Agentic AI represents a significant advancement beyond the generative AI tools that have gained attention since 2023. For financial services firms, these autonomous systems offer practical solutions to longstanding challenges, such as streamlining operations, improving decision-making, and enhancing client experiences.
The financial industry is already seeing the impact of this technology. In a 2024 joint study conducted by the Financial Conduct Authority (FCA) and the Bank of England, researchers found that 55% of all AI use cases at financial services firms “have some degree of automated decision-making.”
For financial leaders seeking actionable insights into integrating this technology into their operations, these autonomous systems promise to change how work gets done, decisions are made, and clients are served in the industry. This article explores what agentic AI is, how it works, its current applications in financial services, and practical considerations for implementation.
What Is Agentic AI?
Agentic AI represents the next step beyond popular generative AI tools. At its core, agentic AI builds on large language models (LLMs) enhanced with additional capabilities. Unlike generative AI systems that require frequent human input, agentic AI systems can take action independently—in some cases, without human interaction at all.
Specifically, agentic AI systems “go beyond passive data retrieval and response generation,” as Moody’s describes. “Unlike traditional AI… agentic AI autonomously determines what actions to take, plans multi-step workflows, and adapts based on real-time data.”
The key capabilities that distinguish agentic AI include:
- Autonomy and action: Making decisions, executing tasks, and taking action with minimal human intervention through integrated tools and services.
- Adaptability and learning: Continuously improving performance by learning from feedback and incorporating new information.
- Coordination and perception: Working with other systems, APIs, and databases to gather information and complete complex workflows.
- Planning: Breaking down complex goals into actionable steps and determining the best course of action.
In these ways, agentic AI behavior is not unlike that of a human being—at least in a limited capacity. As we will share, this introduces both opportunities and risks in the context of the financial services industry.
Current Applications of Agentic AI in Financial Services
Already, financial institutions can implement different agentic AI models. For example, in orchestrator-worker frameworks, a central AI delegates specialized tasks to worker AIs handling specific functions like data analysis or client communication. Here we identify some agentic AI use cases that may drive value and help overcome common use cases at financial firms.
Compliance and Regulatory Management
Already, firms must monitor constantly changing regulations across jurisdictions. Agentic AI can help by continuously tracking regulatory changes, analyzing their impact, and implementing compliance measures.
For example, in trade finance, tasks like evaluating flagged transactions require gathering data from multiple sources. Agentic AI can streamline this process by codifying workflows and providing summarized recommendations for human review.
“Growth of regulatory complexity has made it difficult for companies to comply with the routine thousand-page regulation in a time-sensitive and cost-effective way,” according to one expert in a January 2025 Citi report.” Regulatory AI makes it possible to convert these complicated regulations into AI agents that are capable of compliance determinations.”
Fraud Detection and Security
Banks face increasingly sophisticated threats, especially with the rise of deepfakes. “Deepfake scams have increased more than 2000% over the last three years and banks/FinTechs are one of the most targeted victims of deepfake attacks,” according to the Citi report.
Agentic AI can analyze communications for signs of manipulation, examine transaction patterns, and respond to threats faster than human teams alone. In one use case, a major financial institution implemented an AI-powered system to analyze calls and videos for synthetic deepfakes in its private banking division. The system processed audio streams in real-time through a secure pipeline, examining multiple aspects of calls for signs of manipulation without introducing latency. The firm found that 37 calls per week were potentially manipulated, highlighting both the presence of deepfake threats and the system’s effectiveness in detecting them.
Client Onboarding and KYC Processes
Agentic AI is transforming client onboarding and Know Your Customer (KYC) processes by automating data collection, verification, and compliance tasks, significantly reducing the time and resources required. For instance, Mastercard has implemented AI-powered “agents” that accelerate onboarding by automating routine tasks and answering critical questions, ensuring a smoother experience for both clients and staff, American Banker reports.
Jürgen Eckel, managing director and partner at global management consulting firm Boston Consulting Group (BCG), says client onboarding is among the key opportunities for agentic AI. “BCG research found that there’s widespread optimism about the potential of AI agents, with 67% of companies considering them in their transformation journeys,” said Eckel in a February 2025 FStech article.
Wealth Management and Advisory
One major European bank’s AI-powered platform enables advisors to efficiently narrow down investment options. The system can filter products based on specific criteria like bond maturity, yield, and ratings. Though this system relies on prompts, agentic AI systems in wealth management could later analyze market trends, identify correlations, and fine-tune portfolio allocations to align with individual client objectives and risk tolerances, without specific instructions from wealth managers themselves.
Agentic AI Implementation Considerations
It may be some time before most financial firms adopt agentic AI solutions “due to the weight of regulations, internal processes, and governance requirements,” as Citi describes. “However, they may find their speed of adoption being accelerated by client behavior—individuals are adopting AI fast, and agentic AI may be no different.” Here are some technical aspects all FIs should consider, no matter their stage of assessment, adoption, and implementation of agentic AI.
Technical Requirements
Financial institutions need to assess their technology infrastructure before adopting agentic AI. Integration with legacy systems, data accessibility, and computational resources all affect successful implementation.
According to Citi, Martin Moeller from Microsoft suggests a “compose” approach where firms use modular building blocks to create customized solutions. “This is enabled by tools like low-code/no-code platforms…allowing companies to create tailored agents and workflows without requiring deep technical expertise.”
Data Quality
Agentic AI systems depend on high-quality, accessible data to function effectively. Clean, well-structured information is crucial for these advanced AI systems to operate at their full potential. Moving from initial testing to full implementation involves refining development and operations processes, tackling issues of algorithmic bias, and ensuring the AI’s decision-making can be clearly understood and explained.
Governance Framework
As AI takes on more autonomous roles, governance must evolve. Effective oversight includes:
- Transparent decision tracking
- Human review of AI outputs
- Clear accountability structures
- Regular auditing and testing
As Pawel Gmyrek, Senior Researcher at the International Labour Organization, describes, “A ‘human above the loop’ approach remains essential, with AI complementing human abilities rather than replacing the judgment and accountability vital to the sector,” World Economic Forum reports. “Autonomous AI poses unique governance challenges… Transparency is crucial for maintaining trust,” the article continues.
Next Steps for Financial Leaders on Agentic AI
Financial leaders looking to implement agentic AI should approach the process strategically and methodically. The following steps provide a potential roadmap for successful adoption, balancing innovation with practical considerations.
- Identify use cases with clear business value and reasonable complexity. Begin by pinpointing specific areas where agentic AI can deliver tangible benefits to your organization. Focus on processes that are currently inefficient or error-prone, but not so complex that implementation becomes overwhelming or risky.
- Build AI literacy across management teams. Invest in training programs to build trust and ensure leadership understands both the potential and limitations of agentic AI. This understanding will enable more informed decision-making and help align AI initiatives with broader business strategies.
- Develop governance specific to autonomous systems. Create a framework that addresses the unique challenges posed by AI that can make decisions independently. This should include clear guidelines for human oversight, accountability measures, and processes for auditing AI-driven outcomes.
- Work with regulators, directly or indirectly, to shape appropriate oversight frameworks. Engage proactively with regulatory bodies and resources to help develop guidelines that balance innovation with consumer protection. This deliberate approach can help ensure compliance while also positioning your organization as a leader in responsible AI adoption.
- Start small with targeted implementations before scaling. Begin with pilot projects in controlled environments to test and refine your approach. Use the insights gained from these initial deployments to inform broader rollouts across the organization.
Putting Clients’ Trust and Security First
Financial firms that thoughtfully implement agentic AI while addressing its challenges will gain advantages in efficiency, client service, and risk management. The key is balancing innovation with appropriate controls, focusing on practical business outcomes for clients rather than technology for its own sake. Firms that can balance AI autonomy with human oversight will realize both essential client trust and success.
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