SCI CGI – Certification in General Insurance (BCP, PGI & ComGI) Exam Set Two

These criteria may be used as a starting point in the measure of exposure. The most important of the three is the proportion of the measure to the expected losses. When the measure is objective and easy to obtain, it can be evaluated reliably.

To arrive at the average point of occurrence of the renewal policy, the full incurred amount of all claims is needed to be revalued by using the appropriate inflation index. Using the full incurred amount is essential to making an informed guess as to the value of a claim at a point in time.

The modelling process starts with an analyst’s experience and prior knowledge. With an analyst’s professional opinion, models that are better suited for a specific client or data set are chosen. As a result, experience and prior knowledge save time on relying solely on the available data. Its input increases the efficiency and effectiveness of the model due to the real regularities present in the data.

The correct order is individual loss data, exposure data, portfolio/market information, then cover data. In frequency and severity modeling, exposure data and portfolio information are used for several of its steps.

Exposure rating is unnecessary in personal lines insurance. Loss is experienced normally which makes the use of an exposure rating inappropriate. On the other hand, exposure ratings are useful when the experience is barely sufficient and diversified.

One of the disadvantages of the Gaussian or parametric approximations is that it has limited flexibility. It is because when aggregate deductibles are present and when losses to a high layer need to be calculated. Also one of its main features is that its implementation is still trivial.

Translated Gamma approximations is another approximate method that is considered to be similar to another method called Gaussian approximation. This method is used to model the aggregate loss distribution as a shifted gamma distribution. One of the main advantages of the use of this method with respect to the Gaussian approximation is that it is not symmetric but the other problems, such as the difficulties in modeling the tail and quantifying the errors, remain.

In the actuarial practice, it is important to keep in mind that there is a difference between the pricing framework and pricing decisions. The difference between the two is that the framework of pricing is set in the context of the company’s strategic plan which would include parameters designed to promote coherent and consistent decision making while a pricing decision is made after the framework of pricing is set in place and then monitored to track variance to plan.

The portfolio composition, which can also be called a ‘business mix’, can be defined as the percentage of policyholders that fall into each of the categories. Changes in the portfolio composition are needed to be tracked as they will result in changes in the risk profile of the portfolio and ultimately in the profitability of the portfolio.

The monitoring system should have the following characteristics for it to be considered as a good monitoring system. These characteristics include the conciseness and relevance of the output to the strategic goals of the insurer, should also be accurate both in terms of data and results, it should be simple or easy to use, it should be well documented for references and evidence purposes, and it should be able to produce an analysis quickly from data as-at date to results.

The performance of the frequency analysis in non-proportional reinsurance is just the same way as for direct insurance. However, several differences are also present between the two such as in non-proportional reinsurance, the focus is the number of claims above the reporting threshold while vice versa to direct insurance. Another difference between the two is that the treatment of exposure in non-proportional reinsurance is more complicated than direct insurance, especially when original premiums are involved.

It is important to take into mind the possibility of the occurrence of systematic errors. For organized checking of error, it is ideal that The insurer has the complete history of all reserve estimates and records of how they changed over time so that it can be used as a reference and a basis for checking of errors. Normally, large losses are reviewed at regular intervals and the reserve is typically updated if any new facts emerge while in the case of small losses, they will be given a temporary amount of recording to some company protocols and then, it will be updated the moment it is already settled.

The severity analysis has the objective regarding the production of severity model for the loss amounts. A severity model is not only essential to calculate, in combination with a frequency model, the distribution of aggregate losses but it will also help us to answer questions regarding the distribution of individual loss amounts.

There are advantages and disadvantages to using the method of ignoring the IBNER. The advantages of using this method include the simplicity of its use wherein it only requires a snapshot of the loss run at a given point in time, so there is no need for the full historical development of claims. It would work just fine if there is no occurrence of systematic errors and it is also the only option available when there is not enough loss data to produce a credible statistical analysis of IBNER. Its disadvantage is that if there are systematic errors, the result will be inevitably biased.

Just like any theory, the extreme value theory or Pickand-Balkena-de Haan theorem also has its difficulties such as its difficulty in identifying the extreme value region. The key difficulty in using this theory is that understanding when μ is large enough that it can already be applied, that is finding the so-called ‘extreme value region’. However, it may be difficult to identify this region, but there are many different ways in finding this region.

Based on the studies learned, it is important to incorporate the experience and theoretical reasons in the selection of the severity model rather than scatter-gunning for a model that fits the data well. The modeling of large losses provided reasons why choosing a particular model can be quite compelling. One of the reasons is that in modeling large losses, one needs a threshold distribution, specifically, it is the distribution with values above a certain positive threshold. The second reason is that we would want a severity distribution whose density decreases with size.

The adjustment coefficient depends on the other parameters in the risk model such as in the situation where it depends on the relative safety loading wherein If the parameter 0 increases, therefore it can be interpreted in terms of the portfolio being “safer” so the adjustment coefficient R is expected to be larger. The adjustment coefficient is the number R appearing in the famous Lundberg upper bound wherein a compound Poisson risk process with initial capital u ≥ 0, the ruin probability, ψ(u), is bounded by e−Ru. By changing the safety loading or the distribution of the individual claims that are involved in its definition, the value of R, and with it, the ruin probability can be adjusted.

The claims data in the general insurance includes several fields but it is important to take note that there are times when the practitioners refer to ‘loss’ and ‘claim’ in two different things. According to the practitioners, a loss refers to the loss of the insured while a claim refers to the amount the insured claims based on the policy that the insured purchased. However, in normal circumstances, the two terms are often used interchangeably. There are ideal situations where one can obtain a full list of movements for all claims. However, we are often asked to price a policy simply based on a snapshot of the claims situation overall years under examination at one point in time, rather than having the full history for each claim.

The basket method is a method of choosing claims that have similar effects in its outcome and see the difference in how those claims with similar effects would be priced from one year to another. This method only relies on the selected samples as its basis so the data that could be collected would be small concerning the data sets used in statistical methods but it would not lessen the reliability of the result. It has different components that could be broken down into pieces but the problem is that it would have difficulty in finding claims that have similar effects which are the needed data to be chosen in a basket method.

Just like in any other risk model, the classical risk models also have their assumptions of data which includes the assumption that the claim sizes X1, X2, … are all positive random variables with distribution function Fx and a finite mean. It also has an assumption that the claims arrive in a Poisson process and that the claims sizes X1, X2, … are all independent of the claim-arrival process. Other assumptions include the premium incomes accrues linearly in time at rate c (>0), so that by time t, the total amount of premiums received is ct and that at time t =0, the insurance company has non-negative initial capital.