If you’ve got your eyes set on technology that won’t move the needle this year, it’s time to reevaluate what can provide bottom-line results in the short term. AI and machine learning will have their day in commercial insurance. But what are you doing today to drive tangible business results? Insurtech does not have to be a “pie in the sky” endeavor. It can be deployed right now.
Just a year ago, the insurtech conversation was all about innovation labs, blockchain, IOT, wearables and, of course, AI. Now, the dust has settled a bit, and the realization has set in that those bright, shiny objects may take years to make a real impact on re/insurers’ bottom lines. While they are still undoubtedly vital to innovation, long-term success and survival, it’s important to strike a balance between “pie in the sky” and practical. Last year’s devastating catastrophes served as a catalyst for more focus on short-term solutions that can improve bottom lines—now. Not years from now. This swing to here-and-now solutions was recently articulated in an article by Ilya Bodner, founder of insurtech startup Bold Penguin, where he notes:
“Insurtech is moving rapidly now into commercial lines where the attention and intent is focused on solutions that will deliver a strategic and immediate return on investment (ROI)....Insurers are moving away from bright, shiny, insurtech objects and toward service partners, emerging technologies and solution providers with a return on investment more immediate than promised for five years down the road.”
I second this sentiment. P&C risks are changing, as evidenced by 2017’s $144 billion in global insured losses and a commercial lines combined ratio of 104%. And, while a strong market made many insurers whole last year, that is not a guarantee going forward. The next hurricane, flood or wildfire won’t wait for you to innovate. Insurers must find ways to bring innovation to their bottom lines now. Don’t get me wrong, pie in the sky is good—and it is necessary. But insurers must strike a balance between their long games and short gains. You need both.
Caution: The hard truth
I don’t have to tell you that following last year’s back-to-back hurricanes
there was an outcry about how the models got it wrong (of course, it didn’t help that some modelers put out early and grossly inaccurate estimates that incited market confusion and concern). Here’s the hard truth: Insurers also got it wrong. Got it wrong by using a single view of risk; by not taking advantage of innovations in data; by taking too long to operationalize data; by waiting for the perfect, utopian platform (in-house or commercial) to be built or delivered; by expecting legacy analytics software to deliver the scalability, reliability and insight required to act efficiently and effectively. No longer can insurers approach risk The. Same. Old. Way. Risk is changing. You must change with it. And the good news is, integrating insurtech in a way that helps you better assess and manage the evolving landscape of catastrophe risk doesn’t have to be time-consuming or costly, and it can produce immediate results.
Here are a few of the challenges that insurers face that insurtech can help them address, in the here and now:
See also: Can Insurtech Rescue Insurance?
- Reality: Models provide a “framework for thinking; they don’t represent truth.” Evan Greenberg, chairman and CEO of Chubb, recently stated, “Given there have been three one-in-100-year floods in 18 months, how can Harvey represent a 1% chance of occurring, as the models suggested? Models provide an organized framework for thinking; they don’t represent truth.” Now, we all know models serve an important purpose, and our clients can derive insights from modeled data within our platform. But models must be taken with a dose of good old-fashioned human judgment. Models and the outputs are nuanced. It’s all about identifying the right models and model components that best represent your lines of business, geography and business practices. But it’s also about balancing resources and business value with this expensive exercise. You need to have an intelligent conversation about model nuances—and figure out the “so what” questions that models provoke but don’t answer.
- Reality: You can’t handle all the data. There’s a gap between the wealth of data now available and an insurer’s ability to quickly process, contextualize and derive insight from that data. Insurers are generally frustrated by a lack of process and an easy way to consume the frequent and sophisticated data that expert providers put out during events like Harvey, Irma, Maria, the Mexico City earthquake and the California wildfires. Beyond the sheer volume of data, insurance professionals are expected to make sense of it by using complex GIS tools. In reality, you have all this data but no actionable information because you can’t effectively make sense of it. Even insurers with dedicated data teams and in-house GIS specialists struggle to keep up. (SpatialKey tackles this problem by enabling expert data from disparate sources (e.g. NOAA, Impact Forecasting, JBA, KatRisk) and putting it into usable formats that insurers can instantly derive insight from and deploy throughout their organizations. We do the processing work, so our clients can focus on the analysis work.)
- Reality: Your best data is your own, but you’re not benefiting from it. It’s one thing to be in possession of data, and quite another to be able to realize its full value. Data alone has little value. One of our clients, for example, needed a way to re-deploy its own data to its underwriters, so we helped the company integrate an underwriting solution that would put its data, along with expert third-party data, in the hands of its underwriters—all from a single access point that would consolidate disparate sources and drive enterprise consistency.
- Reality: Your customers expect on-demand; you should, too. Your customers don’t want to wait for a quote or go through a lengthy process to submit a claim. Our society is instant everything, and while commercial insurance may not be held to the same real-time pressure as personal lines, it is moving in that direction. When you need the latest hurricane footprint, you need it now, not four hours from now. When an earthquake strikes Mexico City, you need to understand your potential business interruption costs today. When a volcano is erupting and no drones are allowed in the surrounding airspace, you need a geospatial analytics solution that can help you provide advanced outreach to insureds and do the financial calculations to understand actual exposure. Likewise, when your underwriters are trying to win business, you’d rather they spent their time evaluating the risk than searching for information.
Who knows what this hurricane or wildfire season will hold. The question is, are you prepared to handle it better than last year? What changes have you made to strengthen your resilience and that of your insureds? What has been learned and applied for meaningful results? It’s a misnomer that insurtech and disruption go hand in hand. Some insurtech solutions are built to complement—to drive efficiencies, cost savings and underwriting profitability—not necessarily replace existing processes or legacy systems. Data and analytics is an area where insurers, brokers and MGAs can still improve their bottom lines yet in 2018.
See also: To Be or Not to Be Insurtech
Take down the pie and dig in
My intention is not to dilute the importance of up-and-coming insurtech technologies, like AI and machine learning. They will undoubtedly help insurers compete as risks become more complex. My point is that those longer-term technological investments must be tempered with an understanding of what technologies will help move the needle in the present. You can strike a balance between pie-in-the-sky insurtech and insurtech that works for you now.