For over a decade, banking, financial services and insurance (BFSI) organisations have steadily invested in workflow automation, rule engines, and digital decisioning systems with the expectation that these tools would streamline operations and reduce manual effort. While these systems have delivered incremental improvements, they were fundamentally designed to follow instructions not to interpret context or adapt to real-world complexity.
Agentic Artificial Intelligence (AI) marks the next major shift. It moves the industry from systems that simply do what they are told, to systems that can understand what needs to be achieved and determine the best way to get there. Instead of speeding up existing workflows, Agentic AI has the capacity to reshape them.
Traditional automation works through predefined checklists. If something unexpected appears missing document, a discrepancy in customer information, or an ambiguous medical note the system stops and pushes the task back to a human. In contrast, Agentic AI is designed to handle ambiguity. It can gather missing information, interpret unstructured content, reason through variations, and progress a task without constant human intervention.
This becomes particularly relevant for BFSI processes where exceptions are the rule. Underwriting, KYC, claims assessment, loan origination, and risk reviews all involve dozens of micro-decisions that rarely unfold in a perfect sequence.
Agentic AI takes a goal-first approach, focusing on the outcome the organisation wants, accurate risk assessment, faster turnaround time, fewer inconsistencies and then dynamically charts the path. It can interpret documents, ask clarifying questions only when necessary, fetch additional information from internal or external systems, and keep the process moving.
{{/usCountry}}Agentic AI takes a goal-first approach, focusing on the outcome the organisation wants, accurate risk assessment, faster turnaround time, fewer inconsistencies and then dynamically charts the path. It can interpret documents, ask clarifying questions only when necessary, fetch additional information from internal or external systems, and keep the process moving.
{{/usCountry}}Consider underwriting. Even with digital applications, underwriters often spend time digging through documents, verifying information, and chasing missing details.
An Agentic AI underwriting system can retrieve historical records, validate disclosures, identify missing information, and interpret medical reports with high consistency. It can suggest risk classifications or exclusions with clear reasoning, and it knows when to escalate cases requiring human expertise.
Over time, the system becomes progressively better at handling exceptions and improving decision quality. What begins as an automation tool gradually becomes a decision partner.
Every action taken by an AI agent is recorded and explainable. Risk thresholds are embedded directly into the system’s reasoning, ensuring compliance is built-in rather than bolted on.
For BFSI leaders, the implications are significant. Efficiency no longer comes at the expense of rigour. Processes become faster not because steps are skipped, but because they are refined. Manual effort reduces, but oversight strengthens.
Crucially, Agentic AI doesn't replace human expertise it amplifies it. Experts are freed from repetitive work and can focus on nuanced decisions, edge cases, and portfolio-level insights.
As the sector enters a new phase, incremental improvements will not be enough. The opportunity now lies in rethinking how core processes work end-to-end. Agentic AI offers a fundamentally different approach to how decisions are made and how operations evolve.
The leaders of the next decade will be those who recognise that Agentic AI is not a technology upgrade it is the foundation for a new operating model.
This article is authored by Pritesh Tiwari, founder and chief data scientist, DSW.