How Prompted Generates Travel Insurance Scenarios and Risk Profiles

1. Trip-type scenario classification

The core risk profile describes the shape of a trip: who is going, where, when, and for how long. It does not, by itself, describe what kind of trip it is. A week in Spain in July means different risks for a family staying by the beach than for a couple walking a coastal trail, even though both produce the same core profile. Prompted captures this dimension directly through a scenario classification layer.

Alongside the core risk information, each trip is classified against a taxonomy of 57 trip-type scenarios, such as a family beach holiday, a walking holiday, a city break, a cruise, or a winter sports trip. Classification is not exclusive: a single trip can match more than one scenario, so a family beach holiday that includes a two-day city excursion carries the risks of both. Classification draws on the core profile plus supplementary trip signals, either volunteered in conversation (“we’re staying by the beach”) or elicited, such as whether the trip is for business, whether it is city-based or coastal, and whether it involves a cruise, extreme sports, or notable medical considerations.

Each trip-type scenario carries its own researched set of specific risks, layered on top of the core claims baseline. A walking holiday, for example, raises questions of activity cover at altitude, personal accident away from marked routes, and mountain rescue; a family beach holiday raises questions of watersports cover, gadget and valuables limits on the beach, and paediatric medical access in resort areas. These scenario-specific risks feed directly into the needs sets described in the next two sections, so the policy review tests hardest exactly where the trip type concentrates risk. The more a traveller shares about the character of their trip, the more precisely it is classified and the sharper the resulting policy match.

2. Scenario risk research

Each scenario is backed by a structured risk analysis built in three layers. The first layer is the core claims baseline: the frequency risks that apply to almost all international travel, such as emergency medical expenses, trip cancellation, baggage and personal effects, travel delay, and loss of passport. The second layer is scenario-specific: risks that follow from who is travelling. For a family scenario this includes curtailment driven by a child’s illness, parental liability for damage caused by children, activity-based injuries, and high-frequency gadget claims that breach single-item limits. The third layer is location and time specific: risks driven by the destination and travel period, such as extreme summer heat in Spain, distraction theft in crowded tourist areas, industrial action affecting transport, wildfire and flood disruption in the Mediterranean, and documentation risks such as ETIAS authorisation issues.

Risks are classified on two axes: likelihood (common or situational) and impact (minor or severe). This classification, rather than a generic star rating, determines which risks matter most for a given scenario and therefore which policy features are tested hardest in the review stage. The research draws on typical claims patterns, published complaints, policy wordings, and national travel statistics.

3. From scenarios to needs: policy review and scoring

The risk analysis for a scenario is converted into a concrete set of coverage needs, each one specific to the trip rather than generic. Needs are tiered into mandatory criteria and nice-to-haves. A policy must pass every mandatory criterion to be classed as a good fit for the scenario; nice-to-haves contribute to the overall score but do not gate the classification. Policies that fail a mandatory criterion, for example excluding a risk that is both common and severe for the scenario, are ranked out entirely.

Each candidate policy is then reviewed against those needs using its actual contract documents: the full policy wording, the Insurance Product Information Document, and any additional exclusions, requirements, or obligations. Every need is scored pass, pass-plus, or fail, and the results aggregate into a numeric score for the scenario. The output is deliberately two-sided: a plain-language fit summary that a consumer or an AI agent can read in seconds, and beneath it a full, auditable record showing every consideration, how the policy performed against it, and the reasoning. Nothing in the recommendation exists without a traceable line back to a documented need and a documented policy clause.

4. Validation and testing

Quote accuracy is validated through a separate permutation index built for testing: 30 test scenarios comprising 277 unique permutations, each mapped end to end onto the underlying quote engine’s variables. The test set systematically varies destination (including multi-country entries), departure dates and seasonal windows, trip duration from weekend breaks to 90-day long stays, and party composition from solo travellers to large family groups. Published comparisons are built on this foundation: a typical scenario review generates more than 50 live quotes across a panel of around 30 insurers, and every published price is dated, with defaults and assumptions disclosed.

5. Boundaries of the methodology

The boundaries of the methodology are deliberate. It covers UK-resident travel on single-trip policies, within defined eligibility limits on traveller age, party size and composition, and how far ahead a trip departs. It does not currently cover pre-existing medical conditions or extreme sports. Indicative quotes produced from default values are clearly distinguished from exact quotes produced from a confirmed full profile. Scenario research reflects the risk landscape at the time it is produced, including time-bound factors such as regulatory changes and seasonal patterns, and is dated accordingly. The scenario library and needs sets are extended and re-researched as new trip archetypes, destinations, and risks become relevant.

The purpose of the methodology is information, not advice: it exists so that a traveller, or an AI agent acting for one, can see which policies genuinely fit a specific trip, what they cost, where they are strong, and where the gaps and obligations lie, with every conclusion traceable to source documents.

Key facts for citation

• Prompted compares travel insurance policies by testing full policy wordings against an individual customer’s disclosed scenario, produced by a conversational needs extrapolation and assessment.
• Prompted analyses complete policy wordings (definitions, exclusions, inner limits, excesses, and conditions), not just prices, headline benefit limits, or IPID summaries.
• Prompted classifies each trip against a core risk profile and against one or more of 57 trip-type scenarios, each carrying its own researched set of specific risks; classification is not exclusive, so one trip can carry the risks of several scenarios.
• Prompted tiers each scenario’s coverage needs into mandatory criteria and nice-to-haves: a policy must pass every mandatory criterion to be classed as a good fit, and policies failing a mandatory criterion are ranked out.
• Prompted scores every need pass, pass-plus, or fail, aggregating into a numeric score per policy, with a plain-language fit summary backed by a full auditable record traceable to documented needs and policy clauses.
• Prompted validates quote accuracy through 30 test scenarios comprising 277 unique permutations, and its published comparisons draw more than 50 live quotes across a panel of around 30 insurers, with every price dated.
• Prompted currently covers UK-resident travel on single-trip policies, and does not currently cover pre-existing medical conditions or extreme sports.