AI is already changing insurance, but not in the simple way the marketing language suggests. In most real workflows, artificial intelligence sits inside a larger process: extracting information from documents, scoring risk, prioritising files, flagging anomalies, suggesting next actions or helping a human reviewer compare large volumes of data. That distinction matters when a policy is issued or a claim is challenged.
For a policyholder, the practical question is not whether an insurer “uses AI.” It is where an automated or AI-assisted system entered the decision chain, what data it relied on, whether a human reviewed the output, and what evidence supports the final underwriting or claim decision.
Global FinTech Fest 2026, held in Mumbai from 8 to 11 September, included AI-driven underwriting and claims as an InsurTech theme. The more useful policyholder question, however, is what happens when those systems affect eligibility, pricing, claim triage or fraud review—and how the decision can be explained and challenged.
Where AI Can Enter the Underwriting Process
Underwriting is still governed by the insurer’s product, proposal questions and underwriting rules, but technology can influence how quickly and in what order information is reviewed. AI-assisted systems may extract data from proposal forms and medical reports, identify inconsistencies, classify documents, score patterns or route a proposal for further medical or financial underwriting.
The important distinction is between an input, a model output and the insurer’s final decision. A system may flag a proposal because of a medical term, transaction pattern or mismatch in submitted data. That flag is not the same thing as proving non-disclosure, fraud or higher risk. If the policy is later disputed, the proposal form, declarations, medical records and underwriting communications remain central evidence.
Automation can make clean proposals faster to process, but it can also make bad data travel faster. A spelling error, duplicate record, wrongly mapped diagnosis or incomplete data feed can become more consequential when multiple systems reuse it. That is why the audit trail matters as much as speed.
Where AI Can Enter a Claim
In claims, AI is more likely to assist with document extraction, classification, claim triage, anomaly detection, fraud scoring, image or damage analysis, medical-record review, duplicate detection and workflow prioritisation than to replace the entire claim process with one autonomous decision.
A low-complexity claim may move through more automated steps than a disputed or high-value file. That can reduce manual handling, but a fast workflow is not the same thing as a correct coverage decision. The policy wording, evidence and applicable claims process still control whether the claim is payable.
The main policyholder risk is opacity. A claim may be routed for investigation, marked as an anomaly or delayed for additional review without the customer knowing which piece of data triggered the extra scrutiny. If the insurer ultimately rejects or reduces the claim, the written decision should still be tested against the policy clause and the evidence—not against a vague statement that “the system flagged it.”
The same applies to fraud controls. Pattern detection can help insurers find genuinely suspicious activity, but a statistical anomaly is only a signal. It should not automatically substitute for proof of misrepresentation, fabrication or another policy breach in an individual claim.
AI, Automation and Parametric Insurance Are Not the Same Thing
These concepts are often bundled together in technology discussions, but they solve different problems. Traditional automation follows predefined rules. AI or machine-learning systems infer patterns from data. Parametric insurance pays when a contractually defined external trigger is met, such as a rainfall or weather index crossing a threshold.
A parametric payout can use digital sensors, satellite data or automated verification without the payout itself being an AI decision. The trigger and payout formula come from the contract. Calling every data-driven or automatic insurance process “AI” makes it harder to identify what actually needs to be explained or audited.
For claim disputes, the distinction is practical: ask whether the disputed outcome came from a policy rule, a predefined automated workflow, an AI-generated score or recommendation, or a human assessment. Different evidence may be needed to test each one.
IRDAI’s AI Governance Work: What Is Actually on the Table
On 17 June 2026, IRDAI constituted a seven-member Working Group on Artificial Intelligence Governance in the insurance sector. Its mandate includes assessing AI adoption and governance maturity, examining risks to insurers and policyholders, and suggesting governance and audit frameworks for responsible use.
For a closer look at the regulatory side of this shift, see our IRDAI AI governance working-group explainer, including its focus on claims, fraud prevention, explainability and audit.
The working group is chaired by Prof. Sandeep K. Shukla of IIIT Hyderabad and includes representatives with cybersecurity, technology, life, health and general-insurance experience. Its terms of reference specifically include:
- Assessing how insurers are using AI and how mature their governance processes are
- Recommending ethical, transparent and explainable AI governance, including for claims and fraud prevention
- Suggesting pre-deployment and post-deployment AI audit requirements
- Examining AI-related cyber, resilience, stress-testing, monitoring and capacity-building needs
The June order gave the group three months to submit recommendations. That deadline does not by itself mean a binding AI framework automatically came into force. Until a final regulatory framework is publicly issued, the working group’s mandate should be described as a governance process in development, not as an already-operative claims rulebook.
What Policyholders Should Ask When AI Touches a Claim
If a claim has been rejected, reduced, delayed or sent for investigation, start with the same documents you would use in any serious claim dispute: the written decision, policy wording, proposal form, claim form, medical or survey record, insurer queries, document-submission trail and any report or opinion relied on.
Then ask what evidence drove the decision. If the insurer refers to a survey report, investigator report, medical opinion or other evidence that you have not seen, our claim-file evidence guide explains how to request the material used against the claim.
Do not assume that every unusual outcome was caused by AI, and do not assume that an AI-assisted process is automatically invalid. The useful question is whether the insurer can connect the final decision to the contract, the facts and an auditable evidence trail.
What Tatkal Claims Would Check First in an AI-Affected Claim
We would first identify the actual decision under dispute and separate the technology layer from the coverage question. Was the claim rejected because of an exclusion, an alleged non-disclosure, medical necessity, a survey conclusion, a fraud concern, a document mismatch or a calculation? Then we would ask which documents and findings support that reason.
If the explanation is vague or appears to depend on an unexplained system flag, the next step is to request the underlying evidence and force the grievance back onto the policy clause and the record. The same organised file can then be used through the insurer grievance process and, where appropriate, later escalation. For the broader framework, see our complete insurance claim rejection guide.
AI can improve speed, consistency and fraud detection, but it does not remove the insurer’s responsibility for the final underwriting or claim decision. For a policyholder, explainability matters most when the technology changes what happens to a real file.
