Agentic Artificial Intelligence in Financial Systems: Transforming Predictive Analytics, Market Stability, And Autonomous Financial Decision-Making

• Irinna Kovarik

  Department of Information Systems and Digital Finance, University of Vienna, Austria

  Author

The rapid integration of artificial intelligence into financial systems has fundamentally reshaped how financial institutions analyze risk, forecast market movements, detect fraud, and deliver customer services. Recent advances in machine learning, deep learning, and agentic artificial intelligence have accelerated the transition from rule-based decision frameworks toward autonomous, adaptive systems capable of learning from vast financial datasets. This study investigates the evolving role of artificial intelligence, particularly agentic AI, in financial decision-making, predictive analytics, and market stability. The research draws upon existing theoretical frameworks and empirical findings in financial machine learning, algorithmic trading, credit scoring, and financial regulation to examine how intelligent systems are transforming modern financial infrastructures.

The study employs a comprehensive literature-based analytical methodology synthesizing interdisciplinary research across finance, economics, artificial intelligence, and regulatory studies. The analysis explores the emergence of agentic systems capable of autonomous goal-oriented decision-making and their implications for financial markets, risk management, and institutional governance. Particular attention is given to machine learning-driven predictive analytics, deep reinforcement learning in algorithmic trading, AI-driven credit scoring, and fraud detection frameworks based on neural networks and graph-based architectures.

Findings indicate that artificial intelligence technologies significantly enhance predictive capabilities, operational efficiency, and customer engagement in financial institutions. However, the increasing autonomy of agentic AI introduces new systemic risks, including algorithmic bias, model opacity, and potential market instability arising from interacting autonomous agents. The research highlights the importance of trustworthy AI frameworks, regulatory innovation, and human oversight mechanisms to mitigate such risks while maximizing the benefits of AI-driven financial innovation.

This study contributes to the growing academic discourse on financial AI by integrating theoretical insights from economics and computational sciences with emerging developments in agentic artificial intelligence. It further identifies critical research gaps related to governance, ethical deployment, and long-term systemic implications of autonomous financial systems. The findings provide strategic insights for policymakers, financial institutions, and researchers seeking to navigate the rapidly evolving landscape of AI-driven financial transformation.

Abdulsalam, T. A., & Tajudeen, R. B. (2024). Artificial intelligence in the banking industry: a review of service areas and customer service journeys in emerging economies. Business Management Compass.

Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic AI: autonomous intelligence for complex goals-a comprehensive survey. IEEE Access.

Acharya, V., Richardson, M., Van Nieuwerburgh, S., & White, L. (2009). Causes of the financial crisis. Critical Review.

Ala-Pietilä, P., et al. (2020). The Assessment List for Trustworthy Artificial Intelligence (ALTAI).

Aldasoro, I., Gambacorta, L., & Traina, J. (2024). The impact of artificial intelligence on output and inflation. BIS Working Papers.

Anwar, U., et al. (2024). Foundational challenges in assuring alignment and safety of large language models.

Araujo, D., et al. (2024). Artificial intelligence in central banking. BIS Bulletin.

Arner, D. W., Barberis, J., & Buckley, R. P. (2017). FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business.

A.K. Bhat and G. Krishnan, "A Review of Agentic Artificial Intelligence: Power of Self-Driven AI in the Future of Financial Autonomy and Enhanced Customer Engagement," 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2025, pp. 1160-1165, doi: 10.1109/ICSCDS65426.2025.11167368.

Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert systems and hybrid intelligent systems. Neural Computing and Applications.

Broby, D. (2022). The use of predictive analytics in finance. Journal of Financial Data Science.

Cao, L. (2021). AI in finance: A Python-based guide. O’Reilly Media.

Czarnitzki, D., et al. (2023). Artificial intelligence and firm-level productivity. Journal of Economic Behavior and Organization.

Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in finance: From theory to practice. Springer.

Gambacorta, L., Huang, Y., Qiu, H., & Wang, J. (2024). How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm. Journal of Financial Stability.

Georges, C., Briola, A., & Natoli, F. (2021). Market stability with machine learning agents. Journal of Economic Dynamics and Control.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies.

Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks.

Hosseini, S., & Seilani, H. (2025). The role of agentic AI in shaping a smart future: a systematic review. Array.

Hughes, L., Dwivedi, Y. K., Malik, T., Shawosh, M., Albashrawi, M. A., & Jeon, I. (2025). AI agents and agentic systems: a multi-expert analysis. Journal of Computer Information Systems.

Liu, X. Y., Yang, H., Gao, J., & Wang, C. D. (2020). FinRL: Deep reinforcement learning framework to automate trading in quantitative finance.

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: a systematic review. Expert Systems with Applications.

Pricope, T. V. (2023). Deep reinforcement learning in quantitative algorithmic trading: a review.

• PDF

Copyright (c) 2025 Irinna Kovarik (Author)

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