By Sumanas Kar, Senior Regional Sales Director – Asia, Vymo
In financial services, decision-making has traditionally been supported by data and analytics. But while banks and insurers have become better at knowing what needs to be done, the actual act of doing often depends on people noticing, interpreting, and responding. That model is reaching its limits.
Agentic AI changes the equation. These systems are designed to act. They operate with a defined goal, process signals from multiple sources, and trigger tasks based on established logic. They do not replace core workflows but enhance them by ensuring that routine, time-sensitive actions are carried out consistently. For the BFSI sector, where timing, consistency and compliance are non-negotiable, this kind of intelligence offers real operational leverage.
The execution gap still persists
Across lending, insurance and retail banking, technology has helped organisations generate visibility. Dashboards track performance, alerts signal delays, and reports highlight drop-offs. Yet teams continue to spend time chasing follow-ups, reassigning stalled cases, and nudging internal stakeholders to act.
The issue is not access to information. It is the lack of systems that can respond to it. That is where Agentic AI fits in. When a claim is ready but not processed, a lead sits untouched, or a document waits in queue for verification, agentic logic can intervene and resolve the bottleneck automatically.
This is not speculative. Some of the most effective implementations today are solving precisely these small but recurring lags in execution.
Real-world applications that are already working
Several BFSI firms are already deploying agentic models within targeted areas of their operations. The results are visible in micro-interventions that improve process flow and reduce manual load. Autonomous financial advisors, powered by agentic logic, are now capable of not just reacting to user input, but proactively monitoring markets, assessing customer portfolios, and recommending real-time changes.. In parallel, agentic systems are transforming customer service by acting as intelligent finance assistants, guiding users through complex processes such as mortgage applications or claims filing. Unlike traditional chatbots, these agents anticipate user needs, send reminders for bills or renewals, and tailor responses based on intent and sentiment, effectively orchestrating entire customer journeys.
Supporting human judgement, not replacing it
There is often a concern that increased AI-driven automation may displace people. That is not the goal of Agentic AI. These systems are not designed to make judgement calls or manage relationships. They are built to handle tasks that are defined, repeatable and often neglected.
By automating such decisions, teams are free to focus on areas where their skills are most valuable: customer engagement, strategic problem-solving and exception handling. The outcome is not just greater productivity, but also better alignment between roles and responsibilities.
The role of Agentic AI in BFSI strategy
For Agentic AI to succeed, it must be integrated into operational strategy. This begins by identifying workflows where progress depends on repetitive human actions that follow predictable logic. These are often approval chains, verifications, task handoffs, and follow-ups.
Once identified, clear rules need to be defined. What conditions trigger an action? When is escalation required? What qualifies as a closed loop? The strength of an agentic system lies in its ability to act with precision, but that depends on well-designed logic and relevant signals.
Data access is equally important. Agentic AI systems require context. That means drawing from activity history, behavioural cues, workflow states and timing patterns. Is the customer nearing a decision point? Is the rep overdue for a follow-up? Has a process stalled beyond the expected SLA? These signals guide the system’s actions and determine its accuracy.
Finally, deployment must be iterative. Starting with low-risk use cases allows teams to monitor outcomes, refine logic and build internal trust. Over time, agentic systems can be scaled across departments, offering compounding value with every task they touch.
Why the timing matters now
Financial institutions are operating under increasing pressure. Margins are tighter, service expectations are higher, and regulatory scrutiny is expanding. Traditional process models, which depend heavily on human bandwidth to manage routine tasks, are proving unsustainable.
Agentic AI provides a practical path forward. It does not require new teams or massive infrastructure shifts. What it needs is clarity around operational logic and willingness to let systems act when they can do so reliably.
The way forward
The adoption of Agentic AI represents more than just another technology trend. It marks a shift in how financial services organisations manage execution. Rather than relying on supervision or escalation to keep tasks moving, agentic systems allow actions to be taken precisely when they are needed, without delay or oversight. To ensure compliance when deploying Agentic AI in BFSI, institutions must maintain transparency, human oversight, and data governance. AI actions should be explainable and auditable, especially in sensitive areas like credit decisions and fraud detection. Regulatory frameworks also require that certain decisions, particularly those with significant customer impact, involve human review, reinforcing accountability and building trust in autonomous operations.
This is about ensuring that complexity does not prevent action. In that, Agentic AI offers a practical, measurable, and scalable way to build more responsive and resilient BFSI operations.