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Industry · Life Insurance

Underwriting and claims, read and reasoned

Life insurance decisions hinge on documents a human has to read carefully — medical evidence, financial proofs, policy terms. Agentic decisioning is designed to do that reading at scale: parse the evidence, evaluate it against the underwriting manual or policy, and return a cited recommendation, with the high-judgement cases escalated to an underwriter or claims officer.

In-VPC
Deployed inside your cloud account, data stays in India
Designed for IRDAI
Audit-trail evidence and outsourcing artefacts
Reference
Architecture in repo · pre-revenue, pilots open

Where it applies

New-business underwriting

Parses proposal forms, medical reports, and financial documents against the underwriting manual, returning a cited accept / rate / refer recommendation.

Medical evidence review

Structures test results and history — including scanned and multilingual records — and flags the contradictions and gaps an underwriter looks for.

Claims decisioning

Assesses claims against policy terms, exclusions, and required evidence, citing the clause behind each determination.

Early-claim scrutiny

Surfaces the patterns that warrant investigation on early or high-value claims, routing them to a specialist with the reasoning attached.

Life insurance FAQ

Where does agentic AI fit in life insurance?

Two high-judgement, document-heavy places: underwriting, where the agent parses medical evidence and financial documents against underwriting guidelines; and claims, where it assesses a claim against the policy terms and required evidence. Both produce a cited decision with uncertain cases escalated to an underwriter or claims officer.

How does it handle medical evidence?

It reads and structures medical reports, test results, and history — including multilingual and scanned documents — and evaluates them against the underwriting manual, citing the guideline and the evidence behind each finding. It flags missing or contradictory evidence rather than guessing.

Is this defensible under IRDAI?

The audit chain is designed for IRDAI’s oversight expectations — board-approved outsourcing policy mapping, audit-trail evidence, and an exit-management plan that mirrors the RBI outsourcing structure. Final defensibility is the insurer’s determination; no IRDAI-regulated deployment of Vihaya has occurred.

Does the agent decide claims on its own?

No. Confidence-floor and policy-spec rules force uncertain, high-value, and early-claim cases to a human, who reviews the agent’s recommendation, rationale, and citations. The agent accelerates the clean cases and prepares the rest.

Next step

Want to see this in your environment?

30-minute discovery call. We follow up with a draft SOW shortly after.

Talk to us about a pilot