The Risks of Hazard

Last week, Property and Casualty 360 published a post by Sean Allen entitled 4 areas for the Insurance Industry to Modernize. The article is based on the consensus after the Xchanging London Market Conference last November, and it starts with:

Fair or not, many look at insurance as an outdated industry, lagging behind others of similar stature and scale when it comes to innovation.

Last week I wrote about the event in San Francisco I went to where we explored innovation in insurance with Big Data. The point I made was that Big Data and new software are innovating insurance incrementally, but not disruptively. Since then, I’ve been wondering if I was selling the innovation short. Is there actually something disruptive happening? There are plenty of articles out there suggesting there is, so what am I missing?

One of the story lines emerging from this year’s Rendez-vous de septembre in Monte Carlo was the continued relevance of P & C insurance and reinsurance. At first, it sounds a bit overdramatic, but there is something to it.

The continued relevance of the P & C industry was first questioned in 2012 by XL Group’s CEO Mike McGavick in an Insurance Journal article. Many of his points were centered on the relationship of insurers and reinsurers with other global industries, including their access to capital. The one point from this three-year old article that resonated for me is this:

As another example McGavick noted that following the floods in Thailand the price of computer chips rose by 10 percent. But the P & C industry’s response hasn’t been to offer solutions. It’s mainly been to impose sub-limits or to exclude it entirely. [McGavick] warned: “We cannot exclude our way to prosperity, and we cannot sub-limit our way to relevance.”

From a P & C executive, a CEO no less, those are fighting words, a rallying cry. Unfortunately, Mr. McGavick was viewed more as a Cassandra (to use IJ’s description) than a Henry IV.

Big data is big news, and rightly so. The ability to glean answers from huge datasets is enabling previously impossible innovation in insurance, just like these gents mentioned a few months ago. There are datasets that comprise decades of building history for most houses in the United States. There are geospatial models of the Earth’s surface, with elevations every 15’ or so for the lower 48 states and Hawaii. Then there are risk models and hazard maps that combine all sorts of scientific data. And then there are historical records of losses and loss-causing events. There are tons and tons of data — to no end.

However, there is an unspoken premise for solutions that excavate answers out of data: the right data is in there somewhere. Even though there are “predictive analytics”, “intelligent algorithms”, “virtual learning” and other software tricks to deliver answers, there is nothing quite like having the right data involved. Sometimes there is no “right” data – wouldn’t it be nice if there was a catalog of future floods or earthquakes? Without some predictive modeling, underwriters would be dealing with the Turkey Problem. But using the right information in predictive models or algorithms is, again, essential.

Carrier Management published a great feature on innovation in P & C insurance last month (June 2015). Inside the Minds of Insurance Innovators explores the subject of innovation with a group of 16 leaders from different carriers, reinsurers, brokers, rating agencies, and (for good measure) a modeler, by asking each of them 11 questions. The questions and responses present an overall picture of how a 400+ year-old industry approaches the future at a time when the business and technology is changing faster than ever before, and the speed of change is still accelerating.  

There are some definite trends in the responses, and some complete outliers. I'd like to explore what these 16 industry thought leaders are telling us with a two-part series: In today's post I will focus on the consensus. Later in the week I will look at the not-so-universal responses, which are interesting of course because they're different.