LLMs in Commercial Insurance Policy Drafting in India

Commercial insurance policies in India are legal contracts that directly determine what is and is not indemnified when a loss occurs. For lines such as professional indemnity, cyber insurance, directors and officers liability, and specialty property, the wording can run to 40 or 80 pages, with endorsements, schedules, and manuscript clauses negotiated individually for large accounts. The drafting process is slow, error-prone, and dependent on a small pool of experienced policy drafters. Senior underwriters at Indian insurers have estimated that bespoke policy preparation for a mid-to-large commercial account can consume between 8 and 20 hours of skilled legal and underwriting time, representing a significant cost before a single rupee of premium is collected.

The IRDAI-approved policy wordings filed by insurers serve as base forms. Insurers must file all policy wordings with IRDAI under IRDAI (Insurance Products) Regulations, 2024, which replaced the earlier product filing framework and requires that any deviation from filed forms either follow a use-and-file route or obtain prior approval. In practice, commercial policies for large industrial accounts are drafted as bespoke contracts drawing from the filed base form, with manuscript endorsements negotiated between the insured's legal team, the insurer's underwriter, and often the broker's technical team. The volume of this negotiation has grown as Indian corporates have become more sophisticated buyers of insurance, especially post the supply-chain disruptions of 2020 and 2021 that exposed policy language gaps.

Large language models entered this process initially as informal tools. Underwriters at several Indian insurers began using ChatGPT and Claude in 2023 and 2024 to summarise policy wordings, compare clauses across versions, and draft initial endorsement language for review. By 2025, this informal use had evolved into structured deployment: workflow-integrated LLM tools that receive inputs from the underwriting team, access a library of approved clause language, and produce draft policy sections for legal review. Understanding where these tools work well and where they fail requires unpacking how LLMs interact with the specific constraints of IRDAI-regulated policy language.

The core LLM task in policy drafting is clause generation: producing policy language that is legally coherent, consistent with the base form, aligned with the risk being covered, and compliant with IRDAI-filed wordings. Well-deployed LLM tools in this space do not generate clauses from first principles. They operate over a curated library of approved clause language, retrieving relevant precedents and adapting them to the specific risk.

A practical example illustrates this. An underwriter quoting a cyber insurance policy for a fintech company processing UPI transactions needs a data restoration clause tailored to cloud-native infrastructure rather than the standard clause written for on-premises tape backup. An LLM tool given the base cyber policy wording, the fintech's technology questionnaire, and a library of approved data restoration clause variants can produce a draft that substitutes cloud storage references for tape references, adjusts the restoration time objective language, and notes that the standard exclusion for data stored on third-party platforms requires review given the insured's AWS and Azure footprint. The output is a first draft, not a finished product, but it replaces what would otherwise have been 2 to 3 hours of a senior underwriter's drafting time.

Endorsement drafting for bespoke commercial accounts

Endorsement drafting is where LLM tools have seen the most rapid adoption among Indian brokers and insurers. A commercial property policy for a large manufacturing group may require 15 to 25 endorsements: waiver of subrogation for specific contractors, agreed bank clause for lenders, loss payee arrangements for equipment on hire purchase, terrorism cover extensions, business interruption endorsements tied to specific revenue streams. Each of these must be drafted, reviewed, and attached to the base policy.

LLM tools trained on endorsement libraries can produce first-draft endorsement language within minutes. Brokers including Marsh India, Aon India, and Willis Towers Watson India have reported using LLM-assisted drafting to reduce endorsement preparation time by 40 to 60% on large commercial accounts. The reduction is not uniform: standard endorsements (waiver of subrogation, loss payee) are largely automated, while novel endorsements addressing new risk types (AI-generated content liability, supply chain disruption from specific geopolitical events) require substantial human authorship with LLM providing structural scaffolding rather than substantive language.

The technical approach matters. LLMs used without retrieval augmentation from approved clause libraries have a high risk of generating plausible-sounding but unapproved language. The professional indemnity clause 'covering all claims arising from negligent acts, errors, or omissions in the provision of professional services' sounds reasonable but differs materially from an IRDAI-filed wording that may restrict coverage to specific professional categories or require the claim to be first made and notified within the policy period. An LLM generating this clause from general training data rather than from the insurer's filed wording may produce language that is broader than the filed form, creating an unanticipated coverage grant.

One of the most commercially valuable LLM applications in Indian commercial insurance is the comparison of draft or negotiated policy wordings against IRDAI-filed base forms. When an insured's legal counsel returns a marked-up policy with proposed changes, the underwriter must assess each change against the approved wording. This comparison is painstaking manual work when done against a 60-page document with multiple appendices.

LLM-powered comparison tools receive the filed base form and the negotiated draft, and produce a structured diff identifying where the two texts diverge. For each divergence, the tool classifies the change as cosmetic (punctuation, formatting), substantive but within approved endorsement parameters, or potentially non-compliant with the filed wording. The last category is escalated for legal and compliance review. This classification is not perfect: LLMs sometimes misidentify paraphrased language as substantive divergence, or fail to catch subtle shifts in defined terms that have material coverage implications. But even an imperfect automated comparison that catches 70 to 80% of divergences before human review adds substantial value by focusing the underwriter's attention on the most consequential changes.

Several Indian technology firms have built products around this use case. Artivatic Data Labs, PolicyBazaar's commercial arm, and a number of Lloyd's of London market service providers operating in India have wording comparison tools in production. The accuracy benchmarks vary significantly by policy type: fire and property wordings with stable, well-structured clause hierarchies show higher LLM comparison accuracy than liability wordings where coverage grants and exclusions are embedded in dense, nested language.

The IRDAI filing system creates a specific challenge for LLM-based review tools. Filed wordings are not always available in clean, machine-readable formats. Many historical filings are PDF scans. The quality of OCR extraction from these documents is variable, introducing noise that can degrade the accuracy of LLM comparison. Insurers building internal wording review tools have invested in a parallel effort to create clean digital versions of their filed wording libraries, which is a prerequisite for high-quality LLM-assisted review.

Bespoke clause negotiation for large commercial accounts in India involves at least three parties: the insured's risk management or legal team, the insurer's underwriting and legal teams, and the broker. Each party has a different objective. The insured wants broad coverage with minimal exclusions. The insurer wants to stay within filed wording parameters while pricing the risk accurately. The broker mediates, often proposing compromise language that satisfies both parties while being technically accurate.

LLM tools are being deployed by all three parties in this negotiation, with different configurations. Broker technical teams use LLMs to quickly research how similar clauses have been worded in Lloyd's market policies, London market wordings, or Indian industry association standard forms, giving them a broader reference base when proposing compromise language. Insurer legal teams use LLMs to identify the coverage implications of proposed changes by the insured's counsel, mapping each proposed change to potential claim scenarios. Insured risk managers use LLMs to summarise complex policy language and flag where the proposed wording differs from what they understood the coverage to be.

Where language ambiguity is flagged

A key LLM capability in the negotiation context is ambiguity detection. LLMs can identify policy language where the coverage scope is unclear because a term is undefined, a condition is potentially contradictory, or the relationship between a coverage clause and an exclusion clause is uncertain. For example, a professional indemnity policy might include a coverage clause for 'IT consulting services' without defining whether that term includes software development, data analytics, or AI model deployment. An LLM tool reviewing this wording would flag 'IT consulting services' as potentially ambiguous given the insured's described activities, and note that the absence of a definition creates uncertainty about coverage for a claim arising from a data analytics engagement.

This ambiguity detection is valuable precisely because it surfaces issues before a claim occurs rather than at the claims stage when the dispute is adversarial. Several Indian insurers have begun using LLM ambiguity screening as a standard step before policy issuance on large commercial accounts. The practical implementation involves the LLM receiving the near-final policy wording, generating a list of potentially ambiguous terms and clauses, and having a qualified underwriter or legal reviewer adjudicate each flag. The process adds one to two days to policy finalisation but has been credited by underwriters at ICICI Lombard's commercial lines division with reducing policy wording disputes at claims stage.

The most serious risk in LLM-assisted policy drafting is hallucination: the model generating plausible-sounding but factually incorrect policy language that is incorporated into a live contract. In the context of insurance policy drafting, hallucinations take three distinct forms, each with different consequences.

Fabricated exclusions are perhaps the most immediately harmful. An LLM generating a cyber policy exclusion schedule might produce language excluding 'claims arising from cryptocurrency mining operations on the insured's systems' without this exclusion appearing in the filed base form and without explicit instruction to include it. If this exclusion reaches the final policy without detection and the insured subsequently suffers a ransomware attack that attackers launched through cryptocurrency mining malware, the insurer may attempt to rely on the exclusion while the insured contests that it was not agreed. The resulting dispute can escalate to IRDAI's Insurance Ombudsman, to consumer forums under the Consumer Protection Act 2019, or to arbitration under the Arbitration and Conciliation Act 1996.

Ghost conditions are LLM-generated policy conditions that look like standard policy management obligations but either contradict other conditions in the policy or impose requirements that were never discussed. An LLM might generate a condition requiring the insured to submit quarterly risk management reports when the base form contains no such condition. A risk manager who does not read the final policy carefully might discover this condition only when the insurer invokes it to resist a claim on grounds that quarterly reporting was not maintained.

Coverage gaps from LLM drafting arise when the model omits language that was present in the source materials. An endorsement drafted by an LLM that is supposed to extend coverage to a subsidiary operation may omit the subsidiary's specific registration details or the territorial extension clause, leaving the subsidiary uninsured despite the insured's expectation of coverage. These omissions are harder to detect than additions because a reviewer looking for what has been added will not naturally be looking for what has been left out.

Quantifying the frequency of these hallucination events in Indian insurance policy drafting is difficult because most incidents are resolved quietly in the pre-policy-issuance review process. Anecdotal reports from underwriting teams at mid-tier Indian insurers suggest that LLM policy drafting tools without retrieval augmentation and human review produce material errors in 15 to 25% of outputs, a rate that is unacceptable for a legal contract but which drops to 2 to 5% with proper retrieval augmentation and declines further with structured human review workflows.

Given the risks outlined above, the responsible deployment of LLMs in policy drafting requires structured human oversight built into the workflow, not optional review appended at the end. The distinction matters: if the LLM produces a draft and the human reviewer has the option to approve without reading carefully, hallucinations will reach final policies. If the workflow requires the human reviewer to actively adjudicate specific flags and sign off on defined check categories, the probability of a hallucination reaching a live contract is substantially reduced.

Leading Indian insurers deploying LLM-assisted drafting tools have developed tiered authority matrices that define who must review what. For standard endorsements on standard commercial accounts, a qualified underwriter's review and approval is sufficient. For bespoke clause language on accounts above a defined premium threshold (commonly INR 1 crore annual premium), a joint sign-off from underwriting and the legal or compliance function is required. For manuscript clauses negotiated with an insured's external counsel, the insurer's in-house legal counsel must review the final wording before it is attached to the policy.

The role of legal review in LLM-assisted drafting

Legal review in the LLM-assisted drafting context is not reading the entire policy from scratch. It is focused review of the sections that the LLM has drafted or modified, with specific attention to three categories: changes from the filed base form, newly generated language not drawn from the approved clause library, and any language flagged by the LLM itself as ambiguous or potentially non-compliant. Well-designed LLM drafting tools produce a review package alongside the draft policy that directs the legal reviewer's attention to these categories, reducing review time while maintaining coverage of the highest-risk elements.

Several Indian insurers have implemented a 'four-eyes' principle for LLM-assisted policy drafting: the underwriter who used the LLM tool to produce the draft cannot also be the reviewer. A separate underwriter or legal officer conducts the review. This principle, borrowed from trading and treasury operations where it is a standard control, provides an independent check that the LLM's outputs have been appropriately evaluated before the policy is issued.

The training of reviewers is also a material factor. An underwriter who understands how LLMs produce language and what categories of errors they are prone to will conduct a more effective review than an underwriter who treats the LLM output as if it were produced by a junior human drafter. The specific failure modes of LLMs in policy drafting (fabricated exclusions, dropped conditions, imprecise defined terms) are teachable, and several Indian insurers have added LLM literacy training to their underwriter development programmes in 2025 and 2026.

IRDAI has not, as of April 2026, issued specific guidance on LLM-assisted or AI-generated policy wording. The question of whether a policy wording produced with LLM assistance satisfies the requirements of IRDAI (Insurance Products) Regulations, 2024 has not been formally adjudicated. The prevailing position among Indian insurance legal practitioners is that IRDAI's requirements relate to the content of the wording, not the process by which it was produced, meaning that an LLM-drafted wording that is otherwise compliant with filing requirements is not rendered non-compliant merely by the use of AI in its production. This position has not been tested.

What is clear is that responsibility for policy wording accuracy and IRDAI compliance remains with the insurer. IRDAI Circular IRDAI/NL/CIR/CMT/172/11/2014 on policy servicing standards, and subsequent circulars on policyholder grievance redressal, establish that the insurer cannot shift accountability for policy language errors to a technology vendor. If an LLM-generated clause creates an unintended coverage grant or exclusion, the insurer bears the consequences, including potential regulatory action, policyholder compensation, and reputational harm.

The IRDAI Regulatory Sandbox, updated in 2024, provides a route for insurers to test AI-assisted policy drafting workflows and report outcomes to IRDAI. At least two Indian insurers were reported to be exploring sandbox applications for AI-assisted commercial lines policy production in early 2026. The sandbox route allows for structured experimentation with regulatory visibility, which is likely to accelerate IRDAI's development of specific guidance on AI-generated policy language.

The international context is instructive. The UK's Financial Conduct Authority published a discussion paper on AI in insurance in 2025 that addressed policy wording generation, focusing on disclosure requirements (should policyholders be told when AI assisted in drafting their policy?) and accountability (must the signing underwriter have personally reviewed AI-generated language?). IRDAI is expected to draw on this work as it develops its own guidance, though the Indian regulatory context, including the IRDAI (Insurance Products) Regulations, 2024 filing regime and the policyholder protection framework under the Insurance Act 1938, will shape how international approaches are adapted. Industry bodies including the Insurance Brokers Association of India (IBAI) and the General Insurers' Public Forum (GIPF) are actively engaging with IRDAI on this question, and formal guidance is anticipated before the end of the 2026-27 financial year.