TRADITIONAL FINANCE TO INTELLIGENT SYSTEMS OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND EMERGING RESEARCH FRONTIERS
Downloads
Objective: The present review addresses the transition from conventional finance to intelligent finance via six inter-related dimensions including financial digitalization, application of AI technologies along the financial value chain, decision-making through AI, value generation, governance issues, and research horizons. Method: The present review summarizes empirical data concerning the application of AI in financial forecasting, fraud detection, credit analysis, risk management, investment management, financial reporting, generative AI, and explainable AI. Results: The available evidence until 2025 shows that AI is increasingly being adopted in financial services, as reported by Gartner that 59% of finance organizations were using AI in 2025 compared to 37% in 2023. It is shown that AI is evolving finance by going far beyond conventional automation towards prediction and adaptation as well as decision-making. Novelty: The review claims that the future of intelligent finance will rely on collaboration between humans and AI.
Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165–1195. https://doi.org/10.1007/s00521-010-0362-z
Milana, C., & Ashta, A. (2021). Artificial intelligence techniques in finance and financial markets: A survey of the literature. Strategic Change, 30(3), 189–209. https://doi.org/10.1002/jsc.2403
Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2021). Machine learning towards intelligent systems: applications, challenges, and opportunities. Artificial Intelligence Review, 54(5), 3299–3348. https://doi.org/10.1007/s10462-020-09948-w
Sarker, I. H. (2022). AI-Based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158. https://doi.org/10.1007/s42979-022-01043-x
Mosteanu, N. R., & Faccia, A. (2021). Fintech frontiers in quantum computing, fractals, and Blockchain Distributed ledger: Paradigm shifts and open innovation. Journal of Open Innovation Technology Market and Complexity, 7(1), 19. https://doi.org/10.3390/joitmc7010019
Uddin, N., Kadir, A., & Arafat, M. Y. (2025). Artificial intelligence adoption for financial reconciliation and strategic business performance. Academic Journal of Digital Economics and Stability, 38(4), 179–189. https://doi.org/10.51699/economics.v38i4.1640
Lukyanenko, R., Wiggins, A., & Rosser, H. K. (2019). Citizen Science: an information quality research frontier. Information Systems Frontiers, 22(4), 961–983. https://doi.org/10.1007/s10796-019-09915-z
Biju, A. K. V. N., Thomas, A. S., & Thasneem, J. (2023). Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis. Quality & Quantity, 58(1), 849–878. https://doi.org/10.1007/s11135-023-01673-0
Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2022). Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis. Information Systems Frontiers, 25(2), 871–896. https://doi.org/10.1007/s10796-022-10279-0
Uddin, N. (2025). Artificial Intelligence in Financial Governance for Strengthening Internal Control and Regulatory Compliance. American Journal of Management Practice, 2(08).
Weber, P., Carl, K. V., & Hinz, O. (2023). Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature. Management Review Quarterly, 74(2), 867–907. https://doi.org/10.1007/s11301-023-00320-0
Nguyen, D. K., Sermpinis, G., & Stasinakis, C. (2022). Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology. European Financial Management, 29(2), 517–548. https://doi.org/10.1111/eufm.12365
Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2021). Machine learning towards intelligent systems: applications, challenges, and opportunities. Artificial Intelligence Review, 54(5), 3299–3348. https://doi.org/10.1007/s10462-020-09948-w
Uddin, N., Kadir, A., & Ahmed, W. (2026). Data Analytics capability for improving financial Decision-Making and organizational performance. Academic Journal of Digital Economics and Stability, 39(4), 351–360. https://doi.org/10.51699/economics.v39i4.1664
Subburayan, B., Sankarkumar, A. V., Singh, R., & Mushi, H. M. (2024). Transforming of the Financial Landscape from 4.0 to 5.0: Exploring the Integration of Blockchain, and Artificial Intelligence. In Financial Mathematics and Fintech (pp. 137–161). https://doi.org/10.1007/978-3-031-47324-1_9
Chen, Y., Lu, Y., Bulysheva, L., & Kataev, M. Y. (2022). Applications of Blockchain in Industry 4.0: a Review. Information Systems Frontiers, 26(5), 1715–1729. https://doi.org/10.1007/s10796-022-10248-7
Khan, M. A., Khan, H., Omer, M. F., Ullah, I., & Yasir, M. (2024). Impact of artificial intelligence on the global economy and technology advancements. In Advanced technologies and societal change (pp. 147–180). https://doi.org/10.1007/978-981-97-3222-7_7
Alzubaidi, L., Al-Sabaawi, A., Bai, J., Dukhan, A., Alkenani, A. H., Al-Asadi, A., Alwzwazy, H. A., Manoufali, M., Fadhel, M. A., Albahri, A. S., Moreira, C., Ouyang, C., Zhang, J., Santamaría, J., Salhi, A., Hollman, F., Gupta, A., Duan, Y., Rabczuk, T., . . . Gu, Y. (2023). Towards Risk‐Free Trustworthy Artificial intelligence: Significance and requirements. International Journal of Intelligent Systems, 2023(1). https://doi.org/10.1155/2023/4459198
Yeasir, A. M., & Uddin, N. (2026). Digital transformation in financial management through artificial intelligence and intelligent automation. Journal of Knowledge Learning and Science Technology ISSN 2959-6386 (Online), 5(1), 57–63. https://doi.org/10.60087/jklst.vol5.n1.006
Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1–29. https://doi.org/10.1080/23270012.2019.1570365
Qin, Y., Xu, Z., Wang, X., & Skare, M. (2023). Artificial Intelligence and Economic Development: An Evolutionary investigation and Systematic review. Journal of the Knowledge Economy, 15(1), 1736–1770. https://doi.org/10.1007/s13132-023-01183-2
Jefroy, N., Azarian, M., & Yu, H. (2022). Moving from Industry 4.0 to Industry 5.0: What Are the Implications for Smart Logistics? Logistics, 6(2), 26. https://doi.org/10.3390/logistics6020026
Malviya, A., & Jaspal, D. (2021). Artificial intelligence as an upcoming technology in wastewater treatment: a comprehensive review. Environmental Technology Reviews, 10(1), 177–187. https://doi.org/10.1080/21622515.2021.1913242
Uddin, N., Arafat, M. Y., & Ahmed, W. (2026). Artificial intelligence capability for enterprise financial risk management and business resilience. Central Asian Journal of Innovations on Tourism Management and Finance, 7(1), 624–632. https://doi.org/10.51699/cajitmf.v7i1.1365
Zhao, L., Tang, Z., & Zou, X. (2019). Mapping the knowledge Domain of Smart-City Research: A Bibliometric and scientometric analysis. Sustainability, 11(23), 6648. https://doi.org/10.3390/su11236648
Li, J. (2018). Cyber security meets artificial intelligence: a survey. Frontiers of Information Technology & Electronic Engineering, 19(12), 1462–1474. https://doi.org/10.1631/fitee.1800573
Bhatti, U. A., Tang, H., Wu, G., Marjan, S., & Hussain, A. (2023). Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence. International Journal of Intelligent Systems, 2023(1). https://doi.org/10.1155/2023/8342104
Yang, Y., Zhuang, Y., & Pan, Y. (2021). Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies. Frontiers of Information Technology & Electronic Engineering, 22(12), 1551–1558. https://doi.org/10.1631/fitee.2100463
Uddin, N. (2025). Artificial intelligence for intelligent financial reconciliation and discrepancy detection. American Journal of Economics and Business Management, 8(5), 2653–2661. https://doi.org/10.31150/ajebm.v8i5.5420
Zhang, X., Zhang, P., Shen, Y., Liu, M., Wang, Q., Gašević, D., & Fan, Y. (2024). A Systematic literature review of Empirical research on applying Generative Artificial Intelligence in education. Frontiers of Digital Education., 1(3), 223–245. https://doi.org/10.1007/s44366-024-0028-5
Zhang, L., Guo, W., Lv, C., Guo, M., Yang, M., Fu, Q., & Liu, X. (2023). Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress. Animal Research and One Health, 2(1), 93–109. https://doi.org/10.1002/aro2.44
Copyright (c) 2026 Journal of Artificial Intelligence and Digital Economy
This work is licensed under a Creative Commons Attribution 4.0 International License.
