Open Access

The Algorithmic Frontier of Financial Intermediation: A Comprehensive Analysis of Agentic AI, Large Language Models, And Blockchain Integration in Modern Fintech Ecosystems

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Joshua Hoffman ¹ ,

⁴ Department of Financial Engineering and Computational Intelligence, London School of Economics and Political Science, United Kingdom

Abstract

The global financial landscape is undergoing a radical transformation characterized by the convergence of high-capacity computational intelligence and decentralized ledger technologies. This research explores the profound impact of agentic artificial intelligence (AI) and Large Language Models (LLMs) on the evolution of financial services, moving from traditional automated systems to autonomous, self-driven entities capable of complex decision-making. By synthesizing foundational machine learning techniques in credit scoring with contemporary advancements in generative AI, the study identifies a shift from "FinTech" to "TechFin," where data-driven logic precedes financial function. The research delves into the architecture of specialized financial agents, such as FinRobot and StockAgent, examining their capacity for real-time market sentiment analysis and asset allocation. Furthermore, the article investigates the role of confidentiality-preserving blockchain systems in facilitating secure transactions within these agentic frameworks. Through an exhaustive theoretical elaboration, this work addresses the regulatory challenges and ethical considerations of delegated financial autonomy, proposing a synergistic model that integrates machine learning and agent-based modeling to enhance investment accuracy and operational efficiency. The findings suggest that while AI agents significantly augment knowledge work and underlying asset reviews, the future of financial stability depends on robust grounding mechanisms and multidisciplinary regulatory oversight.

Keywords

Agentic Artificial Intelligence, Large Language Models, Fintech, Blockchain, Credit Scoring

References

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.

Ganesh, S., Ardon, L., Borrajo, D., Garg, D., Sehwag, U. M., & Narayanan, A. L. (2024). Generative AI agents for knowledge work augmentation in finance. Annual Review of Control, Robotics, and Autonomous Systems, 8(1), 189-210.

Han, X., Wang, N., Che, S., Yang, H., Zhang, K., & Xu, S. X. Enhancing investment analysis: optimizing AI-Agent collaboration in financial research. 538-46.

Jadhav, A., & Mirza, V. (2025). Large language models in equity markets: applications, techniques, and insights. Frontiers in Artificial Intelligence, 8, 1608365.

Krishnan, N. AI agents: evolution, architecture, and real-world applications.

Li, J., Zhang, J., Li, H., & Shen, Y. An agent framework for real-time financial information searching with large language models.

Sinha, A., Agarwal, C., & Malo, P. FinBloom: knowledge grounding large language model with real-time financial data.

Srivastava, P. R., & Kumar, V. (2015). An approach toward credit scoring using machine learning classifiers. Neural Computing and Applications, 28(1), 1-12.

Taddy, M. (2019). Business data science: Combining machine learning and economics to optimize, automate, and accelerate business decisions. McGraw-Hill Education.

Thakor, A. V. (2020). Fintech and banking: What do we know? Journal of Financial Intermediation, 41, 100833.

Tian, F., Byadgi, A., Kim, D. S., Zha, D., White, M., & Xiao, K. Customized fingpt search agents using foundation models. 469-77.

Wan, X., Deng, H., Zou, K., & Xu, S. Enhancing the efficiency and accuracy of underlying asset reviews in structured finance: the application of multi-agent framework.

13. Wang, Y., & Kogan, A. (2018). Designing confidentiality-preserving Blockchain-based transaction processing systems. International Journal of Accounting Information Systems, 30, 1-18.

Xing, F. Z., Cambria, E., & Malandri, L. (2018). Intelligent asset allocation via market sentiment views. IEEE Computational Intelligence Magazine, 13(4), 25-34.

Yang, H., Zhang, B., Wang, N., Guo, C., Zhang, X., & Lin, L. FinRobot: an open-source AI agent platform for financial applications using large language models.

Zetzsche, D. A., Buckley, R. P., Barberis, J. N., & Arner, D. W. (2018). From FinTech to TechFin: The regulatory challenges of data-driven finance. New York University Journal of Law & Business, 14(2), 393-446.

Zhang, C., Liu, X., Zhang, Z., Jin, M., Li, L., &Wang, Z. When AI meets finance (stockagent): large language model-based stock trading in simulated real-world environments.

Zhang, W., Valencia, A., & Chang, N. B. (2021). Synergistic integration between machine learning and agent-based modeling: a multidisciplinary review. IEEE Transactions on Neural Networks and Learning Systems, 34(5), 2170-90.

Upadhyay, H. (2026). Agentic AI Orchestration Frameworks for Composable Commerce Ecosystems: A Case Study of Enterprise Transformation . American Journal of Technology, 5(1), 40–54. https://doi.org/10.58425/ajt.v5i1.476

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The Algorithmic Frontier of Financial Intermediation: A Comprehensive Analysis of Agentic AI, Large Language Models, And Blockchain Integration in Modern Fintech Ecosystems

Abstract

Keywords

References

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