Decision-Making Support for Supervisory Control of Non-Banking Financial Institutions

Nikita Perevozchikov; Oleg Golovnin

Информационные технологии интеллектуальной поддержки принятия решений, Информационные технологии интеллектуальной поддержки принятия решений 2020

Nikita Perevozchikov, Oleg Golovnin

Изменена: 2025-02-20

Аннотация

With the change in the regulatory landscape in the financial sector, the organization of the activities of nonbanking financial institutions (NBFI) is making more and more demands on the part of state regulatory authorities. To increase the effectiveness of supervision of NBFIs, regulatory bodies use systems that automate the supervisory activities, and, in particular, decision support systems. This paper presents a decision support system to plan supervisory activities over NBFIs in accordance with the principle of risk-based supervision, in which resources are concentrated on NBFIs with the maximum amount of risk or scope of activity. The developed system tracks and prevents violations, adjusts the correctness of actions in relation to NBFIs. In addition, the system plans supervisory activities, changes the supervision regimes for NBFIs and controls the timing. The application of the developed system increases the effectiveness of the regulatory bodies in terms of control and supervision of both individual NBFIs and the financial market as a whole

Ключевые слова

supervision system; event planning; risk-based supervision

Литература

[1] R. M. Lalon and S. Hussain, “An analysis of financial performance on non-bank financial institutions (NBFI) in Bangladesh: A study on Lanka-Bangla Finance Limited,” Int. J. Econ., Fin. and Manag. Sci., vol. 5, pp. 251, September 2017.

[2] I. Ofoeda, “Credit risk management and NBFI profitability,” Int. J. of Fin. Serv. Manag., vol. 8(3), pp. 195–216, November 2016.

[3] K. R. Shanmugam, R. Kannan, and S. Bhaduri, “Non-banking financial intermediaries: International experiences,” in Non-Banking Financial Companies Role in India's Development, vol. 1. Singapore: Springer, 2019, pp. 103–124.

[4] K. Mungai and A. Bayat, “The impact of Big Data on the South African banking industry,” Int. Conf. on Intell. Capital Knowl. Manag. & Organis. Learn., Cape Town, pp. 225, December 2018.

[5] R. Rateiwa and M. J. Aziakpono, “Non-bank financial institutions and the attainment of sustainable development goals: could this be the trump card for Africa?,” Africagrowth Agenda, vol. 14, pp. 8–13, September 2017.

[6] B. G. Coury and R. D. Semmel, “Supervisory control and the design of intelligent user interfaces,” Autom. and hum. perform., vol. 11, pp. 221–242, October 2018.

[7] F. M. Favarò and J. H. Saleh, “Application of temporal logic for safety supervisory control and model-based hazard monitoring,” Reliab. Engin. & Syst. Safety, vol. 169, pp. 166–178, January 2018.

[8] A. Şensoy, G. Uysal, and A. A. Şorman, “Developing a decision support framework for real‐time flood management using integrated models,” J. of Flood Risk Manag., vol. 11, pp. 866–883, November 2016.

[9] J. Popescu, M. Simionescu, C.Meiță, L. Nela, and B. Popa, “A specific solution to decrease the credit risk at a non-banking financial institution,” Young Econom. J., vol. 13, pp. 27, November 2016.

[10] M. Moradi-Aliabadi and Y. Huang, “Decision support for enhancement of manufacturing sustainability: a hierarchical control approach,” ACS Sustain. Chem.& Eng., vol. 6, pp. 4809–4820, March 2018.

[11] D. Bykov, E. Frank, O. Surnin, P. Sitnikov, A. Ivaschenko, and O. Golovnin, “Samara polytech innovation: Digital campus 2.0,” Int. Conf. Complex Systems: Control and Modeling Problems, Samara, pp. 49-53, September 2019.

[12] D. Casey, P. Burrell, and N. Sumner, “Decision support systems in policing,” Europ. Law Enforc. Research Bull., vol. 4, pp. 97–106, October 2018.

[13] D. L. Olson and D.Wu, “Enterprise risk management models,” in Enterprise risk management models. Berlin: Springer, 2017, pp. 175– 192.

[14] P. Verma, “Promethee: A tool for multi-criteria decision analysis,” Multi-Criteria Decision Analysis in Manag., vol. 1, pp. 282–309, January 2020.

[15] C. Sibley, J. Coyne, G. V. Avvari, M. Mishra, and K. R. Pattipati, “Supporting multi-objective decision making within a supervisory control environment,” Int. Conf. on Augmented Cognition, Toronto, pp. 210–221, Jule 2016.

[16] I. Scholl, “Organizational-and system-level characteristics that influence implementation of shared decision-making and strategies to address them–a scoping review,” Implementation Sci., vol. 14, pp. 40, March 2018.

[17] O. Golovnin and T. Mikheeva, “Detailed models and network-centric technologies of transport process management,” 5th IEEE Int. Conf. on Models and Tech. for Intelligent Transp. Syst., Napoli, pp. 768-773, June 2017.

[18] X. Li, “A dynamic decision-making approach for intrusion response in industrial control systems,” IEEE Transactions on Industrial Informatics, vol. 15, pp. 2544–2554, August 2018.

[19] A. Ivaschenko, A. Stolbova, and O. Golovnin, “Spatial clustering based on analysis of Big Data in digital marketing,” Comm. in Comp. and Inf. Sci., vol. 1093, pp. 335–347, October 2019.

[20] Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda,” Int. J. of Inform. Manag., vol. 48, pp. 63–71, October 2019.

[21] S. Chakravarthy, A. Santra, and K. S. Komar, “Humble data management to Big Data analytics/science: A retrospective stroll,” Int. Conf. on Big Data Analytics, Warangal, pp. 33–54, December 2018.