From Assistance to Action: What Agentic AI Means for Financial Institutions

Agentic AI moves beyond just making suggestions to AI that can make decisions and take actions – but when money and critical data are involved, the stakes are high. Here's how Fiserv is building an agentic AI operating system with guardrails to enable the next wave of innovation for clients.

Imagine you’re a branch executive opening a Monday morning email from the bank president who wants a list of your primary accounts at risk - with your plan to save them. And she wants it later today.

Today, a request like that might trigger a familiar routine – dashboards, analyst reviews, calls and follow-ups. Increasingly, an AI agent can handle the sequence: scan recent transaction patterns for early warning signals, identify who’s at risk and push out customized offers – before the customer quietly moves on. 

The last part – not just identifying but making decisions and taking actions – is what defines “agentic AI” as fundamentally different from chatbot-style Q&A interfaces.

 

The speed of AI

Even in an industry accustomed to accelerating digital change – from the earliest days of internet banking to the cloud and open banking – the velocity of AI innovation has been unprecedented. 

At Fiserv, AI is central to how the company is prioritizing execution – using AI to help deliver stronger client outcomes while also improving how work gets done inside the enterprise. 

“We’re embracing an AI-first and client-first mentality. That means challenging our people to look at every business process to improve and deliver quality results, faster for clients,” says Dhivya Suryadeva, Co-President, Fiserv. 

“The guiding principle encourages bold innovation paired with careful consideration. Security, compliance, and responsible business practices remain fundamental priorities.”

That’s an important frame for how the company is approaching agentic AI – with the experience and understanding that forty-plus years of serving financial institutions brings. Compliance, security and resilience are built-in from the beginning.

 

What is agentic AI? And what can it do for banks and credit unions?

Agentic AI goes beyond predefined workflows and rules-based robotic process automation. “It can plan, decide, act and adjust across systems to reach a goal – even when conditions are uncertain,” says Srini Krish, Head of Technology and Operations, Financial Solutions, Fiserv. 

For example, an agent might spot a suspicious pattern, gather the right context, initiate the appropriate action and prepare the documentation a reviewer or regulator expects. 

While highly aware of the promise of agentic AI, most institutions are still in the early stages of overall AI adoption. 

“Many institutions have tested AI in pockets – like service chatbots and document processing – but haven’t yet run AI in a scalable way that owns outcomes,” says Krish. “Agentic AI changes the question from “What can AI suggest?” to “What can AI reliably execute, with the right oversight?”

The early value of agentic AI isn’t mysterious. It’s the work financial institutions already do every day – service, risk, operations and lending – made faster, more consistent and more proactive. The benefit the agent brings over assisted AI is that it can string tasks together and push a case forward instead of just surfacing insights for the human to act on.

 

Agentic AI use cases in financial services

Near term, Krish points to domains where rules are clear, volume is high and outcomes are measurable – areas like end-of-day reconciliation, onboarding and alert triage. In those workflows, agents don’t just accelerate tasks; they reduce rework by keeping context intact from start to finish.

• Early warning for attrition: agents that flag customers at risk of leaving based on transaction patterns – then suggest the next-best action.
• Smarter recommendations: agents that combine call transcripts and account activity to surface relevant products or advice, with context a banker can trust.
• Operational triage: agents that route exceptions, collect missing information, and move work to resolution instead of creating more queues.

The next wave extends beyond pattern detection into judgment-heavy decisions in more complex and high-stakes areas: 

• Commercial lending support: agents that continuously absorb internal performance data alongside external signals to help manage exposure, covenants and portfolio health.
• Relationship intelligence at scale: agents that prepare a banker for the next conversation – summarizing context, spotting needs and drafting follow-ups within approved policy.
• Always-on risk response: agents that monitor patterns across channels and coordinate a response while creating an audit-ready record for review.

Krish notes that agentic AI is evolving faster than traditional planning cycles can accommodate. “Given the pace of change, there are use cases on the horizon that nobody has imagined yet,” he said. “We’re co-creating those possibilities side-by-side with clients. It’s a partnership of continual iteration.”

 

Managing risk in agentic AI

When AI has the power to decide and act, rather than just suggest, it introduces entirely new levels of risk. Important decisions must be transparent and traceable. Actions must be attributable to human owners. And people and systems must be able to intervene, override, or stop an agent when needed.

Questions that must be answered include:

• What guardrails keep an agent inside policy (access, permissions, approvals)?
• Who’s accountable for an agent’s actions– and how is intent captured?
• How do you observe what agents are doing in real time – and stop them instantly if needed?
• What’s auditable: can you trace decisions and produce evidence for compliance?

“Agentic AI only works if it operates within defined boundaries,” said Vishal Dalal, Chief Product Officer, Financial Solutions at Fiserv. “You need guardrails, ownership and the ability to reproduce every interaction if a regulator or auditor asks for it.”

Based on the utility of agentic AI, it’s not hard to envision a near future where institutions have hundreds – or even thousands – of agents working on their behalf. In addition to productivity and performance gains, this creates a new challenge: managing a complex web of agent risk across models, tools, data access, approvals and outcomes.

 

An operating system for agentic AI in financial services

Fiserv is building to enable agentic AI today and looking ahead to support future growth with the scalability, guardrails and resilience required for financial services. One new innovation is agentOS by Fiserv, an agentic AI operating system to help institutions build, secure, observe and scale agents across their institutions. 

agentOS unlocks data across core platforms and other systems providing the controls and capabilities to deploy agents across areas like service, fraud, payments and engineering. The approach is outcome-first, enabling the institution to get rapid value and move away from disconnected agents and pilot projects to a governed system of record for agentic AI within the institution. 

Ultimately, it will also serve as fintech agent app store: agents deployed by the institution and Fiserv, together with those from trusted third parties, can operate in an environment that is built to the requirements of financial services.

“The most important thing for clients to know is their data is secure and they own it – our role is to give them access to it and build the appropriate guardrails to help them leverage it,” said Krish. “That’s the secret sauce here. Getting an agent to do end-to-end execution isn’t that hard but the governance and data integrity is a unique challenge we can solve to help our clients.” 

 

Managing your future digital financial workforce

As agentic AI matures, agents can’t be managed as individual endpoints, which would create complex webs of risk. Instead, the paradigm begins to look more like an AI workforce. And just as HR evolved to manage human capital, financial institutions will need an agentic operating system to manage, govern and support an always-on workforce of AI agents.

Agentic AI has broad potential implications for how products and initiatives are rolled out within an institution. Today, product rollout and process improvements begin within constraints. Product managers and businesses define requirements. IT translates those requirements into what the systems can support and then engineering builds to specification. Innovation moves at the speed of infrastructure, integration and release cycles. 

According to Dalal, this is the most exciting development on the horizon: as AI models and capabilities improve, and with an architecture and governance operating system like agentOS, institutions can unlock data across disparate, legacy systems.

“The paradigm is intelligence, not infrastructure. If technology were not a constraint, what would you do?” Dalal asks. “That’s the real shift, moving away from thinking in terms of infrastructure and toward thinking in terms of outcomes.”

That means product and business teams start thinking in terms of optimal outcomes, deploying capabilities faster and more iteratively, and measuring success based on how agents are performing for customers and the institution. IT and engineering teams are focused on strategic priorities, supporting governance, risk and availability across the institution, rather than supporting individual initiatives, coordinating workarounds to overcome limitations of their integrations or infrastructure.

Agentic AI isn’t magic. It needs strong governance, security, and accountability. Even more care must be taken to prevent issues like model bias, hallucinations or agents acting outside of policy. However, when applied as an intelligence layer over infrastructure, it becomes a practical engine for better decisions and faster action. For clients, Fiserv is partnering to enable the possibilities of agentic AI, so our banks and credit union partners can realize the best outcomes for their institutions, customers and members.