At the PAMIC Annual Spring Conference in State College, PA last week, there were some outstanding presentations on industry trends. Michael Bond from Hartford Steam Boiler, Joel Hopkins from Saul Ewing Arnstein & Lehr LLP, Andrew Seiffert from BMS, and Special Agent John McCall from the National Insurance Crime Bureau all touched on how data and analytics are transforming the insurance industry. From the use of IOT to improve Commercial Auto results to utilizing historical weather patterns to better predict catastrophe loss costs, the use cases were impressive and inspiring as they showcased how much opportunity there is to innovate in the insurance marketplace. While a lot of feedback was positive, a common theme we discussed with clients afterward was the applicability of such advanced techniques to their company – many of the companies, especially those on the smaller end of the market, felt a bit intimidated and overwhelmed by the broad topic of analytics.
The marketplace reality is that every company, regardless of size, needs to embrace analytics to more effectively compete going forward. Those that do not will be at a competitive disadvantage against others that do – adverse selection can be quick and debilitating for a company that is left behind. To be most effective though, the approach needs to be customized based on the size, scale, and business priorities of an organization. A $25M premium company creating a telematics solution that will rival the large, publicly traded companies is not realistic – the cost, infrastructure, and expertise would be challenging to practically bring to market. But beginning the journey by fully utilizing their own data and marrying it with readily-available third-party data can have a profound impact on their business performance.
The most commonly used framework for a company’s analytical journey is Gartner’s four stages of analytics:
For every company, there is business value across all stages of the analytics continuum. We have experiences where descriptive analytics in the form of improved management information systems for monthly results can bring tremendous actionable insight to the executive leadership team. A deep understanding of what happened for a given month through well-crafted data visualization tools like Tableau or Power BI can be incredibly powerful – seeing truly is believing.
The next evolution on the analytics journey is diagnostic – why did the business perform in the way that it did? As an example, not just knowing that premium growth was positive in a month, but understanding the key drivers for premium growth between new business and renewals is also important, as profitability implications are different. Ensuring that the business on which our clients are winning is intentional and purposeful (and not that the marketplace has found the soft under-belly) is so important for being a consistent and sustainable competitor. The business value of such insight is profound.
Predictive analytics has received a lot of attention over the last few years with the increase in available data, more powerful computing capabilities, and the creation of new positions like data scientists and data engineers. Successfully implementing predictive analytics into a business model is exceptionally challenging; it touches on every part of the value chain (agency relationships, technology capabilities, underwriting roles and responsibilities, etc.) and involves many different implementation decisions–but the opportunity to improve performance is very real. The investment to embark upon this leg of the journey starts to add up as well – it is not unreasonable to spend over a quarter of a million dollars for a first-generation model – but the return on investment is very high when the new model is well executed.
The last part of this journey, prescriptive analytics, is very rarely attained, as the complexity and cost are substantial. The approach here is to have a group of predictive models that interact with one another to simulate the insurance market with a focus on creating options that optimize top- and bottom-line results. The complexity inherent in this approach and the broad organizational impacts are so profound that this stage is mostly aspirational, even for the largest companies. Along Gartner’s continuum, this is why both the value and difficulty are high; if there was a third dimension, the additional risk would also be high. Although difficult, there are real competitive advantages for a company that reaches this plateau; the one that is most regularly referenced as achieving this status is Progressive.
With so much innovation in data and analytics arising in the insurance space right now, these are very exciting times to be in the industry. Our belief is that the successful carriers of the future will balance a focus on this innovation with a strong ability to utilize existing competitive advantages to execute locally. The broad spectrum of analytics can be overwhelming, which is why we focus on a crawl, walk, run approach – get some wins based on insight that had never been observed before, build momentum, and then keep on driving success on this never-ending journey!