Author: Monique Hesselink
During a recent long flight from Europe, I read up on my insurance trade publications. And although I now know an awful lot more about block chain, data security, cloud, big data and IoT than when I boarded in Frankfurt, I felt unsatisfied by my readings (for the frequent flyers; yes, the airline food might have had something to do with that feeling). I missed real live case studies, examples of all this new technology in action in normal insurance processes, or integration into down-to-earth daily insurer practices. Maybe not always very disruptive, but at least pragmatic and immediately adding value.I know the examples I was looking for are out there, so I got together with a couple of insurance and technology friends and we had a great time identifying and discussing them. For example, the SynerScope team in the Netherlands told me that their exploratory analysis on unstructured data (handwritten notes in claims files, pictures) demonstrated that an unexplained uptick in home owners claims was caused by events involving horses. Now think about this for a moment: in the classical way of analyzing loss causes we start with a hypothesis and then either verify or falsify that. Honestly, even in my homeland I do not think that any data analyst or actuary would create a hypothesis that horses would be responsible for an uptick in home owners losses. And obviously “damage caused by horse” is not a loss category on the structured claims input either, under home owners coverage. So up to not too long ago, this loss cause either would not have been recognized as significant, or it would have taken analysts enormous amount of time and a lot of luck identifying it by sifting through mass amounts of unstructured data. The SynerScope team figured it out with one person in a couple of days. Machine augmented learning can create very practical insights.
In our talks, we discovered these type of examples all over the world; here in the USA, a former regional executive at a large carrier told me that she found an uptick in house fires in the winter in the South. One would assume that people mistakenly set their house on fire in the winter with fireplaces, electrical heaters etc to stay warm. Although that is true, a significant part of the house fires in rural areas was caused by people putting heating lamps in dog houses: to keep Fido warm. Bad idea.. Again; there was no loss code for “heating lamp in doghouse” in structured claims reporting processes, nor was it a hypothesis that analysts thought to pose. So it took the trending of loss data over years before the carrier noticed this risk and took action to prevent and mitigate these dreadful losses. Exploratory analysis on unstructured claims file information in a deep machine learning environment, augmented with domain expertise and a human eye -as in the horse example I mentioned earlier- would have identified this risk much faster. We went on and on about case studies like those..
Now, although I am a great believer and firm supporter of healthy disruption in our industry, I think we can support innovation by assisting our carriers with these kind of very practical use cases and value propositions. We might want to focus on practical applications that can be supported by business cases, augmented with some less business case driven innovation and experimenting. I firmly believe that a true partnership between carriers, instech firms and distribution channels and a focus on innovation around real-life use cases will allow for fast incremental innovation and will keep everybody enthusiastic about the opportunities of the many new and exciting technologies available. While doing what we are meant to do; protecting homes, horses and human lives.