Understanding IBNR and Its Importance in Insurance Planning

IBNR stands for Incurred But Not Reported, which refers to claims that have been incurred by policyholders but have not been reported to the insurer yet. This can happen when policyholders don't immediately report their claims, often due to various reasons such as lack of awareness or delayed processing.

IBNR is a significant concern for insurance companies, as it can lead to underestimation of losses and affect their financial stability. According to the article, IBNR claims can account for a substantial portion of an insurer's total losses, sometimes up to 20%.

IBNR stands for Incurred But Not Reported, referring to the estimate of liability from claims that have taken place but have not yet been reported to an insurer.

Insurance carriers estimate IBNR by using a range of actuarial services to accurately represent this figure, which is then added to the actual realized losses.

There are two ways IBNR claims present themselves: untimely reporting of the original claim, and subsequent injuries from the original claim.

For example, if John slips and falls at work and tears his ACL, MCL, and meniscus, but hasn't reported it yet, it would fall under IBNR.

Insurance carriers must account for the IBNR exposure they carry on their books, and if the realized IBNR is less than predicted, they recognize favorable loss development and improve their profitability.

IBNR liabilities represent the amount of money insurance companies set aside to cover claims that have already occurred but have not yet been reported or fully settled.

Insurance companies must maintain sufficient reserves to meet all payments on past claims, even those they do not officially know about yet.

IBNR can be defined in many ways depending on when things are reported, paid, or processed, but each varies by time of measurement and what additional items are known and considered.

IBNR is critical for an insurer's financial stability and solvency, as large changes in IBNR can cause major changes to financial statements.

Accurately estimating IBNR is a balancing act that directly affects an insurer's financial health and regulatory compliance.

Underestimating IBNR can lead to inadequate reserving, potentially compromising an insurer's ability to fulfill its obligations to policyholders.

On the other hand, overestimating IBNR can unnecessarily tie up capital that could be otherwise invested or used for other business decisions.

IBNR plays a vital role in pricing and underwriting decisions, helping insurers adjust their pricing strategies to reflect the true cost of assuming certain risks.

By understanding the patterns and trends in IBNR, insurers can maintain competitive pricing while ensuring profitability and solvency.

IBNR estimation is essential for reinsurance negotiations and decisions, helping insurers and self-funded groups transfer part of their risk to reinsurers.

Effective IBNR estimation is a key element of risk management and strategic planning within both self-funded groups and insurance companies.

By providing an accurate picture of future liabilities, IBNR analysis helps plan sponsors identify potential areas of risk and implement strategies to mitigate them.

Calculating IBNR involves using various models to determine unpaid claim liabilities. The most often utilized method is a form of the claim triangle or lag method, which requires gathering claim information and considering plan, provider, or personnel changes that might impact claim processing.

To calculate IBNR estimates, most models need only two required elements: members and claims. However, including additional data elements like premium can provide useful information, such as real-time estimates of medical loss ratios.

A good IBNR model should be able to average multiple methods of completion factor averaging, including simple averages, smoothed averages, and harmonic averages. A weighted average of these methods can provide a more accurate estimate.

We use a form of the claim triangle or lag method to determine unpaid claim liabilities. This method involves analyzing historical payment patterns to detect irregularities that might suggest an adjustment is needed to the reserve.

At Watkins Ross, we consider various factors, including plan, provider, or personnel changes that might impact claim processing. Average monthly claim experience is calculated and compared with reserve estimates for reasonableness.

A margin is added to allow for unforeseen claim experience that actuarial approaches might have missed. This helps ensure that our IBNR calculations are as accurate as possible.

We review the results with you and, if necessary, with the audit teams once the results of the calculations and report are completed. This ensures that you have a clear understanding of the IBNR calculations and can make informed decisions.

External factors such as extreme weather events can delay processing, and we consider these factors in our calculations. If considered, these factors are disclosed in the IBNR report.

We gather claim information and use it to detect irregularities in payment patterns. This helps us identify potential issues that might impact the reserve.

Our IBNR report includes a description of the method used to determine the IBNR, the data and source of data, and reconciliation of prior year's IBNR calculations and actual run-out to the current year.

There are three frequent types of averaging used in IBNR models: Simple Averages, Smoothed Averages, and Harmonic Averages. Simple Averages can be 3-month, 6-month, or 12-month averages.

A Simple Average of 3-month completion factors might not accurately reflect the current trend, while a 12-month average might be too slow to adjust to changes in claim payment patterns.

Smoothed Averages of 6 of 8, or 10 of 12, can provide a more stable estimate, but may not capture sudden changes in claim payments.

Harmonic Averages can also be used, but might not be the best choice in all scenarios.

To get a more accurate estimate, you can take a weighted average of the various methods and determine an average completion factor. This allows you to average multiple methods and also opens up the opportunity to easily run scenarios of the methods.

Here are the three frequent types of averaging:

Smoothed Averages: 6 of 8, or 10 of 12

Harmonic Averages

To calculate IBNR estimates, you'll need just two required data elements: members and claims. These are the foundation of most IBNR models, and they're essential for establishing an estimate.

Claim triangles come in different shapes and sizes, but as long as your data has both members and claims, it doesn't matter how it's arranged. I prefer columns of individual paid months and rows of incurred months, but you can use whatever format works best for you.

Including premium in your IBNR model can provide valuable insights, especially with the passage of ACA and increased attention to loss ratios. This allows you to make real-time estimates of medical loss ratios, giving you an early warning sign of experience deterioration.

Estimating IBNR requires a delicate balance between complexity and simplicity. The basic developmental method for calculating IBNR is not complex and has been around for a long time.

Actuaries often try to add complexity to the estimation process, but this is not necessary and can even be counterproductive. Medical IBNR does not require sophisticated mathematics, but rather good actuarial judgment.

It's essential to avoid predetermined expectations of what the IBNR amount should be, as this can introduce bias into the estimation process. Each month's estimate should be calculated independently without preconceived notions.

Seasonality is a crucial factor in IBNR estimation, and ignoring it can lead to inaccurate estimates. Reviewing seasonality can help avoid overstatement or understatement of IBNR liabilities.

The IBNR model is not always right, and actuarial judgment plays a critical role in producing accurate estimates. It's essential to review and adjust estimates regularly, rather than relying solely on the model.

Calculating IBNR estimates at too fine a level of detail can be counterproductive, as it can lead to noisy and unstable patterns. A general rule of thumb is to aim for cells with at least 2,500 people to reduce volatility and create more stable patterns.

IBNR models should be used for more than just calculating IBNR, they should also serve as an early warning sign to what's going on in the company as a whole or in a specific line of business.

Loss Ratios have become very popular, especially with the passage of ACA, and can be estimated using an IBNR process by simply inputting premium, giving an up-to-date estimate of the basic medical loss ratio.

A good IBNR model should be viewed as often as looking at the IBNR itself, and claim seasonality is an output that should be reviewed regularly.

An IBNR model can be used for more than just calculating IBNR. It can serve as an early warning sign to identify potential issues in a company or specific line of business.

If estimated incurred claims are skyrocketing, it may be time to review pricing and benefit designs. This can help identify areas for improvement.

Loss Ratios can be estimated using an IBNR model, giving an up-to-date estimate of the basic medical loss ratio. This is particularly useful in the wake of the Affordable Care Act (ACA).

Claim seasonality is an output of a good IBNR model, and reviewing it regularly can be just as important as monitoring IBNR itself. Many companies spend a lot of time creating separate seasonality models, but a good IBNR model can already provide this information.

Utilization can significantly affect IBNR, so monitoring it is a good way to stay on top of business trends. This can involve tracking metrics like admits for inpatient claims or specialty scripts for RX claims.

Data Visualization is a game-changer for IBNR estimation. By looking at numbers and outcomes in a more visual way, we can gain a deeper understanding of the data.

Looking at graphs and visuals can be a more effective way to analyze data than just staring at raw numbers. The article highlights the importance of finding better ways to show data, rather than just focusing on complex equations and decimal points.

Graph-1, which shows seasonality by year, is a great example of how visualization can help identify patterns over time. By calibrating the data to the same starting month, we can easily see how trends change from year to year.

Manual overrides are a common part of IBNR estimation, but Graph-2 shows how quickly we can see if these estimates are drastically different from previous years. This helps us identify potential issues and make more informed decisions.

Graph-3, which displays monthly estimated incurred claims trends, is another powerful tool for analyzing data. By showing cyclical patterns and differences in 3, 6, and 12 month trends, we can gain a better understanding of what's driving the data.