Jonathan Mitchell is Financial Industry Lead at Founder Shield.
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The conversation around artificial intelligence is rapidly evolving. We're moving beyond simple chatbots that answer questions and generative models that create content on command. The next big thing in finance is Agentic AI—autonomous systems designed to perceive their environment, plan a course of action, and execute multi-step tasks with minimal human intervention.
For banks and fintechs, this is more than a technological upgrade; it's a paradigm shift with the potential to automate data entry, streamline loan approvals, enhance fraud detection, and create hyper-personalized customer experiences. However, as this technology moves from theory to practice, so do the overlooked risks. In this article, let’s define agentic AI, uncover its hidden risks, and outline a strategic path for safe and responsible adoption.
What Agentic AI Means for Banks and Fintechs
At its core, agentic AI represents a fundamental shift from reactive to proactive technology. Think of it this way: a traditional AI chatbot is like a receptionist waiting for a call. It can answer a limited set of questions based on a script, but it can’t anticipate needs or act on its own. An agentic AI, by contrast, is more like a self-starter who not only schedules a meeting but also sends follow-up materials, books the room, and handles any rescheduling—all with minimal supervision. It’s goal-oriented, taking initiative to complete multi-step tasks across different systems.
This proactive approach is unlocking a new wave of operational efficiency and customer-facing innovation. In the back office, for example, agents are revolutionizing workflows. For loan approvals, an agent can autonomously collect and verify borrower data, run a credit check against multiple bureaus, and flag potential compliance issues—all in minutes. This dramatically reduces the review cycle time and frees up human underwriters to focus on complex cases.
Similarly, for regulatory compliance, an agent can continuously monitor for new updates from government bodies and automatically adjust internal reporting frameworks, ensuring the bank stays compliant without manual oversight.
On the customer-facing side, agentic AI is enabling truly personalized experiences. Instead of a customer having to call in about an issue, an agent could proactively monitor their spending, detect unusual activity like a pending overdraft, and automatically initiate a solution, such as a temporary credit line increase or a savings plan recommendation.
These functions not only enhance satisfaction but also build trust. In fraud detection, agents go beyond simple rule-based alerts to analyze real-time transaction patterns and behavioral data. They can identify a novel fraud scheme as it happens and take immediate action, such as freezing an account or requiring additional verification, before a human is even aware of the threat. It’s this combination of increased speed, reduced costs, and enhanced personalization that has everyone in the financial world talking.
Beyond the Hype: The Real Risks of Agentic AI
While the potential of agentic AI is undeniable, its autonomous nature introduces a new layer of risk that banks and fintechs must proactively manage.
The first and most significant concern is the potential for algorithmic bias and unfair decisions. Agentic AI models are trained on vast datasets of historical financial information. If this data reflects past human biases—for instance, in lending criteria or credit risk assessments—the AI will learn and perpetuate those same prejudices at an unprecedented scale.
This can lead to discriminatory loan approvals and unfair outcomes for certain customer segments, creating severe legal and reputational damage. The solution lies in building transparent, explainable models so institutions can understand and audit how decisions are made, ensuring fairness is built into the system from the start.
Beyond bias, the interconnected architecture of agentic AI creates significant security gaps and an expanded attack surface. Unlike a single, siloed program, an agentic system acts by communicating with numerous internal and external tools and APIs. This web of connections is an open invitation for malicious actors.
For example, a hacker could exploit a vulnerability in a third-party API to manipulate an agent's behavior, leading it to execute fraudulent transactions or leak sensitive customer data. A more subtle and insidious threat is an “adversarial attack,” where a hacker subtly manipulates an agent’s input to corrupt its reasoning and decision-making process.
Finally, there’s the risk of unintended consequences and systems "going off course." The very autonomy that makes agentic AI so powerful is also its greatest vulnerability. An agent’s goal-oriented logic, while efficient, may lead to an outcome that is technically correct but strategically or ethically problematic.
For example, an agent tasked with maximizing a portfolio’s returns might make a series of high-risk trades that ultimately destabilize it. Furthermore, like other AI models, agents can sometimes “hallucinate” or act on false information, causing a cascading failure without human oversight. To mitigate this, it’s vital to utilize a “human-in-the-loop” model, where a person is the ultimate arbiter for critical, high-stakes decisions.
Risk Management Steps for Smart, Safe AI Adoption
For financial institutions, navigating the risks of agentic AI requires a proactive and strategic approach. The key is to move past reactive measures and embed a "compliance-by-design" framework into the foundation of every AI system. This means that risk management is not an afterthought; it's a core component of the development process.
One of the most critical steps is to prioritize transparency and explainability: explainable artificial intelligence or XAI. You must choose AI models that can clearly articulate how they reached a decision. This allows for audits, builds trust with regulators, and gives human experts the ability to review and validate the system's logic.
Alongside this, strong data governance is non-negotiable. Without a strict policy for data quality and integrity, you risk training your AI on flawed or biased information, which will inevitably lead to unfair outcomes. To maintain control, a "human-in-the-loop" model is essential. In this framework, autonomous agents are empowered to handle routine, low-risk tasks, but they are programmed to automatically escalate high-stakes or anomalous decisions to a human for final review.
Furthermore, a comprehensive strategy for securing and monitoring your AI ecosystem is crucial. Treat agentic AI with the same rigor as you would your core IT infrastructure. This includes implementing robust access controls that grant agents only the permissions absolutely necessary to complete their task, thereby minimizing the potential for malicious exploitation.
Continuous monitoring through real-time dashboards is also vital to track an agent's behavior, detect any anomalies, and ensure it operates within predefined parameters. Finally, establish a clear incident response plan, including insurance programs, for what to do in the event an agent malfunctions or is compromised. By starting small with well-defined, low-risk use cases and gradually building a robust framework, banks can confidently scale their adoption of agentic AI.
Conclusion
Agentic AI represents a powerful new chapter for banks and fintechs, offering the potential for unprecedented efficiency and innovation. However, its true value can only be realized by embracing a strategic, risk-aware approach. By implementing a framework of transparency, strong governance, and continuous monitoring, financial institutions can move beyond the hype and confidently enter this new era, turning the promise of agentic AI into a reality of secure, strategic growth.
About Jonathan Mitchell:
A proud University of Georgia alumnus with an Emory MBA, Jonathan has spent 11 dynamic years navigating the insurance landscape for top brokerages. He specializes in hospitality, real estate, technology, financial institutions, private equity, and Fintech. Beyond his expertise, Jonathan's enthusiasm for mentorship, entrepreneurship, and economics shines, all while passionately cheering on UGA football. His team-first mentality consistently delivers exceptional client support.
