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December 16, 2014

3 Warning Signs of Adverse Selection

Summary:

Insurers need to use better models and broader sets of data to avoid having rivals grab all the good risks and leave the bad ones behind.

Photo Courtesy of Phoebe Epstein

The top 25 insurers consume 70% of the market share in workers’ compensation, and, as the adoption of data and predictive analytics continues to grow in the insurance industry, so does the divide between insurers with competitive advantage and those without it. One of the largest outcomes of this analytics revolution is the increasing threat of adverse selection, which occurs when a competitor undercuts the incumbent’s pricing on the best risks and avoids writing poor performing risks at inadequate prices.

Every commercial lines carrier faces it, whether it knows it or not. A relative few are actively using adverse selection offensively to carve out new market opportunities from less sophisticated opponents. An equally small crowd knows that they are the unwilling victims of adverse selection, with competitors currently replacing their best long-term risks with a bunch of poor-performing accounts.

It’s the much larger middle group that’s in real trouble — those that are having their lunch quietly stolen each and every day, without even realizing it.

Three Warning Signs of Adverse Selection
Adverse selection is a particularly dangerous threat because it is deadly to a portfolio yet only recognizable after the damage has been done. However, there are specific warning signs to look out for that indicate your company is vulnerable:

  1. Loss Ratios and Loss Costs Climb – When portfolio loss ratios are climbing, it is easy to blame market conditions and the competition’s “irrational pricing.” If you or your colleagues are talking about the crazy pricing from the competition, it could be a sign that your competitor has better information to assess the same risks. For example, in 2009, Travelers Insurance, known to be utilizing predictive analytics for pricing, had a combined ratio of 89% while all of P&C had a combined ratio of 101%.
  2. Rates Go Up, and Volumes Declines – As loss ratios increase along with losses per earned exposure, the actuarial case emerges: Manual rates are inadequate to cover expected future costs. In this situation, tension grows among the chief decision makers. Raising rates will put policy retention and volumes at risk, but failing to raise rates will cut deeply into portfolio profitability. Often in the early stages of this warning sign, insurers opt to raise rates, which makes it tougher on both acquisition and retention. After another policy cycle, there is often a lurking surprise: The actuary will find that the rate increase was insufficient to cover the higher projected future losses. At this point, adversely selected insurers raise rates again (assuming their competitors are doing the same). The cycle repeats, and adverse selection has taken hold.
  3. Reserves Become Inadequate – When actuaries express signs of mild reserve inadequacy, the claims department often argues that reserving practices haven’t changed, but their loss frequency and severity have increased. This leads to major decreases in return on assets (ROA) and forces insurers to downsize and focus on a niche specialization to survive, with little hope of future growth. The fundamental problem leading to this occurrence is that the insurer cannot identify and price risk with the accuracy that competitors can.

Predictive Analytics Evens the Playing Field
The easiest way to prevent your business from being adversely selected is starting with the foundation of your risk management — the underwriting. Traditional insurance companies rely only on their own data to price risks, but more analytically driven companies are using a diversified set of data to prevent sample bias.

For small to mid-sized businesses that can’t afford to build out their internal data assets, there are third-party sources and solutions that can provide underwriters with the insight to make quicker and smarter pricing decisions. Having access to large quantities of granular data allows insurers to assess risk more accurately and win the right business for the best price while avoiding bad business.

Additionally, insurers are using predictive analytics to expand their scope of influence in insurance. With market share consolidation on the rise, insurers in niche markets of workers’ compensation face even more pressure of not only protecting their current business, but also achieving the confidence to underwrite risks in new markets to expand their book of business. According to a recent Accenture survey, 72% of insurers are struggling with maintaining underwriting and pricing discipline. The trouble will only increase as insurers attempt to expand into new territories without the wealth of data needed to write these new risks appropriately. The market will divide into companies that use predictive models to price risks more accurately and those that do not.

At the very foundation of any adversely selected insurer is the inability to price new and renewal business accurately. Overhauling your entire enterprise overnight to be data-driven and equipped to utilize advanced data analytics is an unreasonable goal. However, beginning with a specific segment of your business is not only reasonable but will help you fight against adverse selection and lower loss ratio.

This article first appeared on wci360.com.

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About the Author

Dax Craig is the co-founder, president and CEO of Valen Analytics. Based in Denver, Valen is a provider of proprietary data, analytics and predictive modeling to help all insurance carriers manage and drive underwriting profitability.

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