LLM-Based Claims Adjudication: Opportunities and Risks for Indian Insurers

Claims adjudication in Indian commercial insurance is a process defined by paperwork, delays, and friction. A typical commercial property claim involves the policyholder submitting a first notice of loss, the insurer appointing a surveyor under Section 64UM of the Insurance Act 1938, the surveyor conducting a physical inspection and preparing a detailed report, the claims team reviewing the surveyor's report against the policy wording, and finally, the settlement or rejection decision being communicated to the policyholder. For straightforward claims, this process takes 30 to 60 days. For complex claims involving multiple policy sections, disputed causation, or large sums, the process can stretch to 6 to 18 months.

The bottleneck is not primarily technological. It is a combination of document volume, interpretive complexity, and human resource constraints. A single commercial fire claim might generate 200 to 500 pages of documentation: the policy schedule, endorsements, claim form, FIR copies, fire brigade reports, the surveyor's report with annexures, invoices, repair estimates, tax returns, financial statements, and correspondence between all parties. The claims adjudicator must read, understand, and cross-reference these documents to determine coverage, quantify the loss, and apply policy conditions correctly.

Indian general insurers processed approximately 2.8 crore claims in FY 2024-25 across all lines (motor, health, fire, marine, miscellaneous), according to IRDAI's annual report. The claims departments of most insurers are understaffed relative to volume, with experienced adjusters carrying caseloads of 80 to 120 open claims simultaneously. This volume pressure creates two predictable outcomes: delays in settlement (the average turnaround time for non-motor commercial claims exceeds 45 days) and inconsistency in decision-making (identical claims may be settled differently depending on which adjudicator handles them).

Large language models (LLMs), the technology behind systems like GPT-4, Claude, and Gemini, offer a potential pathway to address both problems. Their ability to process, summarise, and reason about large volumes of unstructured text makes them theoretically well-suited to claims adjudication tasks. But the gap between theoretical capability and safe, reliable deployment in a regulated financial services context is wide, and Indian insurers must approach this technology with clear-eyed assessment of both its promise and its pitfalls.

The most immediate and least risky application of LLMs in claims adjudication is document intake and summarisation. When a commercial claim is submitted, the claims team receives a heterogeneous bundle of documents, often as scanned PDFs, photographs, handwritten notes, and emails. Before any coverage assessment can begin, these documents must be catalogued, classified, and their key information extracted.

LLMs, combined with optical character recognition (OCR) and document classification models, can automate this intake process. A well-designed system can ingest the entire document bundle, classify each document by type (policy schedule, surveyor report, invoice, FIR, financial statement), extract key fields (policy number, sum insured, date of loss, claimed amount, cause of loss), and generate a structured claim summary that the human adjudicator can review in minutes rather than spending hours reading the full document set.

The value proposition is substantial. If document intake and summarisation currently consumes 30-40% of an adjudicator's time on a complex commercial claim (industry estimates suggest this is a reasonable figure for Indian insurers), automating this step frees significant capacity for higher-value adjudication tasks. It also reduces the risk of human error in data entry, missed documents, and incorrect cross-referencing.

Several Indian insurers and third-party administrators have deployed early versions of this capability. New India Assurance has piloted an AI-based document processing system for motor claims. ICICI Lombard has publicly discussed its use of machine learning for claims document processing. In commercial lines, the adoption is still nascent, partly because the document formats are more varied (commercial policies are bespoke, unlike standardised motor policies) and partly because the accuracy requirements are higher, an error in summarising a INR 50 lakh motor claim is consequential, but an error in summarising a INR 50 crore industrial fire claim can have far greater financial implications.

The critical success factor is accuracy, specifically in handling Indian document formats. Commercial insurance documents in India frequently contain a mix of English and Hindi (or other regional languages), use non-standard formatting, include handwritten annotations from surveyors, and reference Indian-specific terminology (SFSP policy, Tariff Advisory Committee rates, Section 64VB compliance). LLMs trained primarily on Western insurance documents will struggle with these inputs unless fine-tuned on Indian commercial insurance data. Insurers considering deployment should invest in building a training dataset of representative Indian commercial claim documents, annotated by experienced claims professionals, before expecting production-level accuracy.

The most intellectually demanding part of claims adjudication is coverage determination: reading the policy wording, endorsements, and conditions, then applying them to the specific facts of the claim to determine whether and to what extent the loss is covered. This task requires deep familiarity with insurance contract language, an understanding of legal precedent, and the ability to reason about ambiguous fact patterns.

LLMs demonstrate a surprisingly strong ability to perform this type of structured reasoning over long documents. Given a policy wording and a claim scenario, a well-prompted LLM can identify relevant policy clauses, flag potential exclusions, highlight conditions precedent that must be satisfied, and assess whether the proximate cause of loss falls within the insured perils. In controlled experiments, frontier LLMs have matched or exceeded the accuracy of junior claims adjudicators on standardised coverage determination tasks.

For Indian commercial insurance, the applications are particularly compelling. The SFSP policy wording, with its standard perils and numerous add-on covers, lends itself to systematic analysis. An LLM can be prompted to compare the cause of loss described in the surveyor's report against the list of insured perils, check whether the relevant add-on cover was purchased (by cross-referencing the policy schedule), verify that the claimed amount falls within the applicable sub-limit, and identify any conditions (such as notification requirements or maintenance warranties) that may affect coverage.

Marine cargo claims offer another strong use case. Marine policy wordings, whether Institute Cargo Clauses (A), (B), or (C), have well-defined coverage scope and exclusion lists. An LLM can rapidly assess whether a specific marine loss (water damage, theft, breakage) is covered under the applicable clause set, check whether the policyholder complied with packing and transit conditions, and flag any general average or salvage implications.

However, the risks in this application are substantial. Coverage determination often hinges on the precise interpretation of policy language, and Indian courts have a body of case law that qualifies or modifies the plain meaning of policy terms. An LLM that interprets the phrase 'loss or damage by fire' literally might miss the Supreme Court's ruling that fire damage includes damage caused by heat and smoke even without visible flame. Without access to and training on Indian insurance case law, an LLM's coverage determination will lack the legal grounding that experienced adjudicators apply intuitively.

The recommended deployment model is augmentation, not replacement. The LLM generates a draft coverage assessment with citations to specific policy clauses, the human adjudicator reviews the draft, applies their judgment and legal knowledge, and approves or modifies the determination. This approach captures 70-80% of the efficiency gain while maintaining human oversight on the final decision.

Insurance fraud in India's commercial lines is a persistent problem that the industry has struggled to quantify with precision. The General Insurance Council has estimated that fraudulent claims account for 10-15% of total claims expenditure in non-life insurance, though independent estimates vary widely. Fraud in commercial insurance takes forms ranging from inflated claims (overstating the value of damaged stock or machinery) to entirely fabricated losses (staging fires or thefts) to organised fraud rings that exploit gaps in verification processes.

LLMs can contribute to fraud detection in two distinct ways. First, they can analyse the textual content of claim documents for internal inconsistencies, improbable narratives, and linguistic patterns associated with fabricated claims. Research in forensic linguistics has identified several markers of deceptive writing: excessive detail in peripheral descriptions, inconsistent use of tense, overly precise recall of events, and narrative structures that prioritise persuasion over factual reporting. LLMs, trained on large corpora of text, can detect these patterns with reasonable accuracy.

Second, LLMs can cross-reference information across multiple documents in a single claim file to identify discrepancies that a time-pressured human adjudicator might miss. For example, the surveyor's report states that the fire started in the storage area, but the FIR filed by the policyholder describes the fire originating in the electrical panel room. The claimed inventory value is INR 4.2 crore, but the most recent GST returns show total purchases of only INR 2.8 crore in the preceding 12 months. The repair estimate quotes a particular brand of machinery, but the original purchase invoices show a different, less expensive brand. These inconsistencies, individually subtle, collectively form a pattern that warrants further investigation.

Indian insurers have a specific challenge in fraud detection: the surveyor system. Under Section 64UM of the Insurance Act, claims above INR 20,000 require a surveyor's assessment. Surveyors are licensed by IRDAI and are expected to be independent, but in practice, the relationship between surveyors, insurers, and policyholders can create conflicts of interest. An LLM-based fraud detection system that analyses surveyor reports for inconsistencies, compares a particular surveyor's assessment patterns across multiple claims, and flags statistical outliers could serve as a valuable check on the surveyor process.

The limitations here are significant. Fraud detection is adversarial, fraudsters adapt their methods in response to detection systems. An LLM-based system that flags claims with inconsistent narratives will be effective only until fraudsters learn to construct more consistent narratives. The system must be continuously retrained and updated. Beyond this, false positives in fraud detection carry reputational and regulatory risk. Wrongly flagging a legitimate claim as fraudulent delays settlement, damages the insurer-policyholder relationship, and may attract IRDAI scrutiny if the pattern suggests systemic prejudice against certain policyholder categories.

The single most significant risk in deploying LLMs for claims adjudication is hallucination, the well-documented tendency of LLMs to generate confident, fluent, but factually incorrect outputs. In general conversation, hallucination is an inconvenience. In insurance claims adjudication, it can be financially catastrophic.

Hallucination manifests in claims contexts in several dangerous ways. An LLM asked to identify the applicable policy exclusion might cite an exclusion clause that does not exist in the actual policy wording. Asked to quantify a business interruption loss, it might generate plausible-looking financial calculations based on fabricated numbers. Prompted to reference IRDAI regulations, it might produce a citation to a circular number that sounds correct but corresponds to a different regulation entirely, or to no regulation at all.

The danger is amplified by the nature of LLM outputs: they are grammatically perfect, logically structured, and presented with unwavering confidence. A junior claims adjudicator reviewing an LLM-generated coverage assessment has no obvious reason to doubt its accuracy, because the output looks exactly like what an experienced adjudicator would write. This is fundamentally different from, say, a rules-based system that produces an error, where the output is obviously wrong (a negative settlement amount, a reference to a non-existent policy section) and easy to catch.

The Indian insurance context adds specific hallucination risks. LLMs trained on global insurance data will default to the conventions of London market wordings, US CGL forms, or European civil law frameworks when Indian-specific knowledge is absent. An LLM might state that a particular coverage extension is available under the SFSP policy when in fact it is not part of the GIC-approved standard wording. It might reference a Supreme Court judgment on insurance interpretation that sounds authoritative but does not exist.

Mitigation strategies exist but are imperfect. Retrieval-augmented generation (RAG), where the LLM is provided with the actual policy wording and relevant IRDAI circulars before generating its response, substantially reduces hallucination by grounding the output in source documents. Chain-of-thought prompting, where the LLM is required to show its reasoning step by step, makes hallucinations easier to detect because the erroneous reasoning step becomes visible. Confidence calibration techniques can flag outputs where the LLM's internal confidence is low, directing human attention to the cases most likely to contain errors.

But none of these techniques eliminates hallucination entirely. The current consensus among AI researchers and insurance industry experts is that LLMs should not make final claims decisions autonomously. They should generate draft assessments, flag issues, and accelerate human workflows, but the settlement or rejection decision must remain with a qualified human adjudicator who takes responsibility for the outcome.

LLMs trained on historical claims data will inevitably absorb and reproduce any biases present in that data. If historical claims data shows that claims from certain geographic regions, industry sectors, or policyholder sizes were more frequently disputed, reduced, or rejected, an LLM trained on this data will replicate those patterns, potentially perpetuating unfair treatment of certain policyholder groups.

In the Indian context, this bias risk has specific dimensions. Regional disparities in claims handling exist: claims from Tier 1 cities with sophisticated policyholders and active brokers may receive different treatment than identical claims from Tier 3 cities. Claims involving large corporates with dedicated risk management teams may be processed more smoothly than claims from SMEs. If an LLM internalises these patterns and applies them to future claims, it automates inequality rather than correcting it.

IRDAI's regulatory framework for AI in insurance is still evolving, but the direction is clear. The IRDAI (Protection of Policyholders' Interests) Regulations 2024 require that insurers treat all policyholders fairly and that claims decisions are made on objective, policy-based criteria. The regulator's 2023 circular on technology adoption in insurance explicitly states that algorithmic decision-making must be explainable and auditable. An insurer that deploys an LLM for claims adjudication and cannot explain why the system recommended rejection of a particular claim will face regulatory difficulty when that decision is challenged before the Insurance Ombudsman or a consumer forum.

The explainability requirement is particularly challenging for LLMs. Unlike traditional rule-based systems where the decision logic is transparent and traceable, LLM reasoning is encoded in billions of parameters and cannot be fully decomposed into human-readable rules. Techniques like attention visualisation and feature attribution provide partial explanations, but they fall short of the clear, clause-by-clause reasoning that an IRDAI examiner or consumer forum would expect.

Practical compliance for Indian insurers deploying LLMs in claims workflows requires several safeguards. First, maintain a complete audit trail: every LLM-generated assessment must be logged alongside the input documents, the model version, and the human adjudicator's final decision with reasons. Second, implement regular bias testing: run the LLM on standardised test cases with demographic variations (changing the policyholder's location, size, or industry while keeping the claim facts identical) and verify that the outputs are consistent. Third, establish a clear escalation pathway: any claim where the LLM's recommendation differs from the human adjudicator's judgment should be flagged for supervisory review. Fourth, ensure that the policyholder is never informed that an AI system made or influenced the decision; the insurer's claims team must take full ownership and accountability for every settlement and rejection.

The Data Protection Board of India, once fully operational under the DPDP Act 2023, will add another compliance layer. Policyholders may have rights regarding automated decision-making that apply to LLM-based claims adjudication, though the specific regulations are still being drafted.

For Indian insurers considering LLM deployment in claims adjudication, the practical architecture matters as much as the technology choice. A poorly designed workflow will amplify the risks described above, while a well-designed one can capture significant efficiency gains within acceptable risk boundaries.

The recommended architecture follows a four-stage pipeline. Stage 1 is document ingestion: OCR, document classification, and structured data extraction, using a combination of specialised OCR models (for handwritten and multilingual documents) and LLM-based classification. Stage 2 is claim analysis: the LLM generates a structured coverage assessment, loss quantification review, and red flag analysis, working within a RAG framework that provides the actual policy wording, relevant IRDAI circulars, and Indian case law as context. Stage 3 is human review: the adjudicator receives the LLM's analysis as a structured draft, reviews and modifies it, and records their rationale for any changes. Stage 4 is decision and communication: the final settlement or rejection decision, authored by the human adjudicator, is issued to the policyholder.

Model selection deserves careful consideration. The frontier LLMs (GPT-4, Claude, Gemini) offer the strongest general reasoning capabilities but raise data residency concerns for Indian insurers. IRDAI's outsourcing guidelines require that policyholder data be stored and processed within India, or in jurisdictions that provide equivalent data protection. Cloud-hosted LLM APIs that route data through US or European servers may not satisfy this requirement. Indian insurers may need to deploy self-hosted or India-region cloud instances, which limits model options and increases infrastructure costs.

Fine-tuning is essential for Indian commercial insurance applications. A base LLM, however capable, will not understand the specific structure of Indian SFSP policy wordings, the distinction between Section 64UM surveyor reports and Section 64VB premium payment requirements, or the precedent set by Indian courts on proximate cause in fire and marine claims. Building a fine-tuning dataset requires extracting structured examples from historical claims (with appropriate anonymisation), annotating them with correct coverage determinations and reasoning, and iteratively training the model until it achieves acceptable accuracy on a held-out test set.

Cost-benefit analysis should be grounded in realistic efficiency assumptions. Industry experience from early deployments globally suggests that LLM-assisted claims adjudication reduces average processing time by 30-50% for straightforward claims (those with clear coverage, undisputed facts, and standard documentation) but delivers much smaller gains for complex or disputed claims where human judgment and negotiation skill are the binding constraints. For an Indian insurer processing 50,000 commercial claims annually with an average adjudicator cost of INR 3,000 per claim, a 35% efficiency gain on 70% of claims (the proportion that are relatively straightforward) translates to annual savings of approximately INR 3.7 crore, a meaningful but not transformative figure that must be weighed against the model's development, deployment, and ongoing monitoring costs.

The current state of LLM technology, powerful but imperfect, calls for a measured adoption strategy rather than either wholesale deployment or complete avoidance. Indian insurers that take a structured approach now will build capabilities and institutional knowledge that provide a competitive advantage as the technology matures.

Phase 1 (0-6 months) should focus on document automation, the lowest-risk, highest-certainty application. Deploy an LLM-based system for claim document intake, classification, and summarisation. Start with a single line of business (marine cargo is a good candidate because of its relatively standardised documentation) and measure accuracy against manual processing. Set a target accuracy threshold of 95% for document classification and 90% for key field extraction before expanding to other lines.

Phase 2 (6-18 months) should introduce coverage assessment augmentation. Deploy an LLM in a co-pilot mode where it generates draft coverage assessments for human review. Implement RAG with a curated knowledge base of Indian policy wordings, IRDAI circulars, and relevant High Court and Supreme Court rulings on insurance interpretation. Measure the acceptance rate (how often the human adjudicator accepts the LLM's assessment without modification) and the error rate (how often the LLM's assessment contains material errors that the human adjudicator corrects). Target an acceptance rate above 70% and a material error rate below 5% before expanding scope.

Phase 3 (18-36 months) should explore fraud detection and loss quantification support, the applications that require the highest accuracy and carry the greatest risk. These applications should be deployed only after the insurer has built confidence in the technology through Phases 1 and 2, established rigorous monitoring and audit processes, and developed internal expertise in AI governance.

Throughout all phases, Indian insurers must engage proactively with IRDAI. The regulator has signalled openness to AI adoption but lacks detailed guidelines on LLM use in claims. Insurers that participate in IRDAI's sandbox programme, share learnings with the regulator, and contribute to the development of industry standards will shape the regulatory framework in a way that supports responsible adoption. Waiting for perfect regulatory clarity before beginning experimentation is a strategy that guarantees falling behind.

The organisational dimension matters as much as the technological one. Claims teams must be involved in LLM deployment from the outset, not as passive recipients of a technology imposed by the IT department, but as active participants in designing prompts, validating outputs, and defining the boundaries of human-AI collaboration. The adjusters who process claims daily understand the edge cases, ambiguities, and judgment calls that no AI researcher can anticipate. Their expertise, combined with the LLM's processing speed and consistency, is where the real value lies.