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In a sharp departure from the past, U.S. marine insurers are turning their attention to pricing tools and relying more on data analytics when it comes to setting rates. Indeed, insurers have slowly come around to accepting an idea that once was heresy — that more appropriate rating comes not only from the wisdom of years of experience, but also from data that can clearly show patterns of loss and risk and therefore lead to more adequate rates to fund claims.
Historically, insurers in lines such as marine have employed a great deal of autonomy in setting rates, relying primarily on “experience rating.” And during periods when interest rates were higher and investment returns significant there was even less pressure on pricing, as high interest rates enabled overall profit for insurers, even when underwriting profits were elusive.
This seismic shift toward the use of pricing tools to set more precise rates arguably benefits the greater good. Charging an adequate premium from all participants to allow for payments being made to the few that sustain loss or damage is not only a noble idea but critical for commerce to continue and welcomed by most who own assets and run businesses.
Despite the more prevalent use of rating tools by marine insurers, there continues to be a wide disparity in rates for the same risks. Using past data to predict the future is still a very subjective process. With the ever-present potential for catastrophe activity, rogue actors, financial changes with policyholders, etc., rating tools can only provide a basis from which to act. They cannot, nor will they likely ever be able to, give absolutely correct pricing or guarantee a desired result.
Apart from analytics, there are factors that affect how insurers derive what they deem to be an adequate rating. First and foremost, not every insurer is duty bound to use actuarial rating on a specific risk. When underwriters use a rating tool, there remains considerable latitude between the price the rating tool dictates and the final rate provided.
For example, when faced with a lack of statistically significant data, or an unfavorable rating from the tool, underwriters often make use of various insurer credits to arrive at a price they predict will allow them to win the business. For instance, an underwriter may apply a credit on the overall rate for a tug that is only operated during the day. Conversely, underwriters may apply a debit, or a surcharge, to a rate if this risk is worse than predicted by the tool. An example of this would be adding an additional charge to a tug that ventures out of the area for which it was originally rated. These credits and debits vary significantly between insurers, as does the individual underwriter’s desire to use them and their ability to implement them. It stands to reason that an underwriter is far more likely to turn to insurer credits to derive favorable rates for a desirable account.
Insurers’ use of exposure rating and/or loss rating, two distinct but equally important aspects of underwriting, is another contributor to rate disparities. Every account should be rated on both an exposure and a loss basis. Exposure ratings rely on analytics and underwriting experience of the class of vessel being assessed to derive a baseline rate, whereas loss basis rating accounts for prior loss history. If the claims record is clean, then ostensibly the exposure rating can work as the means to set rates, as loss rating will have little to no impact.
If losses are present, however, exposure rating alone might be inadequate to achieve the desired loss ratio. When this happens, more premium than experience rate provides could be necessary to account for the extra cost to underwriters resulting from the loss history.
Oftentimes when underwriters are faced with an account with a history of losses, they decide to rate up on a loss basis only. This addresses only one component of the rating process — loss history.
Underwriters would be better off going through the process of exposure rating as well to make certain that both loss history and exposure are taken into account to arrive at more reliable rating. Ideally, rating tools used will consider both exposure and losses when delivering a rate for a specific risk.
Perhaps the most warranted factor contributing to inconsistencies in rate setting is the difference in internal expense loads between insurers. Expense loads are a justifiable reason for disparate rates being applied to the same risks by different underwriters and the truest differentiator within insurers’ control. Insurers with lower expenses should be able to charge lower premiums for the same risk than those with higher expenses.
These factors are critical to keep in mind when thinking about pricing, because insurance is abstract by nature. Underwriters do not have the benefit of hindsight; they never know the actual correct price to charge until the policy period has expired.
And while boards and actuaries hate this, underwriters cannot escape the element of uncertainty when accepting a risk for a set price and conditions, at least in the fixed premium world in which most marine insurers operate. Thankfully, the increased reliance on pricing tools makes the risk a safer bet.
This gets us to where we are today: making more and better use of rating tools. As an industry, marine insurers have done a reasonably good job of collecting data over the years. Their shortfall lies in the ability to mine that data and use it to formulate tangible assumptions to derive a more statistically significant rating.
Although improving, there is still considerable latitude in underwriters’ hands. Two insurers using the same rating tool may derive vastly different pricing for the same risk based on their interpretation of the data provided by the tool as well as their tendency to fall back on familiar pricing practices such as the use of credits. Once the data becomes more dependable for all marine carriers, expense loads should be the biggest price differential.
While U.S. marine insurers have made tremendous strides to catch up to other lines that have been using actuarial data for longer periods of time, we have a ways to go. Until we reach the place where all insurers view the same risk in a similar rating paradigm and are able to reduce expense loads through more efficient processes and therefore drive lower premiums, we will continue to see disparate ratings for the same risk.
Although pricing variance is not a bad thing in the interest of free trade and avoidance of monopolistic activities, a smaller gap and fewer outliers will lend credibility to the reliance on rating tools.
John Ellis is head of U.S. ocean marine at Canopius USA in New York. He can be reached at firstname.lastname@example.org.