Artificial intelligence tools frequently fail to identify critical gaps in superyacht insurance policies, creating significant financial and liability risks for owners and underwriters. A recent evaluation of competing policy proposals revealed that automated comparisons miss layered exclusions, jurisdictional nuances, and structural differences that define actual coverage.
the limits of automated policy comparison
When asked to compare two superyacht insurance proposals, ChatGPT produces structured and fast results that appear logical on the surface. However, the system reads policy wordings as isolated statements rather than interconnected legal frameworks. Insurers apply their own hierarchy within a document, often shaped by specific jurisdictions.
"The difference is rarely found in what is covered, but in how it is worded," said specialists at Paul L'Ortye Superyacht Insurance. For example, one policy might state "worldwide navigation," while another specifies "worldwide excluding war zones." An automated tool might conclude the first offers broader coverage, missing a separate war exclusion clause elsewhere in the document that enforces the same restriction.
structural blind spots and missing details
Automated tools struggle to distinguish between fundamentally different coverage models. A system might see the term "Hull & Machinery" in two documents and assume they are comparable. In practice, one policy may operate on a Named Perils basis, covering only explicitly listed risks, while the other uses an All Risks foundation. The financial difference for a machinery breakdown or latent defect can reach hundreds of thousands of euros.
AI also tends to overlook deductibles and sub-limits. Maximum payouts for tenders, crew medical limits per person, and differing deductibles for machinery damage versus navigational incidents are often scattered throughout a document. These tools cannot flag missing elements either. If a policy lacks a cyber clause, the system will not identify the gap because there is no text to read.
legal context and data security risks
The governing law of a marine insurance policy dictates how wordings are interpreted during a claim. The same phrasing can yield vastly different outcomes depending on the legal system. Under some jurisdictions, sailing outside marked channels or ignoring a weather warning may qualify as gross negligence, immediately affecting claim payments. Other systems apply a proportional approach or require a higher degree of misconduct before reducing coverage.
Beyond coverage gaps, using public AI models introduces severe confidentiality risks. Owners and brokers who upload commercially sensitive documents expose vessel values, cruising areas, crew details, and claims history to external servers. For high-profile clients, this represents a critical privacy breach. Furthermore, AI tends to favor lower-priced options, assuming they offer comparable coverage. In marine insurance, a lower premium usually reflects reduced coverage, higher deductibles, or an insurer with a weaker claims reputation.
why this matters for insurance professionals
Underwriters and brokers must recognize that AI for Insurance cannot replace expert interpretation of complex marine policies. Relying on automated comparisons leaves critical coverage gaps, jurisdictional vulnerabilities, and data privacy risks unaddressed. Professionals must manually verify layered policy structures, sub-limits, and applicable law to ensure clients receive the exact coverage they expect.
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