By Deviprasad Thrivikraman Pillai, Managing Director, Zentis AI
For years, the BFSI industry has relied on automation to manage scale and efficiency. Robotic Process Automation (RPA) reduced manual work, while machine learning brought intelligence to specific tasks such as credit scoring, fraud detection, or customer service. These approaches delivered results, but most implementations were narrow, fragmented, and difficult to scale across complex enterprise processes.
Typically, automation in BFSI meant stitching together scripts, rules, and models to keep pace with scale. However, customer expectations, regulatory complexity, and market volatility are now moving faster than static systems could adapt.
The rise of Agentic AI is possible today because several forces have converged:
- Foundation models: Advances in large language and multimodal models give agents reasoning ability beyond traditional Machine learning classifiers.
- Agent orchestration frameworks: New architectures allow multiple agents to collaborate across workflows, not just execute isolated tasks.
- Open data ecosystems: APIs in banking, insurance, and fintech mean agents can securely fetch, validate, and act on external resources in real time.
- Regulatory shift: There is now an increased demand for continuous compliance, explainability, and resilience where autonomous, monitored agents could potentially outperform static checks.
- Customer expectations: In a digital-first world, decisions measured in days or hours are no longer acceptable. Customers expect near-instant approvals, settlements, and resolutions.
Together, these shifts create the conditions where agents are not just useful, but inevitable.
In Insurance, Underwriting is a good example. It is not a single decision but a chain of steps that include collecting customer data, assessing risk, applying reinsurer guidelines, running compliance checks such as anti-money-laundering (AML), and finally computing the premium. Traditional automation can handle parts of this, but the hand-offs between steps are clunky. Bottlenecks creep in, and customers wait longer than they should.
Agentic AI changes this dynamic. Unlike RPA bots that follow fixed scripts, AI agents can reason through multiple inputs, make choices, and act across systems without constant human intervention. An agent can pull data from internal records, query external APIs, validate results, and move to the next stage automatically. It acts as an orchestrator and decision-supporter, not just a task runner.
For BFSI leaders, the value here lies in augmentation, not replacement. The role of the underwriter, claims officer, or auditor does not disappear. Instead, their productivity multiplies. An example is Audit teams. They can expand their audit coverage from a small percentage of transactions to continuous monitoring. A claims
team can process and validate cases in parallel instead of one by one. The specialist remains in control, but the repetitive effort is lifted away.
An AI agent that accelerates underwriting or claims must leave a clear audit trail, show why it made a recommendation, and remain under human oversight. Any Agentic AI initiative, particularly in regulated industries like BFSI, should follow SAFE AI guidelines. The AI that is used must be secure, accountable, fair and explainable. Observability (ability to monitor what agents are doing in real time) and Explainability (ability to trace why an action was taken) reassures both regulators and internal teams.
Agents can adapt as new data, products, or risk signals emerge, but they must always operate within regulatory boundaries. Guardrails ensure that learning improves performance without introducing bias, breaking compliance, or creating new risks. In BFSI, adaptability and governance must move together.
Another key factor in success is expertise. Agentic AI is not plug-and-play. It requires organizations and leaders with expertise and a strong AI foundation working hand-in-hand with internal technical and business teams. These experts ensure the agents are aligned with enterprise risk policies, compliance requirements, and real-world workflows. Transformation succeeds not by technology alone but by co-design with domain specialists.
The near-term opportunities for Agentic AI adoption are clear:
- Fraud detection – agents analyze transactions in real time and raise alerts before loss occurs.
- Underwriting – agents coordinate risk checks, compliance requirements, and pricing steps to speed up decisions.
- Claims processing – agents track, verify, and route cases with minimal delays.
- Internal audit – agents support audits with larger samples or even does continuous reviews.
Early automation focused on point tasks: reconciling ledgers, classifying claims, or screening transactions. Agentic AI expands the scope. It is not confined to one department. It can operate across the front office (customer service, onboarding), middle office (risk, compliance), and back office (finance, audit, reporting).
This means the “rise” is not about faster underwriting or claims alone. It is about building an enterprise nervous system, a network of agents that reason, adapt, and coordinate across silos. When one agent detects unusual claims activity, another can trigger a risk review, while a third adjusts exposure monitoring — all in near real time.
The bigger picture is clear: traditional automation was about efficiency; agentic AI is about intelligence.
In the coming years, BFSI institutions will move from process-driven organizations (where humans and scripts follow rules) to intelligence-driven organizations where agents continuously learn, adapt, and coordinate, with humans setting the goals and guardrails.
This is the real “rise”: the shift from static, siloed systems to dynamic ecosystems of self-directed, yet governed, intelligence. Institutions that master this balance (speed, safety, and scale) will not just keep up with the future of financial services; they will define it.