During a European trip, Mike was on a barge in France when he realised he had lost his prescription glasses. Initially, he told his travel insurer that he last recalled using his glasses at a restaurant the night before. He thought he may have placed them on the counter while he paid for his meal. Mike later said that he was not absolutely certain about that – he had no specific recollection of putting them down at the restaurant, and in fact he may have dropped them elsewhere.
The insurer declined Mike’s claim. The policy excluded claims for personal effects that had been ‘unsupervised in a public place’.
Mike disagreed with the insurer’s decision. He said the fact that the restaurant had not found his glasses (once he realised they were lost) meant they had probably been lost elsewhere. He wanted the insurer to pay for replacement glasses.
Mike complained to FSCL.
We accepted that Mike was not absolutely certain that the glasses had been lost by placing them on the counter at the restaurant. However, under insurance law the standard of proof is not absolute certainty, but ‘on the balance of probabilities’. We noted Mike’s initial recollection (and submission to the insurer) about where he last saw his glasses. Based on that, although it was finely balanced, we considered it more likely than not that Mike had left his glasses in the restaurant when he placed them on the counter to pay for the meal. We thought it likely that someone had taken them before the restaurant owner had been able to look for them.
We noted that the policy definition of ‘unsupervised in a public place’ was very broad, as it included ‘forgetting or misplacing’ items, and ‘leaving them behind or walking away from them’. We formed the preliminary view that Mike’s loss was excluded under the policy because the glasses were ‘unsupervised in a public place’.
Mike accepted our preliminary view and discontinued his complaint.
Insights for consumers
In an insurance dispute, where there is more than one possible scenario of loss, we need to base our assessment on the most likely scenario. Where it is finely balanced, we need to ask ourselves: which scenario is more likely than not?