Review of pricing model of universal life for the new suggested product features changes.
Introduction
Blackmore Insurance Company has requested us to review its pricing model of Universal Life. In this memo, we are going to assess the current model used by Blackmore Insurance Company, provide the data needed in evaluating the features of the new product, give suggestions of the changes that can be done on the pricing model, recommend modifications to the process of sensitivity testing process and finally recommend on the operations of reviewing the experience as well as updating the pricing model in the forthcoming years.
Assessment of the current pricing model used by Blackmore Company
The present model is based on Microsoft excel software, where the computations of projections on product cash flows and other leading profitability indicators are made. We are much concerned with the strength and possibility of accuracy in the used model:-
- Excel application is useful in performing multiple computations but may not give accurate results with the pricing model function.
- In the excel application, the grouping policy is found in a separate tab. This makes it uneasy about expanding the number of policy groupings in the future.
- There is a high possibility of making errors when updating the changes in the product. The new changes may fail to be duplicated in all tabs.
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- The application lacks adequate control. For instance, it can be accidentally be altered easily, and there exist no signs to show whether the changes have occurred in any of the tabs. To avoid this kind of challenges, we advise the company to develop control mechanisms to the pricing model it is using, such as the use of password protection in each of its workbook. Before any changes, there is a need to ask for a user password and also track any changes using the tracking tool.
- There is a high chance that the computations in this model do not meet actuarial standards because this is an in-house pricing model.
- Nevertheless, the platform which was used in the developing the model, there exists several practices that indicate potential concerns; first, there exists some assumptions which have been over-simplified such as 50% assumption of the assets distribution in every investment, 6% long term company growth assumption of the assets that are used to support the investment accounts, The credit rates of the model do not take into account the deduction margins obtained from gross price received from the underlying assets and finally one pattern of funding is not suitable for this kind of model.
- The modeling of complete withdrawals is made through lapses.
- The modeling of ages in the current model is not enough. We, therefore, recommend consideration of examining each quinquennial age rather than decennial age.
- There exist other breakdowns beyond sex, smoking status, and age that needs to be considered in the model.
- The model fails to address possible risks in the market that can affect monthly charges to be deducted from the guaranteed option of interest with no adjustment in the market value.
- Through this model, policy owners can pay expense accounts using the guaranteed option of interest when the rates in the market are high.
- The present process of modeling for testing of sensitivity is not adequate.
We noted that the function used to compute profitability is suitable for this kind of business, and therefore, we do not propose any changes. However, it is recommended to evaluate other measures of profitability, such as the internal rate of return. The recommendations will increase the problems of funding patterns and ages concerns, which can lead to an increase in model size by six times that is from 20 -120 tabs. We recommend the company to apply third-party software which is used in performing pricing practices.
Data needed in evaluating the features of the new product and proposing further adjustments to the pricing model.
To appropriately model the new features proposed, there is a need to add more data. Data for a minimum guarantee product feature, free withdrawal guarantee product feature, performance bonus product feature, COI return product feature are required. We are going to discuss the data needed for each element separately.
- Minimum interest Guarantee product feature
To guarantee a “zero-cost guarantee,” we need the data below:
(i) Investment strategy details.
(ii) Yield projection information obtained from the area of investment for the assets
(iii) means and variances of historical returns for the given assets
(iv) credit rates policing documentation
(v) Items involved in investment spread data.
We also need to utilize forecasts of external economic data to support our analysis, which includes; future interest rate pattern forecasts, future financial growth estimates, economic indicator survey reports, and reports on consumer confidence.
- Free withdrawal guarantee product feature
To analyze risks that are related to this feature, we need to examine the previous product withdrawal experience and the conditions that have been put in place by other companies. This is intended to find whether there exists a relationship between withdrawal level and prevailing rates returns of market rates. The market assessment is needed to help us understand other same options existing in the markets as well as the conditions put around by other firms. This feature may result in multiple risks where low return rates may adversely affect product profitability. This can be compounded by the extra free amount of withdrawals, which have no surrender charges.
- Performance bonus product feature
There exist two conditions that generate this kind of bonus; additional funding and exceptional return rates. The data produced to price the minimum rate of interest guarantee can be utilized to evaluate the market increase profitability at some point shortly, which is enough to give this bonus. In this data, we will need to understand how clients of Blackmore find the policies and the circumstances that over –contributes to policy to bring a 25% rise in the value of the account.
- COI return product feature
This is a desirable feature because it guarantees the policyholder to have their COI back to them. The product assists in increases sales. The feature affects the following areas; rates of lapse, rates of mortality, earnings on investment, and reserves. Lapse rates are crucial in the pricing model assumptions. COI is returned when the policyholder lapses at the 9th contract anniversary.
Recommended changes on the pricing model
The new proposed features will not work effectively while using deterministic pricing model because of the following reasons:-
(i) The minimum rate of interest guarantee fails to come into effect when using the static rate of interest assumption. Thus, there exists no way one can evaluate the cost.
(ii) The provision of free withdrawal depends on real return rates. Similarly, the static rate of interest assumption gives no indication that means that this occurs, and thus it is wrong to assume that there are no costs.
(iii) When the return rates are incredibly high, the bonus of performance comes into effect.
The pricing model is recommended to use a stochastic model that can find out possible costs on each of the above models. We recommend keeping the modifications generic adequately to apply to either in the revised or current model.
First, it is recommended to develop a set of stochastic rate forecasts for every asset type that will be involved in the investment portfolio of Blackmore Insurance Company. A significant number of paths need to be implemented to help in refining the results; we suggest generating more than 1000 different ways. Also, the paths should be connected with the classes of assets, and for example, equity returns are expected to depend on the curve of current yield partly. For every path, profits should be produced from the model of pricing. The rates found in each path will determine invested assets rates and investment accounts rates credited. The suggested features of product need to be explicitly involved in the pricing model so that they can be upgraded with circumstances are made. To find out the features costs, the pricing model needs to operate with or with no elements in touch.
Next stochastic return rate paths should involve functions that reflect the behaviors of the policyholder. And this can be achieved through the inclusion of the following parameters; return as one of the options of investment becoming much attractive than others, deposits into the option of guarantee interest, increasing the lapses of the policyholder when there exists provision of free withdrawal, and market returns need to correlate with the funding levels. The suitable COI rider return needs to test the effect of changing the rates of lapses.
Recommended modifications to the process of sensitivity testing process
The present sensitive testing is not enough. The following changes are recommended to assist in improving the sensitivity analysis:
(i) Assumptions are changed by 5%. The level of adjustments needs to change as per the assumption provided. The adjusted premises need to correlate with the forecasted confidence or variability of the hypothesis. The modifications should be founded on statistical confidence (95% confidence level).
(ii) Some assumption affects profitability and displays variability. These assumptions are mix shits of business, pattern changes of premium, choice of investment, and rates of interest. The sensitivity should be tested on the premises as well.
(iii) The required adjustment range may change contrarily by duration, especially for the proposed new features of a product. The model required to be adjusted in such a way that it takes into account the period taking in such testing of sensitivity.
(iv) There should be regular sensitivity testing and the results provided as the backing information for profitability figures generated.
Recommendations on the processes of reviewing the experience as well as updating the pricing model
The newly introduced product features require increased monitoring of the company’s business to help in understanding the possible costs going forward. The available lapse and mortality experience monitoring need to be maintained. An extra parameter needs to be added to the process of lapse monitoring to detect the effect of free withdrawal provision. There is a need to study this experience where the guarantee is not working well. It has been learned that there exists no justification for the present lapse and mortality assumptions. If confirmed by the experience studies, then adequate documents needed to be availed. We also did not find any experience monitoring for levels of withdrawals, which may be contrary to lapses. The experience monitoring of lapse should be expounded to add data on partial free withdrawals that ensures that the policies are in place.
There is a need to expand the funding levels study. It is not enough to check on the annual data of one policy because the level of funding may change as per the duration of the policy. We recommend the development of model levels of funding, as proportions of the lowest premium, are computed and recorded by the policy’s period. The data needs to be examined to find particular patterns that may emerge as time goes by. Expanded analysis needs to be conducted on investment options choice within each policy. We suggest the implementation of a system that gives tracks the account allocation between options of investment. It is also recommended there are a frequent review of the pricing assumptions and also regular update when adjustments are needed. We recommend the following review cycle:-
(i) First year-mortality
(ii) Second-year- withdrawals and lapse
(iii) The third-year- investment options and funding levels.
Conclusion
In conclusion, provided the recommended product changes, we found that the pricing model of Blackmore Company to be not adequate. To improve the strength of the results, we suggested significant changes to the present pricing model or developed an externally-produced pricing model. Also, we recommended the introduction of stochastic modeling to suitably price the new product features. Stochastic return rates of paths can also be implemented to help in pricing. Behaviors of policy owners need to be included in the model. Experience monitoring requires to be expounded beyond lapse and mortality to examine levels of funding and investment choice option patterns. The new features of the product should include a different parameter for monitoring.