This is an extended version of the article that appeared in the September 2015 issue of Investment Advisor.
Insurance is acknowledged to provide not just a valuable bulwark in case of loss, but also to allow clients to sleep at night. Peace of mind is worth a premium — but just how much of a premium? That depends on what kind of information is being used to determine how much the consumer pays.
If you’ve been following the headlines, you’ve no doubt noticed that Big Data is playing an increasing role in all sorts of things, from targeted ads on your Web browser to bargain — and maybe less-than-bargain — prices on everything from airplane tickets and hotels to tools and home furnishings.
Big Data also plays a role in how your clients’ car insurance policies are priced. Thanks to price optimization software, consumers’ shopping habits, marital status and other factors unrelated to their driving and safety record are now taken into account by a number of insurers in multiple states when pricing auto insurance premiums.
While the rumble over price optimization has been growing over the past couple of years, a fresh flurry of reports at the end of July highlighted a study by the Consumer Federation of America (CFA) that found that premiums on state-mandated liability coverage for single, separated, divorced and widowed individuals increased by an average of 20% at four of six major insurers.
Some might argue, as the Insurance Information Institute (III) does, that there has been an “absence of consumer complaints” about the use of price optimization, which relies on myriad data to predict not just how an individual might drive but also how much he or she might pay for insurance before reaching the tipping point and deciding to look elsewhere.
That absence of complaints could be simply because the public hasn’t quite realized how much data unrelated to their driving skills is being used to determine the premiums they’ll pay. According to a survey commissioned by CFA, there was “overwhelming consumer support” for insurers to emphasize “driving-related factors such as accidents, traffic violations and miles driven” when determining pricing.
People don’t like to be taken advantage of, and numerous reports have pointed out that if Big Data indicates a certain demographic is more inclined to be loyal customers (perhaps senior citizens who aren’t Web-savvy, or those singles, widows and divorcees who are busy dealing with a bunch of other tasks?), they’re most likely to get hit with rate increases simply because they don’t routinely go hunting for bargains. (Not for nothing is the use of marital status in pricing known as the “widow penalty.”)
Those consumers probably don’t think that’s fair — and neither does Bob Hunter, insurance director at CFA, who said in a statement, “It is terribly unfair and entirely illegal for insurance companies to vary premiums based on whether or not [individuals] are statistically likely to shop around.” In the wake of Florida’s decision to ban the practice, Hunter termed the use of price optimization “price gouging,” while Birny Birnbaum, executive director of the Center for Economic Justice, called the practice “Big Data run amok.”