Tag Archives: Eric Krafcheck

Addressing actuarial and tax issues arising from premium rebates

An unexpected issue faced by property & casualty insurers during the COVID-19 pandemic has been premium refunds to policyholders – especially on personal auto policies.

The refunds and rebates are justified by substantial reductions in auto claims and losses because people have driven less as a result of being sheltered at home, and as stores and restaurants have remained closed or with restricted operations.

Beginning in late March, shortly after stay-at-home orders were widely imposed nationwide, many insurers began voluntarily paying partial premium refunds to personal auto policyholders. In addition, some state insurance commissioners have mandated auto premium refunds because of the reduction in miles driven. In addition, where permitted, some companies have begun offering discounts on renewal premiums to reflect the current better-than-expected loss experience instead of or in addition to refunds on current policies.

In this article, Milliman’s Susan Forray and Eric Krafcheck discuss actuarial considerations for premium refunds. They also provide perspective on the tax treatment of premium refunds from the standpoints of insurers and policyholders.

Foley & Lardner’s Richard Riley, Jr., also contributed to this article.

How could COVID-19 affect the auto insurance industry?

While the health and welfare of people around the world is the most important concern, the outbreak of the coronavirus will no doubt have far-reaching effects on all aspects of our lives, including our driving behaviors. As a result, auto insurers may likely see short- and long-term effects in their claims experience.

In the short term, the auto insurance industry will likely see a sudden and sharp decline in the volume of reported claims due to social distancing and stay-at-home mandates. Whether or not the effects of social distancing will be felt over a prolonged period will be dependent on how quickly the economy recovers once day-to-day life returns to normal. A sluggish economy could have lasting long-term effects on auto claims frequencies.

In this article, Milliman’s Paul Anderson, Eric Krafcheck, and Katherine Pipkorn discuss what auto insurers may be facing in the weeks and months ahead.

Support documentation for predictive modeling

Insurance companies have many responsibilities involving the submission and approval of rate filings, including providing support for any predictive models used to develop rates. Determining the level of predictive modeling support required can be complicated because it varies widely by state.

In this Insight article, Milliman’s Eric Krafcheck discusses the types of predictive modeling support that companies should include in a rate filing to minimize the objections and amount of time needed to receive a regulator’s approval.

Here is an excerpt:

While there are a variety of predictive modeling support documents that a filer can choose to include in a rate filing, some are more standard than others. At the very least, in addition to the indicated rating factors, an actuarial memo should be included with the filing that describes the data used, the adjustments that were made to the data, and the overall modeling process, including a description of the model validation techniques. Additionally, the memo should include a discussion of the predictive modeling method used and give any necessary specifications of the models. For instance, if a generalized linear model (GLM) was used, include model specifications such as the target variable being modeled (e.g., pure premium, loss ratio, frequency, severity, etc.), the error distribution used (e.g., Poisson, Tweedie, etc.), the link function used, and the predictor variables included in the final model. As always, if you are an actuary, consult the Actuarial Standard of Practice No. 41, Actuarial Communications, for other items that you should consider adding to the memo.

Other types of support are useful to have on hand but might not be necessary to provide in every state. For instance, some states require the submission of various goodness-of-fit measures and other model validation statistics whereas other states may not. If you do not want to provide this information in every state unless necessary, you should at least have the information readily available in an exhibit format. This will save a lot of time down the road if goodness-of-fit information is requested in response to a filing.

Predictive analytics in MPL pricing: Finding opportunity in challenges

The medical professional liability (MPL) industry has been slow to adopt predictive analytics in its rate-making process. This article authored by Milliman consultant Eric Krafcheck identifies challenges that may deter MPL carriers from building predictive models to price policies and offers solutions to these challenges.

Here is an excerpt:

In addition to the lack of available data, MPL writers face challenges related to immature and undeveloped loss data. Because MPL claim amounts can drastically change over time, it is exceptionally difficult to estimate ultimate claim settlement costs, especially when a claim is newly opened. The modeler should be aware that loss development techniques that may work for personal auto and homeowners may not be appropriate when applied to MPL data.

Additionally, traditional loss development methods used by reserving actuaries rely on the principle that a group of claims in aggregate will develop the way claims historically have developed in the past. But a predictive model is built based on data at the individual risk level, so losses must be developed at the individual claim level. What is the most appropriate way to handle this when the claims department is more confident in its estimate of the case reserves for some claims but less certain in its estimates of others? Should the modeler explicitly account for this? It is important for the modeler to research and understand the company’s reserving practices—how case reserves are established, how the claim settlement process differs by claim type, etc.—in order to apply the most appropriate assumptions when developing claim costs.

A further complicating issue is the treatment of incurred but not reported (IBNR) claims. In contrast to personal auto and homeowners claims, MPL claims may not be reported until well after the policy expires. This is especially true for occurrence policies, where coverage is provided for claims that occur during the policy year, but also can affect claims-made policies (for instance, some insurers may initially record reported claims as “incidents,” which later may convert to claims once they meet the company’s claim definition)…

A potential remedy to this problem would be to exclude the most recent immature accident years from the analysis. However, it may take many years before some claims are reported. Therefore, even if the most recent years of data are removed from the data set, it is still important for the modeler to take into account the IBNR claims and adjust accordingly when developing ultimate claim costs.