Case 2: Detecting Early Trends with Lag Analytics
As organizations contend with the current pandemic and its operational, financial, and risk-related impacts, they will need to address new methods of utilizing analytics in their decision-making process. To help them achieve this, SIGMA has started a series of blog posts highlighting possible issues and providing thoughts and suggestions on ways to handle them. The first case study focused on loss projection considerations for companies with closed or limited operations, but today, our topic revolves around monitoring emerging data and relaying the insights gained into loss projection or accrual adjustments.
The insurance industry is overwhelmed with ideas on how the pandemic’s impact might play out, and while some underlying assumptions may differ, one point has reached consensus: pandemic-related data is still very immature. Immature data hampers analytic methods that rely on consistent, credible data, but it doesn’t mean analytics can’t still provide valuable insights. As we will see, understanding early trends in claims data can help organizations stay on top of their risk-related responsibilities. The remainder of our article will focus on workers compensation, but several concepts can easily apply to other coverages as well.
Claim Lag Definitions
First, let’s briefly explore the concept of “claim lag.” Many types of claim lag can exist, but the following are some of the most commonly examined:
1. Report Lag – the difference between a claim’s occurrence date and the report date.
2. Closure Lag – the difference between a claim’s report date and the close or settlement date.
3. Treatment Lag – two potential definitions are:
a. The difference between a claim’s report date and the first treatment date.
b. The difference between the actual treatment date and the date treatment was deemed necessary.
Regardless of the lag type being analyzed, the most important aspect of a claim lag analysis is consistency. As long as data fields can be compared credibly to historical data, the insights provided will be similarly credible. Once a consistent basis of data is established, we can dig deeper into coverage-specific issues. In workers compensation, for example, workplace injury claims may be reported quickly, but repetitive-use claims or latent disease claims may have much longer “report lag.” Similar types of claim lags may help in the early analysis of data in a rapidly changing environment.
Creating a Comparison
To create a lag analysis based on emerging data, it may be necessary to create smaller accident periods to compare with historical information. Many companies track their workers compensation experience on an annual accident period basis matching policy inception. However, it might be helpful to first compare data from the 3/1/20-5/31/20 period or the 4/1/20-6/30/20 period to a corresponding accident quarter at the same maturity. As an example, if the 3/1/20-5/31/20 period is being used with data evaluated as of 6/30/20, the comparison should be to at least the following:
- The 3/1/19-5/31/19 period, evaluated as of 6/30/19
- The 3/1/18-5/31/18 period, evaluated as of 6/30/18
Analysis and Insights
Comparisons may be relatively simple to create, but without knowing potential trends and issues to identify, valuable information could prove elusive. As such, it’s important to work closely with the claim management team during this process to ensure their expertise is fully used. One potential trend lies in the lag averages. If these were to improve or decline significantly in the quarterly comparison, the next step would be to gain an understanding of the directional impact on loss projections and the impact’s potential magnitude. For example, a treatment lag increase could point toward an increase in the overall loss projection, as claims without early treatment intervention may be more costly. Another example could be the improvement of claim settlement lags, which would likely indicate a possible decrease in the overall loss projection. Historical data could also be reviewed to help determine the magnitude of savings related to accelerated claim closure.
During this time, it may also be useful to analyze previously unavailable claim information. Claims specifically coded as COVID-19 or other emerging pandemic risks could be examined in bulk and compared with historical information in the manner outlined above. Doing so would help drive a better understanding of the ways in which pandemic-related claims currently behave and what program-wide effects this may have in the future.
Conclusions
Organizations with appropriate capabilities may wish to track and analyze this information internally, but others may find themselves without the time, expertise, or resources to take on such a task. If that’s the case, prompt discussions with an actuary may be necessary. Many actuarial firms, including SIGMA, are able to track this information from loss runs and provide their own insights and analytics in their actuarial reporting. Irrespective of who gathers the data and performs the initial analytics, though, it’s important that this information reach the actuary, as it can play a significant role in decision-making regarding projected losses.
Similarly, this information and the underlying data could impact historical losses as well. Today’s discussions and suggestions are mostly concentrated on the future, but existing open claims (or claims with the potential to re-open) may see a great deal of variance in their medical or indemnity costs due to both treatment and closure lag. Monitoring information on both an emerging and historical basis makes an organization well-prepared to adjust as impacts from the pandemic become clear.
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