A recent CNBC article details how insurers are leveraging big data to optimize insurance pricing. This is prompting the Consumer Federation of America (CFA) and the Center for Economic Justice to view price optimization as "price gouging". Their contention is that insurers are leveraging price optimization as a way to take advantage of unsophisticated insureds who don't know there might be other insurance options (apparently these insureds don't have TV's or radios and if they do they don't understand what the gecko or Flo are trying to tell them).
While any insurer or anyone familiar with the industry knows price optimization hardly qualifies as news, two things probably made someone cover it: "Big Data" is the topic, and inflammatory remarks made the story sound sexy. If you want to draw attention to almost any article today, put "Big Data" in the title. Price optimization in the insurance industry isn't anything new. It's been around since, well, since the Phoenicians first invented insurance (or Babylonians or Hindus — take your pick, since they all have legitimate claims). It's embodied in the phrase "what the market will bear", a term often used by commercial underwriters to justify disregarding what the actuaries declare the premium should be and provide discounts to get the premium down to a point the underwriter thinks will be competitive. You'll hear underwriters say "what the market will bear" like an unending echo during a soft market.
Insurers have always modeled what rate increases will do to their expected market, both from their ability to attract new clients and retain existing ones. It's a part of every pricing actuary's job. The fact that insurers are leveraging big data as part of this long-standing practice is not news. Nor should it be perceived as harming customers. Really, it's just another tool in trying to gain a competitive advantage and underwrite business profitably.
It's not terribly surprising that Birny Birnbaum, executive director of the Center for Economic Justice, is labeling price optimization as "a euphemism for price gouging." The assumption is that he does not truly understand the insurance market well. What is surprising is that Robert Hunter, a former Texas insurance commissioner of insurance, and current Director of Insurance for the CFA, supports the position that price optimization is "unfair and discriminatory". As a former insurance commissioner, having moderated the business in Texas and overseen applications for both rate increases and underwriting, this view is very myopic.
Insurers are always looking at the propensity of customer flight. It's calculated into every rate change, taking into account demographics, longevity of the client relationship, market, competition, etc. With the prior scandals of red-lining in the industry, insurers are also very aware of the issues around socially unacceptable market modeling. Insurance regulators are there specifically to sniff out inappropriate behavior and snip any price gouging in the bud. The combined ratio for personal automobile insurance was 103.5 for 2012 with a 10 year combined ratio of 102.9 for the entire industry (AM Best 2013 Facts and Stats). This means that on average, insurers lost 3 cents for every dollar of insurance they wrote. If insurers truly are trying to do some price gouging they must be pretty inept at it.
The big lesson from the Great Recession is that insurers can't rely, or at least shouldn't rely, upon investment income. Today all insurers are looking to underwrite profitably. This may seem like an obvious goal, but for anyone who has lived through cash flow underwriting or worked for an insurer who looks to expand market share at any cost, underwriting profitability hasn't always been a top priority for insurers. Price optimization will be leveraged to not only anticipate what the price points are for insurers but will be leveraged to further segment markets into finer and finer slices. All of this will be leveraged to try and turn each segment into a profitable segment.
Optimization won't stop at the ratemaking, product creation and marketing process. It is being extended to include sales optimization. Insurers are increasingly looking to leverage both predictive and adaptive analytics to help optimize the sales process when dealing with clients today. This includes everything from objection handling to right-sizing the recommendations and quotes that are being put in front of the client. Keep in mind that "optimizing" the sales process is not the same as "maximizing" premium. Insurers know that the needs of each client are different and that if they oversell (limits too high, premium too high, too much coverage) the chances of the client cancelling or non-renewing are extremely high. If they undersell the client, especially if there is a loss and coverage isn't adequate, the insured will be equally unhappy and even may be likely to sue. Needs analysis, a staple in the life space for some time, is one form of optimization but this concept is spreading and becoming institutionalized across the sales process for all lines of business, and the service process for that matter.
Optimization itself is also continuing to evolve from more of a non-active practice to a proactive process. Predictive and adaptive analytics is morphing into decision management. Analytics has always been more of a reference in the insurance industry, results that a user or a manager would refer to when doing research or looking to make a decision. Or the user might receive a notification when certain parameters with the analytical model are met. Today, these analytics are built into the process model and drive process itself. This is all in the context of the user, the client and the transaction. When dealing with a prospective client in a call center, decisioning models will leverage information known about the client (demographics, market, region, product etc) and guide the conversation of the call center representative, making suggestions to the CSR on offers to put in front of the client, coverage definitions etc. to complete the real time needs analysis and bring the call to a successful close. Decision management technology can track the lifetime value of a client and make suggestions to sales including creating activities such setting up sales calls and the required processes that need to be completed to continue to service the client. Decision management will not only be leveraged during the sales process to optimize the customer experience and portfolio but to track and manage fraud, predict the optimum claims process and service the client.
Insurers are actually trying to learn as much as they can from other industries that have taken the practice of optimization from an art to a science. In an unusual move for the industry as a whole, many insurers are actively recruiting optimization experts from other industries, especially those with direct sales and social e-business skills, so they can apply that knowledge to the insurance market.
Is there a chance for gouging? Always. That's why the insurance industry is so heavily regulated. However, price optimization, or optimization in any form, does not in and of itself represent a threat to the insured. If anything, optimization is being leveraged by insurers to optimize the client experience and customer satisfaction. The goal of any smart insurer is a long-term fruitful relationship for both parties. Not a get-rich-quick scheme.
About the author: Tom King is a Senior Director of Insurance at Pegasystems, a provider of business process management (BPM) and customer relationship management (CRM) software solutions.