Agentic AI in BFSI: Moving from Pilots to Production with Confidence

The Pressure is Mounting

Financial institutions are operating in a world of increasing complexity. Regulations aren’t easing. Customer expectations are rising. And core systems, while stable, weren’t built for speed or adaptability.

Meanwhile, data continues to explode. Unstructured forms. Call logs. Emails. PDFs. Claims. Spreadsheets. It’s everywhere and nowhere, all at once.

Here’s what this means: banks and insurers that still rely on brittle automation or static AI tools are already behind.

That’s why Agentic AI is gaining ground. Because it doesn’t just process data. It reasons. It acts. It learns. And it works across silos.

What is Agentic AI?

At its core, Agentic AI refers to intelligent agents that perceive, decide, and act autonomously within a defined scope. These aren’t traditional bots. They’re task-specific systems capable of breaking down objectives, adapting in real time, and triggering the right workflows with minimal human input.

These agents can:

• Parse documents and extract key fields
• Trigger next-best actions across enterprise systems
• Collaborate with other agents
• Improve through continuous feedback
• Interact via natural language while maintaining traceability

Unlike conventional AI tools, which act on predefined prompts or workflows, agentic systems are goal-driven and context-aware.

Capgemini estimates that AI agents could unlock $450 billion in value by 2028, through a mix of cost savings and revenue uplift. Yet fewer than 16% of enterprises have a clear strategy for deploying them at scale.

Why BFSI is a Natural Fit

Agentic AI isn’t a generic solution. It thrives where processes are:

• Data-intensive
• Repeatable but decision-heavy
• Regulated
• Spread across teams or systems

That’s the BFSI sector in a nutshell.

Whether you’re underwriting a loan, investigating fraud, settling claims, or monitoring transactions, these are precisely the kinds of high-friction, high-volume workflows where agentic automation can deliver.

In fact, 93% of leaders in financial services believe those who scale AI agents in the next year will gain a competitive edge.

But success isn’t just about ambition. It’s about execution.

Where BFSI Firms Are Struggling

Most BFSI organizations start strong, a chatbot pilot, a document parser, maybe an RAG-powered assistant. But then progress stalls.

Here’s why:

1. Compliance & Explainability

Financial workflows don’t tolerate black boxes. Agents must justify their decisions, be auditable, and align with local and global regulations (GDPR, HIPAA, SOX, etc.).

2. Data Fragmentation

Information sits in policy systems, underwriting tools, CRM platforms, legacy databases, and Excel files. Integrating these sources is non-trivial.

3. Lack of Trust

According to Capgemini, only 27% of organizations currently trust fully autonomous AI agents, down from 43% the previous year. That’s a steep decline, driven by real-world concerns, not just fear of the unknown.

4. Process Diversity

A claims process in India looks nothing like one in the UK. Same goes for underwriting or onboarding. Local rules and institutional quirks add complexity.

5. Weak ROI Visibility

Even when agents are deployed, many organizations struggle to justify the cost. The issue isn’t always the technology, it’s poor alignment between agent capabilities and business value. When companies don’t plan around the right use cases, or fail to define success metrics upfront, the result is a solution in search of a problem. Without a clear ROI story, AI adoption loses momentum internally and buy-in starts to fade.

Where Agentic AI is Already Working in BFSI

Let’s look at actual use cases across banking, insurance, and financial services: