ПРИМЕНА НА ВЕШТАЧКАТА ИНТЕЛИГЕНЦИЈА ВО ФИНАНСИСКО МОДЕЛИРАЊЕ И ПРЕДВИДУВАЊЕ НА РИЗИЦИ
Keywords:
финансиско моделирање, ризик, вештачка интелигенција
Abstract
Во ова истражување е даден теоретски преглед на важноста на вештачката интелигенција во финансиското моделирање и предвидување на ризици. На глобално ниво, финансиските пазари се развиваат со многу брзо темпо, каде што ефикасноста, брзината и прецизноста играат важна улога во постигнувањето на позитивни резултати. Ова е овозможено преку вештачката интелигенција преку која се овозможува анализирање на големи бази на податоци за краток временски период. Финансискиот сектор е динамичен и променлив и затоа е потребно креирање на модели преку кои навремено може да се предвидат потенцијални кризи и да се изработат стратегии за нивно избегнување.
Downloads
Download data is not yet available.
References
[1] Bank for International Settlements (2024). Inovation, fintech and AI. BIS website. Retrieved from: https://www.bis.org/topic/fintech.htm?m=3096&m=185
[2] Cornelli, et al. (2023). Fintech vs bank credit: How do they react to monetary policy? BIS Working Papers No 1157, Monetary and Economic Department.
[3] Doerr, S., et al. (2023). Big techs in finance. BIS Working Papers No 1129, Monetary and Economic Department.
[4] Corporate Finance Institute (2024). Machine Laerning (in Finance). CFI website. Retrieved from: https://corporatefinanceinstitute.com/resources/data-science/machine-learning-in-finance/
[5] Huang, J., et al. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China. Vol. 14, No. 13.
[6]Gilli, M., et al. (2019). Chapter 10 - Optimization problems in finance. Numerical Methods and Optimization in Finance (Second edition), pp. 219-228.
[7] Biriuk, D., et al. (2024). The Role of Blockchain Technologies in Changing the Structure of the Financial and Credit System. Theoretical and Practical Research in Economic Fields. Vol. 15, No. 2(30).
[8] Mhlanga, D. (2023). Block chain technology for digital financial inclusion in the industry 4.0, towards sustainable development? Frontiers in Blockchain, 6: 1-25.
[9] Cunha, P. R. D., et al. (2021). Blockchain for development: A guiding framework. Information Technology for Development, 27(3): 417-438.
[10] Vorobets, V. (2020). Advantages of using blockchain technology in the conditions of digitization of financial instruments. World of Finance 2(63): 49-61.
[11] Klopotan, I., et al. (2018). Early warning system in business, finance, and economics: Bibliometric and topic analysis. International Journal of Engineering Business Management. Vol. 10, pp. 1-12.
[12] Kondapaka, K. K. (2019). Advanced AI Models for Portfolio Management and Optimization in Finance: Techniques, Applications, and Real-World Case Studies. Distributed Learning and Broad Applications in Scientific Research. Vol. 5, pp. 560-597.
[13] Kalogiannidis, S., et al. (2024). The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece. Risks. Vol. 12, No. 19.
[14] Meena, R. and Madan, A. K. (2023). Using AI for Predictive Maintenance in CAM. International Journal of Research Publication and Reviews. Vol. 4, pp. 5712–22.
[15] Mhlanga, D. (2021). Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment. International Journal of Financial Studies. Vol. 9, No. 39.
[16] Gui, L. (2019). University of California Los Angeles Application of Machine Learning Algorithms in Predicting Credit Card Default Payment. Los Angeles: University of California.
[2] Cornelli, et al. (2023). Fintech vs bank credit: How do they react to monetary policy? BIS Working Papers No 1157, Monetary and Economic Department.
[3] Doerr, S., et al. (2023). Big techs in finance. BIS Working Papers No 1129, Monetary and Economic Department.
[4] Corporate Finance Institute (2024). Machine Laerning (in Finance). CFI website. Retrieved from: https://corporatefinanceinstitute.com/resources/data-science/machine-learning-in-finance/
[5] Huang, J., et al. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China. Vol. 14, No. 13.
[6]Gilli, M., et al. (2019). Chapter 10 - Optimization problems in finance. Numerical Methods and Optimization in Finance (Second edition), pp. 219-228.
[7] Biriuk, D., et al. (2024). The Role of Blockchain Technologies in Changing the Structure of the Financial and Credit System. Theoretical and Practical Research in Economic Fields. Vol. 15, No. 2(30).
[8] Mhlanga, D. (2023). Block chain technology for digital financial inclusion in the industry 4.0, towards sustainable development? Frontiers in Blockchain, 6: 1-25.
[9] Cunha, P. R. D., et al. (2021). Blockchain for development: A guiding framework. Information Technology for Development, 27(3): 417-438.
[10] Vorobets, V. (2020). Advantages of using blockchain technology in the conditions of digitization of financial instruments. World of Finance 2(63): 49-61.
[11] Klopotan, I., et al. (2018). Early warning system in business, finance, and economics: Bibliometric and topic analysis. International Journal of Engineering Business Management. Vol. 10, pp. 1-12.
[12] Kondapaka, K. K. (2019). Advanced AI Models for Portfolio Management and Optimization in Finance: Techniques, Applications, and Real-World Case Studies. Distributed Learning and Broad Applications in Scientific Research. Vol. 5, pp. 560-597.
[13] Kalogiannidis, S., et al. (2024). The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece. Risks. Vol. 12, No. 19.
[14] Meena, R. and Madan, A. K. (2023). Using AI for Predictive Maintenance in CAM. International Journal of Research Publication and Reviews. Vol. 4, pp. 5712–22.
[15] Mhlanga, D. (2021). Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment. International Journal of Financial Studies. Vol. 9, No. 39.
[16] Gui, L. (2019). University of California Los Angeles Application of Machine Learning Algorithms in Predicting Credit Card Default Payment. Los Angeles: University of California.
Published
2024-12-23
How to Cite
Mitreva, M., & Koleva, B. (2024). ПРИМЕНА НА ВЕШТАЧКАТА ИНТЕЛИГЕНЦИЈА ВО ФИНАНСИСКО МОДЕЛИРАЊЕ И ПРЕДВИДУВАЊЕ НА РИЗИЦИ. Yearbook - Faculty of Economics, 26(1), 35-40. Retrieved from https://js.ugd.edu.mk/index.php/YFE/article/view/7053
Section
Banking and Finance
