AI Agents in Finance and Fintech: A Scientific Review of Agent-Based Systems, Applications, and Future Horizons
Journal
Computers, Materials & Continua
Date Issued
2026
Author(s)
Rizinski, Maryan
DOI
10.32604/cmc.2025.069678
Abstract
Artificial intelligence (AI) is reshaping financial systems and services, as intelligent AI agents increasingly
form the foundation of autonomous, goal-driven systems capable of reasoning, learning, and action. This review
synthesizes recent research and developments in the application of AI agents across core financial domains. Specifically,
it covers the deployment of agent-based AI in algorithmic trading, fraud detection, credit risk assessment, roboadvisory, and regulatory compliance (RegTech).The review focuses on advanced agent-based methodologies, including
reinforcement learning, multi-agent systems, and autonomous decision-making frameworks, particularly those leveraging large language models (LLMs), contrasting these with traditional AI or purely statistical models. Our primary
goals are to consolidate current knowledge, identify significant trends and architectural approaches, review the practical
efficiency and impact of current applications, and delineate key challenges and promising future research directions.
The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance, yet presents
complex technical, ethical, and regulatory challenges that demand careful consideration and proactive strategies. This
review aims to provide a comprehensive understanding of this rapidly evolving landscape, highlighting the role of
agent-based AI in the ongoing transformation of the financial industry, and is intended to serve financial institutions,
regulators, investors, analysts, researchers, and other key stakeholders in the financial ecosystem.
form the foundation of autonomous, goal-driven systems capable of reasoning, learning, and action. This review
synthesizes recent research and developments in the application of AI agents across core financial domains. Specifically,
it covers the deployment of agent-based AI in algorithmic trading, fraud detection, credit risk assessment, roboadvisory, and regulatory compliance (RegTech).The review focuses on advanced agent-based methodologies, including
reinforcement learning, multi-agent systems, and autonomous decision-making frameworks, particularly those leveraging large language models (LLMs), contrasting these with traditional AI or purely statistical models. Our primary
goals are to consolidate current knowledge, identify significant trends and architectural approaches, review the practical
efficiency and impact of current applications, and delineate key challenges and promising future research directions.
The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance, yet presents
complex technical, ethical, and regulatory challenges that demand careful consideration and proactive strategies. This
review aims to provide a comprehensive understanding of this rapidly evolving landscape, highlighting the role of
agent-based AI in the ongoing transformation of the financial industry, and is intended to serve financial institutions,
regulators, investors, analysts, researchers, and other key stakeholders in the financial ecosystem.