management in Financial Institutions, vol. 11, pp. 218–243, 2018.
14. S. Kannan and K. Somasundaram, “Autoregressive-based outlier algorithm to detect money laundering activities,” Journal of Money Laundering Control, vol. 20, pp. 190–202, 2017.
15. M. P. Khrestina, D. I. Dorofeev, P. A. Kachurina, T. R. Usubaliev, and A. S. Dobrotvorskiy, “Development of algorithms for searching, analyzing and detecting fraudulent activities in the financial sphere,” European Research Studies Journal, vol. 20, pp. 484–498, 2017.
16. Wójcicka, “Neural networks vs. discriminant analysis in the assessment of default,” Electronic Economy, pp. 339–349, 2017.
17. W. Zhang, “Machine learning approaches to predicting company bankruptcy,” Journal of Financial Risk Management, vol. 6, pp. 364–374, 2017.
18. H. G. Zhang, C. W. Su, Y. Song, S. Qiu, R. Xiao, and F. Su, “Calculating value-at-risk for high-dimensional time series using a nonlinear random mapping model,” Economic Modelling, vol. 67, pp. 355–367, 2017.
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