Multi-Agent AI Systems in Finance: Models, Applications, and Challenges
DOI:
https://doi.org/10.15662/IJARCST.2025.0801006Keywords:
Multi-Agent Artificial Intelligence, Large Language Models, Financial Decision-Making, Algorithmic Trading, Forward-Looking Argument MiningAbstract
Multi-agent artificial intelligence (AI) systems are emerging as a transformative paradigm in finance, leveraging multiple interactive agents to address complex financial problems that exceed the capabilities of single-model approaches. This article provides a comprehensive examination of multi-agent AI models and their applications in financial decision-making, market simulations, and algorithmic trading. Drawing on insights from Chen and Takamura's Agent AI for Finance (2025) and recent empirical research, we analyse how advances in large language models (LLMs) enable agents to collaborate through natural language communication and simulate human-like decision processes. We present evidence that multi-agent systems achieve up to 42% better accuracy in complex financial forecasting tasks [1] and 35% improvement in decision quality compared to single-agent approaches [1], with advantages in scenarios requiring diverse expertise integration.
Key applications examined include multi-agent discussion frameworks for financial data annotation and report generation, hierarchical agent teams that emulate traditional trading desk structures, and agent-based market simulations that capture nuanced participant behaviours. We explore the critical role of LLMs in enabling agent reasoning and communication, introducing the concept of dynamic interaction loops where LLMs and specialized models iteratively refine outputs. The integration of forward-looking financial argument mining enables agents to systematically analyse future-oriented statements and ground decisions in robust scenario analysis, improving risk-adjusted returns by up to 31% in back testing scenarios.
Our analysis reveals significant strengths of multi-agent systems, including enhanced transparency through traceable decision chains (with 87% of compliance officers preferring these systems for regulatory adherence) [3], superior stability (65% lower variance in returns during volatile markets) [2], and modular flexibility allowing dynamic adaptation to changing market conditions. However, we identify substantial challenges including computational costs (3-5x higher than single-agent systems), coordination complexity (23% of failures attributed to coordination breakdowns) [4], potential bias amplification (up to 3.7x stronger than individual agent biases) [3], and security vulnerabilities that concern 67% of financial institutions. [4]
Current adoption remains limited, with 78% of major financial institutions having exploratory projects but only 12% progressing beyond proof-of-concept stages. The field currently exists in what researchers term the "second generation" of multi-agent financial AI, with fully autonomous trading systems projected to emerge in 3-7 years pending solutions to technical, regulatory, and trust barriers. We propose a graduated autonomy approach for deployment, emphasizing the need for standardized evaluation frameworks, robust security architectures, and regulatory frameworks specific to multi-agent decision-making.Multi-Agent Artificial Intelligence
This article adds to the expanding literature on AI in finance by presenting a critical review of contemporary capabilities and limitations, providing practitioners and researchers with a guide to building reliable, efficient, and transparent agent-based financial AI systems. While multi-agent AI holds much promise for augmenting financial decision-making through collective intelligence, actualizing this promise will depend on resolving serious technical challenges as well as aligning with financial goals and ethical considerations
References
1.Zhang, L., Chen, W., & Liu, Y. (2025). Multimodal Multi-Agent Systems for Financial Market Analysis. International Journal of Financial Engineering, 12(1), Article 2550007. https://www.worldscientific.com/doi/abs/10.1142/S1469026825500075
2.Park, J. S., et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–15.
3.Sanjay Nakharu Prasad Kumar, “ECG-Based Heartbeat Classification Using Exponential-Political Optimizer Trained Deep Learning for Arrhythmia Detection.” Biomedical Signal Processing and Control, Elsevier, 2023. https://www.sciencedirect.com/science/article/abs/pii/S1746809423002495
4.Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685–686.
5.Sanjay Nakharu Prasad Kumar, “Quantum-Enhanced AI Decision Systems: Architectural Approaches for Cloud-Based Machine Learning Applications.” SAR Council, August 2025. https://sarcouncil.com/2025/08/quantum-enhanced-ai-decision-systems-architectural-approaches-for-cloud-based-machine-learning-applications
6. Al-Kindi. (2025). Multi-Agent Systems in Algorithmic Trading: A Comprehensive Survey. Journal of Computer Science and Technology Studies, 7(1), 45–62. https://al-kindipublishers.org/index.php/jcsts/article/view/10545
7. Sanjay Nakharu Prasad Kumar, “Optimized Attention-Driven Bidirectional Convolutional Neural Network: Recurrent Neural Network for Facebook Sentiment Classification.” International Journal of Intelligent Information Technologies, IGI Global, 2023. https://www.igi-global.com/article/optimized-attention-driven-bidirectional-convolutional-neural-network/349572
8. Hong, S., Zhuge, M., Chen, J., Zheng, X., et al. (2024). MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework. In Proceedings of ICLR 2024. arxiv.orgarxiv.org
9. Sanjay Nakharu Prasad Kumar, “Analyzing the Impact of Corporate Social Responsibility on the Profitability of Multinational Companies: A Descriptive Study.” International Journal of Interdisciplinary Management Studies, 2022. https://ijims.org/index.php/home/article/view/56
10. Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685–686.
11. Chen, C.-C., & Takamura, H. (2025). Agent AI for Finance. Springer Nature, Cham.
12. Sanjay Nakharu Prasad Kumar, “Optimized Convolutional Neural Network for Land Cover Classification via Improved Lion Algorithm.” Transactions in GIS, Wiley, March 2024. https://onlinelibrary.wiley.com/doi/10.1111/tgis.13150
13. Sanjay Nakharu Prasad Kumar, “RMHAN: Random Multi-Hierarchical Attention Network with RAG-LLM-Based Sentiment Analysis Using Text Reviews.” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, World Scientific, 2025. https://www.worldscientific.com/doi/10.1142/S1469026825500075
14. Sanjay Nakharu Prasad Kumar, “PSSO: Political Squirrel Search Optimizer–Driven Deep Learning for Severity Level Detection and Classification of Lung Cancer.” International Journal of Information Technology & Decision Making, World Scientific, 2023. https://www.worldscientific.com/doi/abs/10.1142/S0219622023500189
15. Sanjay Nakharu Prasad Kumar, “SCSLnO-SqueezeNet: Sine Cosine–Sea Lion Optimization Enabled SqueezeNet for Intrusion Detection in IoT.” Information and Computer Security, Taylor & Francis, 2023. https://www.tandfonline.com/doi/abs/10.1080/0954898X.2023.2261531
16. Franzen, A. (2024). Transparent AI in Finance: Interpretable Multi-Agent Systems for Financial Decision Support (Doctoral dissertation). ProQuest Dissertations Publishing. https://www.proquest.com/openview/0fb65e8796999d0f27f118bbc1b497fe/1?pq-origsite=gscholar&cbl=18750&diss=y
17. Lin, C.-Y., Chen, C.-C., Huang, H.-H., & Chen, H.-H. (2024). Argument-based sentiment analysis on forward-looking statements. In Findings of the Association for Computational Linguistics (ACL 2024).
18. Sanjay Nakharu Prasad Kumar, “Recent Innovations in Cloud-Optimized Retrieval-Augmented Generation Architectures for AI-Driven Decision Systems.” Engineering Management Science Journal, Vol. 9, No. 4, 2025. https://doi.org/10.59573/emsj.9(4).2025.81
19. Chen, L., & Takamura, H. (2025). Agent AI for Finance. Singapore: World Scientific Publishing.
20. Sanjay Nakharu Prasad Kumar, “Optimal Weighted GAN and U-Net Based Segmentation for Phenotypic Trait Estimation of Crops Using Taylor Coot Algorithm.” Applied Soft Computing, Elsevier, 2023. https://www.sciencedirect.com/science/article/abs/pii/S1568494623004143
21.Zhang, X., Yau, S. K. S., Lin, Z., et al. (2024). Large Language Model Based Multi-Agents: A Survey of Progress and Challenges. In Proceedings of IJCAI 2024.
22.Sanjay Nakharu Prasad Kumar, “Improving Fraud Detection in Credit Card Transactions Using Autoencoders and Deep Neural Networks.” The George Washington University, 2022. https://scholarspace.library.gwu.edu/concern/gw_etds/cv43nx607
23.Singh, P. (2024). Distributed Intelligence in Financial Markets: A Multi-Agent Perspective. Modern Game Studies, 44(3), 287–304. https://journals.sagepub.com/doi/abs/10.3233/MGS-230039
24.Sanjay Nakharu Prasad Kumar, “An Approach for DoS Attack Detection in Cloud Computing Using Sine Cosine Anti-Coronavirus Optimized Deep Maxout Network.” International Journal of Pervasive Computing and Communications, Emerald, 2023. https://doi.org/10.1108/IJPCC-05-2022-0197
25.Sanjay Nakharu Prasad Kumar, “Scalable Cloud Architectures for AI-Driven Decision Systems.” Journal of Computer Science and Technology Studies, Al-Kindi Publishers, August 2025. https://al-kindipublishers.org/index.php/jcsts/article/view/10545
26.Sanjay Nakharu Prasad Kumar, “AI and Cloud Data Engineering Transforming Healthcare Decisions.” SAR Council, August 2025. https://sarcouncil.com/2025/08/ai-and-cloud-data-engineering-transforming-healthcare-decisions
27.Sanjay Nakharu Prasad Kumar, “Deep Embedded Clustering with Matrix Factorization Based User Rating Prediction for Collaborative Recommendation.” Microprocessors and Microsystems, SAGE, 2022. https://journals.sagepub.com/doi/abs/10.3233/MGS-230039
28.Sanjay Nakharu Prasad Kumar, “Ethical Frameworks for AI-Driven Decision Systems: A Comprehensive Analysis.” Global Journal of Computer Science and Technology, Global Journals, October 2025. https://globaljournals.org/GJCST_Volume25/6-Ethical-Frameworks.pdf
29. Sanjay Nakharu Prasad Kumar, “Hallucination Detection and Mitigation in Large Language Models: A Comprehensive Review.” Journal of Information Systems Engineering and Management (JISEM), October 2025. https://www.jisem-journal.com/index.php/journal/article/view/13133
