SBI Life Adopts Datamatics' Agentic AI Underwriting

Datamatics announced on May 27, 2026, via PR Newswire that SBI Life Insurance has appointed Datamatics to deploy its TruAI Underwriting platform, described as an Agentic AI-powered solution for complex life-insurance underwriting. Per the PR, the solution ingests medical reports, declarations, and laboratory results, extracts key medical parameters and potential risk indicators, generates consolidated digital case summaries, and offers intelligent decision support. The announcement states that the system has self-learning capabilities and that final decision authority remains with human underwriters. The PR includes a statement attributed to Rahul Kanodia, Vice Chairman and CEO, Datamatics: "At Datamatics, we are making strategic investments in AI, and Agentic AI to solve complex enterprise problems."

Per the Datamatics press material shared via PR Newswire, TruAI Underwriting is described as performing the following functions:

• •ingesting multi-format medical documentation and extracting structured clinical parameters;
• •highlighting potential risk indicators and consolidating them into a digital case summary;
• •applying underwriting rules and historical outcomes to surface decision support signals;
• •incorporating self-learning components intended to improve risk evaluation over time.

The press release frames governance into the workflow by keeping human underwriters as the final decision authority.

Editorial analysis: Deployments of agentic AI in insurance underwriting are increasingly focused on automating repetitive information extraction and surfacing decision evidence, while maintaining human oversight to address regulatory and accountability requirements. Companies implementing similar systems often prioritize integration with legacy policy and claims systems, traceability of model outputs for audits, and staged rollouts that limit automated decisioning to recommendation support. Observed patterns also show vendor messaging highlighting throughput gains and operational-cost reductions as commercial justifications for pilots and rollouts.

Editorial analysis: Observers and practitioners should track measurable operational KPIs that vendors and insurers disclose after deployment, including average case processing time, percent of cases routed for manual review, error rates on clinical parameter extraction, and audit logs for model decisions. Regulatory engagement is another signal to watch: filings or public guidance from insurance regulators on AI-driven underwriting will affect how broadly these systems are allowed to influence risk assessment. Finally, customer- and partner-reported metrics from comparable deployments can help practitioners assess expected ROI and technical integration effort.