There is probably an entire branch of microeconomics dedicated to the topic of pricing, but I’ve always found the art of pricing life insurance to be strangely fascinating.
To understand why, a little history lesson is necessary. When it comes to working out the premium to be paid by an insurance client, the goal of any insurer is to accurately understand the riskiness of the client — typically by using a number of rating factors — and using those rating factors to determine the right price.
Rating factors are typically things like age, gender (yes, women live longer and therefore pay less), whether or not someone smokes, and level of education.
But back in the day — when a computer filled an entire room — there was no capability to take these rating factors (and others) into account and calculate an individualised premium. The sales guys who ran around and had to provide quotes to their clients didn’t have iPads, and the internet wasn’t a thing. So instead, insurers would print little books which contained pages and pages of premiums.
To make these books (relatively) practical and usable, the insurers devised a system of allocating people to fairly large, yet homogeneous groups. For many years, most insurers had four groups covering the “best” lives (who got the lowest premiums) to the “worst” lives. And they devised a simple cheat to use the chosen rating factors to determine which group a person was in — and then using that group, it was fairly easy to work out a premium.
Naturally there was, even then, a lot of data being used, and an army of actuaries set out each year to work out the right premiums, so the books could be kept up to date. This means that for the group as a whole — the premium tended to be right. However, because there could only be a finite number of groups (the sales guys couldn’t be expected to carry encyclopedia-sized rate books), within each group there was a lot of subsidy in premium.
Fast forward a few decades, and the ability to create computerised quotation software began to emerge. However, for whatever reason, rather than going back to first principles and pricing without the constraint of book size — most insurers simply replaced the book with software. And yes, that meant that these rate groups lived on.
Right, history lesson over. You’re probably thinking that now that 2017 has swung round, and Elon Musk is landing reusable rockets, insurers would have abandoned this archaic pricing mechanism. You would be wrong.
We know a few things that every insurer knows:
- We have mountains of data and years of claims history
- We know quite a lot about our clients
- And, with some willingness and drive — you can combine these two, and take into account the fact that computers are pretty good these days — and work out the right price
So, what does this mean for you, a potential client?
Well, a few things.
Firstly, at Indie we work out an individualised premium using complex statistical algorithms for everyone, which we believe is the right price.
Secondly, by looking at the data we have, and working closely with some bright minds at our reinsurer, we identified a few other excellent rating factors, which have been hidden in the data the whole time.
So, because we want to give everyone the best price, while also giving everyone the right price, we may ask one or two things extra that other insurers don’t. You might be asking why we care so much about getting the right price. And this is, for me at least, the most interesting part.
If you look at the graph above, I’ve simplified things a bit. The red ‘stepped’ line represents the old-school way of pricing, where everyone in the same group (labelled Groups 1 to 5 for simplicity) pays the same price. The purple line is what we call the “Right Price”.
Notice how the right price intersects the old-school price? The shaded purple areas are where we, by simply pricing to the right price, are cheaper than the competition. The red areas, similarly, are where we’re more expensive than our competitors. And this makes us happy.
While we want everyone to be Indie clients, we’re happy to lose a client on the basis of price to a competitor.
Why? Well, if our price is the right price (as we believe), this means that if our competitors are cheaper, then they are getting clients at the wrong price, and are being set up for some lekker losses down the line. Even better, the individual client still wins. They either get cheaper prices from us, and we still remain profitable, or they get cheaper prices from our competitors, but our competitors are subsidising them. Either way, the client comes out on top.