For those who want to transform commercial auto insurance into a profitable endeavor, predictive analytics just might do the trick.
The automotive insurance industry has seen better days. Smartphone technology has led to an era of distracted driving, which has caused a greater number of accidents and vehicular fatalities in recent years. To add to this unfortunate circumstance, manufacturers are designing more lightweight, fuel-efficient cars, which are more likely to be totaled, and litigation filed against insurance companies has skyrocketed. Claim severity, frequency, and duration have all increased, leaving insurance companies struggling to maintain the profitability of their auto product lines.
In the age of analytics, however, there’s no need to sacrifice potential revenue sources in an all-or-nothing decision. Recent evidence suggests that a precise, data-driven assessment of risk could help insurers understand exactly what risks may be yielded by different auto insurance policies, enabling smarter policies and more sustainable revenue streams.
How a Granular Approach Can Work
A granular, data-driven take on risk assessment in auto insurance is nothing new. In the 1990s, Progressive used this same approach to scoop up what looked like an unappealing market share, and advanced data analytics helped to determine exactly which policies could work in the lower end of the market. As a result, Progressive discovered that policy types were more predictive of policy profitability, not vehicle types (as insurers previously believed). The changes didn’t stop there, either — in later years, a group of auto insurers upgraded a standard 10-20 variable formula (including the age, gender, and driving record of drivers) to include more than 1,000 variables, like credit scores and data from Yelp.
Most recently, Progressive introduced the Snapshot telematics tool, a sensor that collects data about the driving habits of participating policyholders. With this tool, Progressive intends to separate the good drivers from the bad ones, and Snapshot was offered as a bargain deal for policyholders who sought premium discounts. Since then, entire insurtech startups have cropped up around the concept of data-gathering telematics tools, and some groups of consumers are happy to buy into this experimental practice.
The benefits of assessing risk with greater precision should be obvious to anyone who works in the insurance industry: auto insurers are able to stay in business if they can accurately predict the risk posed by an individual driver. By accomplishing this granular view of risk and policy pricing, insurers can ensure that commercial auto insurance lines won’t be a budgetary disaster. Plus, because other insurers are avoiding this market, savvy agencies can snag a larger market share of these lines if they can craft the right policy strategy.
Integrate the Old and the New
While the granular approach holds promise, some insurers are still hesitant to incorporate it into their existing operations. As driverless cars and pay-per-mile insurance policies begin to surface and push humans out of the insurance market, executives fear the move away from what has worked in the past: the guiding hand of an experienced agent. It seems that this hesitancy isn’t entirely mistaken, either — research from Valen Analytics suggests that combining the real-world experience of agents with predictive data can help to increase the effectiveness of policy pricing by 185 percent. In adopting the analytical approach, then, insurers would do well to pair new technologies with the practical expertise of agents.
To gain the confidence of industry decision-makers, advocates of insurance analytics should attempt to address any and all objections while building a well-reasoned consensus. It’s also crucial to ensure that all members of your insurance agency are fully on-board with new analytics tools. While data analysis is a useful tool, it must be seen as just that — a tool in any successful insurer’s toolbox.