The Role of Artificial Intelligence in Financial Forecasting: A Systematic Literature Review

Ahmad Nur Budi Utama; Muhammad Hidayat

doi:10.62207/gk3kzf26

Authors

Ahmad Nur Budi Utama Universitas Jambi, Jambi, Indonesia Author
Muhammad Hidayat STIE Persada Bunda, Pekanbaru, Indonesia Author

DOI:

https://doi.org/10.62207/gk3kzf26

Keywords:

Artificial Intelligence, forecasting financial real-time, risk management, global financial crisis, risk mitigation, financial technology.

Abstract

This research examines the impact of using Artificial Intelligence (AI) in real-time financial forecasting on risk management strategies during the global financial crisis. By using a systematic literature review approach, this research aims to comprehensively evaluate existing literature regarding the application of AI in financial prediction and its impact on risk mitigation. The research process involved collecting data from various academic sources, applying the PRISMA method to ensure the quality and repeatability of the results, and thematic analysis to identify key themes and patterns. The main findings show that AI-enhanced real-time financial forecasting can improve prediction accuracy and speed up responses to financial risks, but it is also faced with challenges such as limited data and the risk of overfitting. This research makes a significant contribution to both academic literature and industry practice by offering guidance for financial institutions in leveraging AI for risk management. Practical implications include investment in technology infrastructure, staff training, and integration of AI with existing risk management systems. It is hoped that this research will enrich understanding of the role of AI in financial forecasting and provide a strong basis for further research in this area.

References

Adams, P., Adrian, T., Boyarchenko, N., & Giannone, D. (2021). Forecasting macroeconomic risks. International Journal of Forecasting, 37(3), 1173-1191. https://doi.org/10.1016/j.ijforecast.2021.01.003

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2017). Artificial intelligence and the ‘good society’: the us, eu, and uk approach. Science and Engineering Ethics. https://doi.org/10.1007/s11948-017-9901-7

Chen, B., Wu, Z., & Zhao, R. (2023). From fiction to fact: the growing role of generative ai in business and finance. Journal of Chinese Economic and Business Studies, 21(4), 471-496. https://doi.org/10.1080/14765284.2023.2245279

Hajj, M. (2023). Unveiling the influence of artificial intelligence and machine learning on financial markets: a comprehensive analysis of ai applications in trading, risk management, and financial operations. Journal of Risk and Financial Management, 16(10), 434. https://doi.org/10.3390/jrfm16100434

Heaton, J., Polson, N., & Witte, J. (2016). Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3-12. https://doi.org/10.1002/asmb.2209

Jain, R. (2023). Role of artificial intelligence in banking and finance. Journal of Management and Science, 13(3), 1-4. https://doi.org/10.26524/jms.13.27

Kamruzzaman, M., Alruwaili, O., & Aldaghmani, D. (2022). Measuring systemic and systematic risk in the financial markets using artificial intelligence. Expert Systems, 41(5). https://doi.org/10.1111/exsy.12971

Kumar, D., Taylor, G., & Wong, A. (2017). Opening the black box of financial ai with clear-trade: a class-enhanced attentive response approach for explaining and visualizing deep learning-driven stock market prediction. Journal of Computational Vision and Imaging Systems, 3(1). https://doi.org/10.15353/vsnl.v3i1.166

Kureljusic, M. and Karger, E. (2023). Forecasting in financial accounting with artificial intelligence – a systematic literature review and future research agenda. Journal of Applied Accounting Research, 25(1), 81-104. https://doi.org/10.1108/jaar-06-2022-0146

Li, W. (2020). Financial crisis warning of financial robot based on artificial intelligence. Revue D Intelligence Artificielle, 34(5), 553-561. https://doi.org/10.18280/ria.340504

Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2020). Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53-63. https://doi.org/10.1080/10580530.2020.1849465

Ranković, M. (2023). Artificial intelligence and the evolution of finance: opportunities, challenges and ethical considerations. Edtech Journal, 3(1), 20-23. https://doi.org/10.18485/edtech.2023.3.1.2

Sezer, Ö., Gudelek, M., & Özbayoğlu, A. (2020). Financial time series forecasting with deep learning : a systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181

Wei, B., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. Plos One, 12(7), e0180944. https://doi.org/10.1371/journal.pone.0180944

Yazdani, A. (2023). The impact of ai on trends, design, and consumer behavior. aitechbesosci, 1(4), 4-10. https://doi.org/10.61838/kman.aitech.1.4.2

Zakaria, S. (2023). Has the world of finance changed? a review of the influence of artificial intelligence on financial management studies. Information Management and Business Review, 15(4(SI)I), 420-432. https://doi.org/10.22610/imbr.v15i4(si)i.3617

Zianko, V. (2023). Artificial intelligence in the financial sector of ukraine: a driver of development and a factor of modernization. Innovation and Sustainability, (3), 6-21. https://doi.org/10.31649/ins.2023.3.6.21

Zohuri, B. (2023). The evolution of artificial intelligence: from supervised to semi-supervised and ultimately unsupervised technology trends. Current Trends in Eng Sc, 3(5), 1-4. https://doi.org/10.54026/ctes/1040

Zohuri, B. and Rahmani, F. (2019). Artificial intelligence driven resiliency with machine learning and deep learning components. Journal of Communication and Computer, 15(1). https://doi.org/10.17265/1548-7709/2019.01.001