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Unlocking the Holy Grail of Insurance Pricing: The Surprising Truth About Employee Wellbeing Investment

By Andrew Supple, Partner

A long, long time ago in a galaxy far, far away, there was a youngish graduate nerd working at a global employee benefits consultancy.  Being at that stage in his career reasonably Microsoft literate (he isn’t now) and with a good grasp of statistics (hmmm, not too bad), he was asked by his boss to build a model of Group Income Protection (GIP)* pricing for the business alongside a member of the actuarial team primarily for the global captive market (don’t worry about what a captive is, it is just an insurance mechanism, and not really relevant to the story here).

The dream team would work as follows: the actuary would bring the pricing tables and the grad would bring the direct market experience.  It took about six months to build a working model and about another six months to test it in the market to make sure that it was producing ‘competitive’ pricing.  During that period, the bottom fell out of the captive market and the model had to be revised to focus solely on the UK market alone and work with much smaller scheme sizes.  But we got there in the end.

If you are going to build a pricing model for GIP, then you need to understand what are the main factors that impact on price AND what ones you could potentially influence to improve pricing.  Age is an important factor in pricing, but it isn’t really something we can impact on (yet?) Benefit Level, Occupation, Location etc. are all risk factors and can potentially be influenced to an extent but aren’t that important or alterable that it was worth spending time on.  The main pricing factor that ‘we’ can influence is claims.  Zero claims ever are good for pricing, but is this possible, especially with bigger schemes where some level of claims is more likely?

At the time, aggressive rehabilitation techniques were being imported into the UK from places like Australia and Canada.  The idea was to try and mitigate musculoskeletal claims by offering services like physiotherapy.  As this approach was bearing fruit by reducing the number of claims and/or the length of claims, insurers started to look at wider rehabilitative options in other areas.  The next obvious place was to offer counselling (and other therapies) to potential mental health claimants.  This was at the heart of the change in the value of GIP schemes from simple income support when an employee is off long term absent to actively offering rehabilitation services during the initial period of absence to help get employees back to work.  GIP became more than just a simple insurance product; it began to help with the underlying issues of absence and took the beginning of its many steps towards being a rounded wellbeing product.

However, if you were aiming for zero claims, rehabilitation was not enough.  Logically, you would have to focus on prevention.  And yet, does prevention work to a level at which it is worthwhile pursuing?  This is the question that has bugged me for the last 20 plus years of my career since I looked at pricing.

The answer is complicated, but the simple version is that there is some evidence that structural preventative measures can work when applied at scale or to put it more bluntly, this tends to work when led by The Government.  The best large-scale improvements to UK Health (outside the establishment of the NHS and Health & Safety rules) have been regulations around smoking and seat belts with data that demonstrates the positive impact they had on health throughout the UK.

But what about wellbeing interventions at the employer level?  There is relatively little long term, robust data at the employer level that supports the case that prevention works.  The meta studies that I have seen all come with a caveat of length of study (it is easier to show a positive result over a short period of time – less than a year), institutional bias towards success (companies effectively cherry-picking data to show that the investment was not wasted) and use of subjective metrics such as engagement factors (rather than harder, objective measures such as changes to actual physical, mental or financial data).  All this means, as far as I am aware, is that there is very little robust data to support employers’ investment in employee wellbeing having an active, positive effect on employee wellbeing or insurance claims.

And yet, most people feel that investing in employees’ wellbeing does have a positive impact on their health and, in turn, is likely to impact on their rates of absence.  There is correlation between how physically healthy you are and the incidence and recovery times from ill-health.  There is also correlation between your psychological wellbeing and absence, especially where you are happy and engaged in your work.

So, do we stop investing in employee wellbeing or do we move to a more objective assessment of employee wellbeing activity to create the hard data that will justify it?  I know which one I want.  Perhaps, off the back of that, I might finally be able to put in place the preventative options for employers that would help reduce the cost of their insurances – the holy grail of pricing intervention – a bring a 20 year plus journey to a rare fulfilling close.

If you are interested in an objective, data driven approach to employee wellbeing, why not reach out to EBC and join the discussion around what works and how we can prove it.

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