How might consumers react to recent signs of insurance being offered on a pre-approved basis? Is there a sense that insurance is being innovative, using data to identify customer needs and offer them products with the minimum of fuss? Perhaps, but there is also a sense that pre-approved insurance could reinforce the worst stereotypes associated with the selling of insurance.
Pre-approval has been a feature of insurance for many years, but until recently, it has been confined to claims, used as a processing control for health insurance claims and motor repairs. Now it is emerging in a quite different form in underwriting, with personal lines products being direct marketed on the basis that you’re already met the underwriting criteria and cover just for you is ready and waiting to be taken out.
There are two key selling points behind pre-approved insurance: firstly, the insurer is saying that they’re sure you need this type of cover, because their data analytics tells them so, and secondly, they’ve made it as easy as possible for you to take out cover, through pre-checking you against important underwriting criteria and determining your premium in advance.
Pre-approved insurance is made possible by the vast data stores and clever analytics upon which ‘big data’ has been built. The logic is that as underwriting increasingly relies on big data to assess a customer, why wait for that customer to come to the insurer? Why not go to them first with ‘oven ready’ cover?
So what’s the problem? Here are several that come to mind:
- it feels like the next step on from ‘opt out cover’, with the consumer being marketed a ‘pre-opted in cover’ just ready and waiting for the go ahead to be given. If the marketing becomes more and more personalised (which seems inevitable), might consumers feel obliged to contact insurers to say they don’t want the cover?
- the pre-approved insurance is presented as a personalised product, but of course it isn’t. It works only if the insurance on offer has been commoditised for a target market of people just like you. The insurer buys contact details from email lists according to the fit with a number of underwriting criteria. The cover is then tailored and trimmed to reflect a ‘great offer’ price, but also interwoven with terms and conditions, and underwriting assumptions, to protect the portfolio’s performance against outliers within that target market. This has all the hallmarks of policies packaged with apparently generous cover, but which the consumer then finds is somewhat hollow where they need it most. A fine print nightmare.
- does pre-approved insurance encourage the consumer to forego their own assessment of what cover they need and where they might obtain it from? Is the convenience being over-presented and the suitability under-presented? Does pre-approved insurance encourage consumers to switch off their critical faculties?
- the insurer is relying on their data being sufficiently representative of the risk that the consumer needs cover for. Is this realistic, given that any statistician will tell you that correlation isn’t the same as causation? Perhaps most critically, how will this big data approach to underwriting translate into claims decisions down theline? While I can see underwriting and marketing directors talking about ‘the fit is good enough’, I’m not so sure of a claims director adopting the same language when settling claims.
- is pre-approved insurance a step in the direction of automated underwriting and automated marketing? If so, can insurers be confident that their checks and balances, their controls and testing, are sufficiently robust to back up their regulatory obligations? Given the gap in expectations around conduct risk set out in this report last year, I’m not so sure that insurers can be that confident at the moment.
I think pre-approved insurance does have a role to play in the development of insurance in the 21st century. Its acceptance by consumers and regulators could stand on how that perennial interplay between cover/price/claims and fairness/suitability is managed.
