Artificial Intelligence and the Financial Market - Unraveling the Transformative Potential and Innovative Applications
DOI:
https://doi.org/10.54536/ajfti.v3i1.4219Keywords:
Artificial Intelligence, Financial Market, Innovative Applications, Machine Learning, Transformative PotentialAbstract
The integration of artificial intelligence (AI) within the financial market has ushered in an era of unprecedented innovation and disruption, redefining traditional paradigms and unveiling transformative opportunities. This study explores the multifaceted applications of AI in the financial sector, including algorithmic trading, risk management, fraud detection, and portfolio optimization. By analyzing cutting-edge advancements such as machine learning, natural language processing, and predictive analytics, the research highlights how AI enhances market efficiency, decision-making accuracy, and operational agility. Moreover, the paper delves into the challenges and ethical considerations surrounding AI adoption, including data privacy, regulatory compliance, and the potential for market destabilization. Drawing on empirical evidence and case studies, this work offers a comprehensive examination of the symbiotic relationship between AI technologies and financial systems, while proposing innovative frameworks to harness their full potential responsibly. By unraveling the transformative capabilities of AI, this article aims to provide valuable insights for academics, practitioners, and policymakers striving to navigate the rapidly evolving landscape of the financial market.
References
Andy, A. M., Ching-Yang, L., & Makoto, K. (2022). Detecting market pattern changes: A machine learning approach. Finance Research Letters, 47(A), 102621. https://doi.org/https://doi.org/10.1016/j.frl.2021.102621
Debidutta, P., Sougata, R., & Raghu, R. (2024). Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review. Heliyon, 10(1), e23492. https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e23492
Dost, M., Iftikhar, A., Khwaja, N., & Malika, B. (2024). An explainable deep learning approach for stock market trend prediction. Heliyon, 10(21), e40095. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e40095
Fatima, D., Manar, A. T., Qassim, N., & Tracy, S. (2024). Artificial intelligence techniques in financial trading: A systematic literature review. Journal of King Saud University - Computer and Information Sciences, 36(3), 102015. https://doi.org/https://doi.org/10.1016/j.jksuci.2024.102015
Johann, F., Katja, H., Julian, W., Volker, B., & Zeljko, T. (2022). How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change, 178, 121598. https://doi.org/https://doi.org/10.1016/j.techfore.2022.121598
Leora, M., & Sheila, A. M. (2011). John McCarthy’s legacy. Artificial Intelligence, 175,(1), 1-24. https://doi.org/https://doi.org/10.1016/j.artint.2010.11.003
Mengjia, W., Dilek, C. K., Chao, M., & Yi, Z. (2021). Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence. Advanced Engineering Informatics, 50, 101368. https://doi.org/https://doi.org/10.1016/j.aei.2021.101368
Michael, S., Nathan, J., & Yang, F. (2024). Artificial intelligence and the end of bounded rationality: a new era in organizational decision making. Development and Learning in Organizations: An International Journal, 38(4), 1-3. https://doi.org/https://doi.org/10.1108/DLO-02-2023-0048
Noella, N., & Yeruva, V. R. R. (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 219, 119640. https://doi.org/https://doi.org/10.1016/j.eswa.2023.119640
Ritika, C., Gagan, D. S., & Vijay, P. (2024). Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction. Technovation, 135, 103067. https://doi.org/https://doi.org/10.1016/j.technovation.2024.103067
Shanmuganathan, M. (2020). Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance, 27, 100297. https://doi.org/https://doi.org/10.1016/j.jbef.2020.100297
Shaoxuan, Z., & Zhenpeng, L. (2023). Artificial intelligence technology innovation and firm productivity: Evidence from China. Finance Research Letters, 104437. https://doi.org/https://doi.org/10.1016/j.frl.2023.104437
Yogesh, K. D., & Anuj, S. (2023). Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192, 122579. https://doi.org/https://doi.org/10.1016/j.techfore.2023.122579
Yogesh, K. D., & Laurie, H. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 2(4), 101994. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Yongjun, X., Xin, L., Xin, C., & Changping, H. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179. https://doi.org/https://doi.org/10.1016/j.xinn.2021.100179
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jdidi Boussetta

This work is licensed under a Creative Commons Attribution 4.0 International License.