‘Big data’ is still a buzzword but if data isn’t on your radar it ought to be. In our digital world, data is key to running a successful business, especially if you deal with individual consumers. Indeed, some firms make data their business, acquiring it, analyzing it, interpreting it and selling it to your business – and maybe helping you apply it to what you do. Twitter’s core activity is generating and selling useful data. It just happens that people use it to communicate using 140 characters or less. Google is an excellent search engine but also it knows a heck of a lot about its users and their behaviors, and it makes big bucks selling that information, suitably analyzed, to corporations.
Predictive modeling (or predictive analytics) and its implications for marketing, sales and underwriting have been a pretty hot topic among life insurers and reinsurers over the last few years but the discussion has to a large degree been theoretical. But now it has gone practical. In 2014 Principal Financial began to write up to US$1 million on ages up to 60 on the basis of a tele-interview alone – allied to use of some powerful data. ING followed suit.
We would not be surprised if some propositions along the same lines emerged from other carriers. Is this the end of life insurance – and maybe more important to you, dear reader, of underwriting – as we know it? Unlikely, but it does look as though the game, or at least part of it, has moved on. The vendors of data and the clever analytics have been claiming that their models are as good as – and sometimes better than – traditional underwriting. Now those claims are being put to the test. And presumably some clever actuarial types have carefully examined the data and pronounced it satisfactory. If they have got some of their sums wrong they can tweak a little here and there – chances are that a major re-think, or even total abandonment, will be unnecessary.
We’ll have to wait and see how it all pans out. And also to see if other carriers join the fray and, if so, what their propositions will be.
However, we would counsel against doing nothing while waiting. Because, as we said at the beginning, data is important in business. Even if the predictive modelling pioneers have got it hopelessly wrong, there is still plenty to do with data. Chances are you already interrogate the MIB, motor vehicle records and pharmacy databases, so you use data already. The big lab firms have developed some powerful analyses based on their millions of accumulated test results. You may use that information too.
If you use an underwriting engine already, that’s great – but see how you can widen its use as far as possible throughout the business. If you don’t have an engine now, you should have one. Engines log information about all aspects of applicants, their risk features and every stage of the underwriting process. The resulting data is invaluable for understanding the risks you write and, when married with data relating to the agent or broker, completions, lapses and claims, tells you precisely who you customers are, how their apps were filled in, what risks you’re writing and where your profitable business is coming from. Just as underwriting enables you to stratify risk, the data enables you to stratify profitability, to segment producers, channels and customers.
Now what if that ‘traditional’ sort of data from internal sources could be combined or overlaid with external data? Wouldn’t that give your company even more power to target new customers, to create propositions more likely to meet their individual needs? That information might even enable you to simplify the underwriting process (for speed, service and efficiency), or enhance it for better risk stratification.
Back to big data again. Smart use of data really seems the way to go.
A big issue for carriers, and also for reinsurers come to think of it – none of whom historically have been good at generating and using management information – is figuring out what data they need and how they are going to use it. They need a data strategy, especially since data and technology are changing the market landscape.
