The Risks of Hazard

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?

Last week I attended an event in San Francisco that was focused on Innovation through Big Data in Insurance. San Francisco in autumn is a pleasure (especially the St. Francis Yacht Club – what a great venue!), and the opportunity to connect with colleagues new and old is always really good. The show itself was interesting, too, but not for the reasons I expected.

Insurance has been working with big data since before “Big Data” was a buzzword – in fact, for centuries before the term was coined. Unlike other businesses that have been disrupted by software platforms and the enormous amount of data they can access and leverage, every insurance company in existence already leverages big data (with varying levels of efficiency). Showing up at this show, I was really curious to see what disruptive insurance might look like.

One of the challenges (and joys) of writing The Risks of Hazard is to find an interesting perspective on topics that concern underwriting property insurance. But, sometimes, there is an event that has only one angle: head-on. The “one in a thousand years” rain in South Carolina is a perfect example.

There is no doubt about it – South Carolina has had some seriously bad rain; tons of it, brought by the highly unusual convergence of at least eight key factors. October 4th was the rainiest day on record in Columbia, with almost 7 inches falling on the airport. Charleston set their own single day record on October 3rd with 11.5 inches hitting the city. In addition, it’s already the wettest October on record for most of the state, and we are only a week into it. But, to hear Gov. Nikki Haley state: "We haven't seen this kind of rainfall in the low country in a thousand years," is not just misleading – it is nonsense. Dave Baker at KATC in Louisiana has saved me the trouble of explaining why.

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.