Also, consider the years of credible historical experience available. Is the business line a new line or for a new target audience or territory? Or is it an established line with years of claims data behind it? Apply methods consistently across successive periods so that we may evaluate emerging trends.
For IBNR reserve estimation for a new line of business, the chain ladder approach may not be appropriate as sufficient data is not available to get credible results. An Expected Claims Technique may be used by the entity instead based on the experience of a similar line with sufficient claims history or the Bornhuetter-Ferguson approach may be used to credibility weigh results from both actual claims data and estimated expected claims data. In the case of UPR, for example, it may not always be appropriate to use the twenty fourths method commonly prescribed and used to calculate the reserve.
The underlying assumption for the twenty fourths method is that the majority of policies have tenors of one year with issue dates that are more or less uniformly spread out over the year.
The twenty fourths method would not be appropriate for policies covering seasonal products such as some forms of agricultural insurance with tenors of 6 months or less. Insurance regulations recommend a proportion of gross premium approach based on the ratio of the unexpired period of the policy and the total period, both measured to the nearest day in such instances.
Data constraints or data quality issues that relate to policy period dates may play a role in deciding whether to apply this exact alternate approach or an approximate method. Model results will vary based on the data and assumptions used for the estimation exercise such as:. Label each model developed with version and date.
The version ultimately implemented or used for determining results for regulatory submission should clearly be labeled FINAL. Ideally, each revision should contain a version history documenting the changes since the prior version, why and by whom. If peer review is conducted, then the date of review and name of the reviewer may also be recorded.
Documentation is a vital and continuous process. It should be of sufficient detail to allow for replicability, understanding, and review by an independent peer reviewer or auditor. The report formally documents and discloses for the intended audience, the process followed and the IBNR reserve results obtained.
The process must be of sufficient detail to allow another actuary to replicate the results. Highlight any recommendations and concerns. In general, the report should follow the standard order listed below. The audience of the report dictates which section receives greater focus and weight. Reports to senior management should focus on results, concerns, and recommendations. Brochure Watkins Ross Company Brochure.
Related Articles. No Results Found The page you requested could not be found. See More Blog Posts. To start your calculation of incurred but not reported reserves, or for additional questions regarding IBNR please contact Christian Veenstra President.
With an estimate of the total incurred claim cost, then the calculation of IBNR is as straightforward as subtracting the claims already reported from the total incurred claim costs, as shown in Figure 1. All the science and art of this method of IBNR estimation revolve around deriving good estimates for how complete the claims are for a given month. This is typically done by using the average incurred claim costs per member from a time period that is assumed to be percent complete or close to complete.
Multiplying this value by the total number of members in the pool gives us our final IBNR estimate. The projection method is a common approach for very recent months, and it relies on the assumption that the claims that have been reported to date in those recent months are not a good predictor of total incurred claims.
The completion factor method is more common in months where the claim payments are assumed to be more mature. Traditional methods like the previous example are technically predictive models, but they treat all individual risks the same. The benefit of such an approach is its simplicity and tractability. However, the underlying assumption that every person in the pool has the same historical payment pattern and propensity to have incurred and unreported claims seems unlikely.
An alternative to these traditional methods is to use predictive models at the member level. One of the strengths of predictive models is their ability to take high-dimensional data sets within which to segment and attribute risk more accurately, while appropriately handling any complex relationships between our prediction and the variables the model uses to make that prediction.
Instead of relying upon aggregate completion patterns, predictive models can estimate IBNR for each member directly. These member-level IBNR predictions can then be summed together into an aggregate reserve amount for an entire employer group or pool of business. Why use predictive analytics in this fashion? The biggest potential gain is in the accuracy of the estimate. IBNR can fluctuate wildly, particularly for small groups or payers with unstable payment patterns, and any additional pickup in predictive power can be helpful in estimation.
An additional drawback of traditional methods is that it can often be difficult to develop IBNR estimates for different subpopulations. For instance, suppose you work at a small insurance company and you are interested in reviewing the incurred claims by month, including IBNR, for individually insured members ages 55 to 64 in a particular geographic region. Using a traditional approach, there would be two options:.
Predictive analytics methods applied at the member level can solve this challenge by leveraging the credibility of the entire pool of members while accurately reflecting the risk characteristics embedded within any slice of the data. By producing estimates for each individual member, the estimates can be aggregated to any desired level. The added sophistication of member-level predictive models is not free.
Generally, estimating IBNR using aggregate methods can be done in a spreadsheet application after doing some data preprocessing in a language of your choice. The minimum data requirements for the completion factor method are simply a summary of claims paid for each combination of incurred month and reported month in the historical period known as a lag triangle.
Building predictive models at the member level is more demanding. Most IBNR estimation models can make reasonable estimates by following the basic developmental approach to estimation, but an amazingly IBNR models are set up in a way to clearly show how adequate your historical estimates have been using the most recent runout.
This includes restatement of prior liabilities and also analysis to show what type of provision for Adverse Deviation i. Even with very limited effort it is possible to develop updates of PAD requirements each month based upon actual claim payment history using statistical analysis. In addition there are many other data elements that could be included in a best in class IBNR tool including: special adjustment for Large or shock Claims, Utilization rates, Seasonality analysis, and Loss Ratio analysis.
There are many traps that actuaries fall into when doing IBNR. One of the main traps that we have seen many times is that actuaries try to take simple math and make it more complex.
As talked about earlier in this article the basic developmental method for calculation IBNR is not very complex and has been around for a long time. Over time many actuaries have taken the basic math and tried to expand on it in order to make it more complex. It might be adding a complex multivariate regression or simulating obscure assumptions, but most of the time these findings are not presented in the output and have very little effect on the outcomes.
Medical IBNR does not require sophisticated mathematics, but rather good actuarial judgement. Many times IBNR estimates that cannot be explained very simply to a stakeholder are not worth the sophistication used in refining the estimation process. These may be the result of past estimates that they or other actuaries made, or it may be from expectations that may have been communicated by other interested parties. Each month the IBNR estimate needs to be calculated independently without any predetermined bias.
It is hard to completely eliminate bias, but estimates need to reflect the data used to make the estimate without bias. With that being said, monitoring previous estimates needs to be part of any best in class IBNR process. If you do not know how adequate or inadequate past estimates have been, how do you plan to be able to make more reasonable estimates in the future.
Seasonality is about as important to IBNR estimation as any type of metric. It is my opinion that it is close to impossible to set IBNR estimates without reviewing the seasonality. Seasonality is very present in medical claims and should be reviewed as to not overstate or understate IBNR liabilities. There are no known perfect IBNR models, and there is no perfect IBNR estimation process that can be used without human intervention or actuarial judgement. Complete and extensive monitoring reports are key to the iterative process of reviewing and refining IBNR estimates.
We once had a client that calculated their IBNR liabilities by making individual estimates at over different splits cells. At this organization they had a team of 10 plus people spending many days calculating all of the IBNR estimates.
In one of our meetings we asked them if they had every thought of doing an allocation instead of doing individual estimates. So what level of detail is right? In order to calculate a reasonable estimate there needs to be a large enough number of people in each cell to reduce the volatility and create more stable patterns.
Doing IBNR on 10 people holds no purpose and it is my professional opinion that making estimates on a population less than people is often too noisy to make reasonably consistent estimates.
We recommended that number being between 12 and 20, but was believed that the 45 could still be done while maintaining actuarially sound principles.
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