Canara HSBC Life Insurance closed FY26 with a 19% year-on-year growth in Individual Weighted Premium Income to ₹2,593 crore, driven by a technology strategy focused on simplifying customer onboarding and underwriting. Chief Operating Officer Sachin Dutta said this growth reflects a broader shift toward using technology to build long-term trust rather than just chasing automation metrics.
The insurer reported total premium income of ₹10,046 crore, alongside a Value of New Business increase of 41% to ₹627 crore. These financial results correspond to improved customer retention and a claims settlement ratio of 99.6%, which the company attributes to consistent digital experiences.
Building consistency through digital onboarding
Historically, purchasing life insurance required lengthy face-to-face meetings and repetitive documentation. Canara HSBC Life replaced this with digital applications that tailor questions based on a customer's life stage and risk profile. This approach ensures every customer receives the exact same product explanation, regardless of the sales channel.
"What technology does is create a common and transparent journey for every customer," Dutta said. "The same product should be explained in the same way, irrespective of who is selling it." The company also uses digital video verification to capture consent during onboarding, which speeds up policy issuance and reduces compliance risks.
Embedding AI into core workflows
The company built its technology foundations around cloud, mobility, and analytics starting around 2018. Rather than bolting AI onto legacy systems, the company redesigned workflows to be AI-ready from the start. "When we redesigned our workflows, the thought process was not how to add AI later," Dutta said. "The thought process was how to make the workflows AI-enabled from the beginning."
This strategy is most visible in the company's OmniGen AI underwriting initiative. The system evaluates incoming cases against standard operating procedures and historical decision frameworks. It initially operated as a co-pilot for human underwriters, but low-risk cohorts now move through autopilot processing, freeing staff to handle complex cases. This approach to AI for Insurance demonstrates how embedding machine learning into core processes reduces friction rather than adding new layers of overhead.
Preparing for agentic orchestration
As the industry looks beyond generative AI, Dutta expects a shift toward agentic systems that coordinate across departments. Instead of isolated bots handling separate tasks, the next phase requires an orchestration layer where agents share context across underwriting, servicing, and claims.
"Agentic AI is about creating the ecosystem first and then creating agents," Dutta said. The company is currently evaluating how interconnected agents might improve operational efficiency while maintaining strict risk controls. This long-term view on AI for Executives & Strategy prioritizes sustainable value over short-term technological experimentation.
Why this matters for insurance professionals
Insurers must treat technology as a mechanism for standardizing trust, not just a tool for cost reduction. When AI is embedded natively into underwriting and onboarding workflows, it eliminates the inconsistencies that historically eroded customer confidence. Professionals should evaluate their own tech stacks to ensure new AI tools integrate directly into existing decision-making processes rather than operating as disconnected add-ons.
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