AI can decode your insurance policy, but can it predict your claim? Know the limits

Decoding a health or life insurance policy can be difficult when key terms and conditions are spread across dozens of pages and filled with jargon and technical language. These days, AI can certainly help policyholders break down the fine print, but a simplified summary may not tell the whole story.

So, how can you use AI to understand your health or life insurance policy and know what to ask?

How can you use AI to decode a health or life insurance policy?

When an insurance policy runs into dozens of pages, AI can make the first reading less intimidating. This can be particularly useful when important conditions are spread across the policy schedule, policy wording, endorsements, and Customer Information Sheet.

Also read: Only ₹2 lakh was paid by Star Health against a claim of ₹4.28 lakh, citing a sub-limit clause: Here’s why the policyholder won ₹2.28 lakh more

For example, an insurance buyer can use it to instantly extract hidden traps like disease-specific sub-limits (e.g., a ₹50,000 cap on cataract surgery) or proportionate deduction clauses buried in a 40-page prospectus, explains Rakesh Goyal, Director of Probus.

A policyholder can ask it to explain, clause by clause, what is covered and what is not, then flag the sum insured or assured, waiting periods, deductibles, co-payments, room rent or treatment sub-limits, renewal terms and claim requirements.

“For a life policy, it can separately map the death benefit, maturity or surrender provisions and riders. It should also be asked to point to the relevant page or clause, so the summary can be checked against the original policy before it is relied upon,” says Abhishek Bansal, CEO, Insurance Business at InsuranceDekho.

What prompts should policyholders use when uploading an insurance policy to AI?

The quality of the AI's response will depend largely on the questions put to the tool. Instead of asking for a generic summary, policyholders should give specific instructions and ask the tool to cite the relevant page or clause.

According to Bansal, a policyholder could write, “Explain this policy in plain language and list the coverage, exclusions, waiting periods, deductibles, co-payments, sub-limits, eligibility rules, and claim deadlines, citing the relevant page or clause for each.”

A follow-up could ask, “Which conditions may affect a claim, what documents are required, and what should I clarify with the insurer?”

For a life policy, the prompt should also cover riders, premium obligations, lapse and revival, surrender value, nomination and claim procedure. It is useful to ask AI to separate the policy’s actual wording from its interpretation and flag anything unclear or inconsistent, he adds.

Goyal suggests using more specific, situation-based questions, such as: “If I am admitted for a heart condition, what specific consumable expenses are excluded?” or “Does this policy have a room-rent cap linked to a percentage of the sum insured, and how would that affect my ICU charges?”

Such prompts can help turn a lengthy policy document into a practical checklist of restrictions and conditions rather than just a generic summary.

What are the limitations of using AI?

AI can make the first reading of a policy easier, but it cannot determine whether a future claim will be accepted.

“It may not understand how comorbidities associated with a pre-existing disease could affect claim assessment, and the relevant PED information may not even appear in the policy copy, as it could be recorded in the proposal form or the insurer’s internal records,” explains Bansal.

Also read: Missed declaring parents’ pre-existing conditions? Know if you should disclose them now or switch policies

This means an AI-generated summary may not capture all the information an insurer could consider when assessing a claim.

For example, AI can instantly point out that a policy has a three-year waiting period for diabetes. But it cannot look at your elderly father's medical history and accurately predict what happens if he faces an eye or kidney complication next year.

Whether that claim gets approved depends entirely on the exact policy terms, past medical records, and how doctors link the conditions together.

Goyal also cautions that AI operates on rigid logic, but Indian insurance thrives on empathy and ground-level execution. A chatbot can hallucinate legal cross-references, creating a dangerous, false sense of security.

“Crucially, insurance in India is not a direct transaction; it is a relationship. When an emergency strikes at 2 AM, an algorithm cannot negotiate with a hospital’s TPA desk, manage a cashless rejection dispute, or comfort a panicked family,” he adds.

A machine can translate the fine print, but it lacks the real-world human context to tell you how that fine print actually applies during a medical crisis.

Therefore, policyholders should check the schedule, endorsements and insurer records, and seek important clarifications in writing. In addition, personal and medical details, including PED information, should be masked before documents are uploaded to AI.