The advent of usage-based insurance (“telematics”) in the automotive market signaled a dramatic shift in automobile insurance from actuarial-based assessments of risk to more precise risk measurement by insurers. Using internet of things (IoT) sensors, connected through the cellular network to the cloud, insurers measured drivers’ mileage, driving times, speed, parking locations, acceleration, and braking. Insurers then compared data from each driver to the national average to determine the relative risk of each individual driver and establish premiums.
A similar transformation is beginning in Commercial General Liability for property.
React & Repair Transforms to Predict & Prevent
Telematics was an early demonstration of a transformation in the insurance industry from reacting to and repairing damage, to predicting risks and preventing claims. The benefits of this transition are two-fold. First, understanding the risks presented by any individual insured, insurers are better able to set premiums to match the risk. Low-risk customers are rewarded; high-risk customers pay premiums that match their heightened risk. Second, by measuring and understanding a driver’s risk, insurers can help individuals remediate that risk. An insurer can guide a driver to drive less, drive more slowly, or park in secured garages.
Strategies for Measuring Property Risk
Annual visits by an insurer’s team of risk analysts can efficiently and accurately measure slowly changing risks, like the condition of stairs and steps, the number of exits, and floor surfaces and condition. Similarly, smoke alarms, fire suppression sprinklers, and flood detection systems can report and remediate common emergencies. The next most important risks correlate to people: how many are present, how densely they are packed, whether they are visitors or employees, and how they are behaving.
Risk Example: Hotels
Insurers need to look for patterns of overcrowding. While visits by risk analysts are unlikely to coincide with overcrowding, risk analysts can be directed to hotels that show repeated crowding events in order to investigate. Rather than send risk analysts to properties at random, better to have them focus on property where behavior on-the-ground doesn’t correspond to expected behavior.
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