Eliminating myths to gain value from analytics
Despite what an unending media blitz around all things InsurTech might have you believe, there are many insurers who haven’t fully embraced how much their business can improve through the use of new technologies.
As an industry, insurers still treat innovation like an accessory to current business models, as opposed to a way to make smarter choices, align risk with premium, increase profitability and retain customers longer.
While many P/C insurers are becoming more cognizant of the fact that data analytics are a key differentiator for their businesses, there remains a series of myths that stand in the way of general adoption. In order for companies to truly accept innovation into their DNA, they must first dispel these misconceptions.
Analytics aren’t a magic bullet
The first myth has to do with a lack of granularity that comes when insurers first implement analytics. Many carriers mistakenly believe that data can identify good and bad risk classes, which is both inaccurate and oversimplified.
Predictive analytics isn’t a magic bullet that identifies entire classes to avoid, and this false understanding can lead to less than optimal results. Eliminating an entire risk class only offers one assurance; that an insurer will miss out on pockets of good risks and profitable business.
Instead of accepting that fate, insurers should use analytics to identify the good risks within each class to more effectively build a book of business. This is particularly true in lines like commercial auto, where both frequency and severity of claims have intensified, leading many carriers to reconsider their stake in the game. Across each subgroup within the line (including things like light pickup, heavy truck, etc.), there are swathes of profitably business waiting for smart insurers to get involved.
Analytics aren’t ‘one size fits all’
A second myth of analytics that’s generated a following is that analytics offer insurers nothing more than a race to the bottom. There is an idea that each insurer will use the same data, derive the same conclusion, and ultimately end up competing on writing the same exact policy for similar rates.
The reality is analytics allow an insurer to better align price to risk uniquely within their own book of business, ensuring profitability. To illustrate this, Valen studied best practices for analytics implementation, looking at the decisions insurers made to achieve specific business goals. Our approach simulated head-to-head competition between two anonymous companies, using actual models in production today in the same state, where one insurer with less market share tries to gain against their competitor’s model and vice versa.
We found that in both scenarios, the challenger was able to gain significant market share of premium that came with a low loss ratio advantage. In short, carriers were able to attract that business they wanted to attract, with premiums that aligned with their individual risk profiles.
Analytics aren’t a replacement for experience
A common concern when using analytics throughout the industry has to do with whether or not a technology solution will replace the people in an organization. Nothing could be further from the truth. Analytics are highly advanced tools seeking to help insurers make their employees’ roles more streamlined and efficient. Our studies have regularly shown that a combination of personal experience and analytics yields results that are more successful than “people” or “machines” alone.
Rather than considering analytics as a threat to employment, insurers should look at these efforts as recruitment tools for a new generation of insurance personnel; one that was brought up with a laptop at their fingertips. The insurance industry is poised to see more than 70k professionals retire in the next year. Smart carriers will see an opportunity to attract millennials and young professionals to fill these roles through their use of technology.
When incorporated into a P/C business, data analytics can have a profound impact, allowing insurers to better align risks to premiums and increasing profitability. However, until the industry is able to overcome misconceptions, such as “analytics eliminates business lines” or “the race to the bottom,” the wide-ranging benefits of analytics solutions will never fully materialize and insurers will continue to underutilize the competitive advantage that the data can provide.
An appropriate mindset and strategy around analytics is just as important as the tools themselves.