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>> LIFE
The Competitive Advantage of Using New Variables in Group Life Insurance (1)



Almost everyone in the group life business is talking about today's seemingly irrational pricing. This once-genteel sideline field is becoming a dangerous area in which staying profitable is increasingly questionable. Ed Martin, FSA and Karen Edgerton, ASA (Lincoln Re, USA) explain why.


One of our clients, a major group life insurer, told us about quoting group life for a prestigious national employer. Our client viewed the business as an attractive opportunity and made what the client termed an "aggressive but reasonable" bid, including a three-year rate guarantee. Our client won the business, but then lost money on the account for two years. Then another group insurance writer made the employer an offer that undercut our client's rates and also included a multi-year guarantee. In the end, our client lost the case.
"The competition is fierce," another client explained. "Plus, we're not sure the rates are based on reasonable mortality expectations. The market's changed so quickly that it's hard to know what's a realistic rate and if it's based on reasonable expectations."

What's Going on out There?

Historically, group life insurance has been viewed as a relatively uncomplicated adjunct to group medical coverage. It has provided a stable, predictable counterbalance to the cyclical swings of the health insurance market. Group insurers' actuaries and underwriters rightly focused their attention on medical insurance, which represented higher risk for their companies. Customers paid close attention to the much larger health premiums, with limited review of group life costs. In this environment, group life premiums could be set to yield comfortable returns, and efforts to develop a more sophisticated understanding of expected mortality costs were viewed as unnecessary. After all, if rates proved too aggressive, they could be raised at the next renewal.
In recent years, the group life market environment has changed significantly - becoming increasingly competitive, with severe pressure on rates and features. The steady returns upon which group life insurers relied have become less certain. Many factors have contributed to this shift, including:
* Poor recent results in the group medical segment have attracted interest in group life as a profit and growth source.
* Numerous companies have stopped writing medical business and increased their focus on group life. * Customers are increasingly unbundling their buying decisions, looking at group life apart from their medical coverage.
* Insurers face pressure for higher guarantee issue limits and longer rate guarantees.
* In many cases, credibility factors used in rating are significantly more aggressive than justified by statistical theory.
* Standard industry mortality tables are dated, and good recent industry mortality data is lacking. With these changing market dynamics, more and more group life writers are facing inadequate returns and even losses.

Pricing Methods in Today's Market

To understand the group life pricing issue, consider how the products are structured. Generally, group life and accidental death & disability (AD&D) coverages are annually renewable term policies. All employees actively at work are covered by basic employer-paid insurance, and retirees may also be included in a separate class. Additional coverage may be available in a supplemental or voluntary plan paid by employees. Other product features include dependent life coverage, waiver of premium, accelerated death benefits and AD&D riders.
For decades, group life writers have relied on industry data and sometimes their own experience to generate a base set of manual rates using three factors - age, gender and industry. Companies use varying approaches to adjust forward the most recent industry mortality data (from the 1985-1989 SOA Group Life Experience Studies) to the current pricing period, including analysis of their own experience and/or application of population mortality statistics. An employer's composite rate is calculated by combining actual experience on the group and the composite manual rates from the employer's own census data. Often credibility factors used to make adjustments for actual experience are significantly more aggressive than statistical theory suggests is appropriate (see square).

Mortality Analysis Explores Added Variables

To help our clients assess mortality assumptions, Lincoln Re undertook an extensive analysis of working population and group mortality experience data in early 2000. This year-long effort by a team of statisticians, actuaries, underwriters and medical directors was a natural extension of the work done in building the Lincoln Mortality System, a knowledge-based system for individual life insurance that helps establish baseline mortality assumptions with greater accuracy.
A multi-variable Cox regression model was used in the analysis of the working population data. A model is considered a good predictor if the overall p-values are less than 0.05, and individual variables are considered to be significant if the individual p-values are less than 0.05. As more statistically significant variables are introduced to the model, the chi-square values increase and the model is considered to be a better predictor of future mortality costs.
As illustrated below in the Model 1 results, we confirmed the effectiveness of the current practice of using age, gender and industry to model expected claims. The overall p-values are less than 0.05. Further, the individual variables are all considered statistically significant, since the p-values are all less than 0.05.
We then studied the predictive value of adding other variables that could be considered in pricing group life programs such as marital status, occupation, educational attainment and level of income. A number of otherwise useful variables (such as smoking/non-smoking) were not reviewed, as we chose to work within the constraints of the information employers generally have available.
In the Model 2 results, we added more variables -marital status, occupation, income and educational attainment level- to the equation. Note the increase in the overall chi-square numbers with the introduction of more statistically significant variables. Interestingly, we found that the "industry" variable becomes insignificant if the others are introduced.

(SEE ATTACHED TABLE)

More Variables = Smarter Pricing

In the end, we confirmed the effectiveness of using age, gender and industry to model expected claims, but found that incorporating additional variables produced greater precision when estimating future mortality cost. Variables that predict future experience include marital status, occupation, income and educational attainment. All four variables will not always be available, but even one additional item results in greater modeling accuracy.
To illustrate the impact on pricing, consider the two sample cases below, which demonstrate projected mortality costs using two rating approaches. In each case, the first rate was developed by recognizing age, gender and industry, and the second by adding income, occupation and education. For Employer N° 1, the added variables' greater precision resulted in a lower rate for both group life and AD&D coverages. For Employer N° 2, we found that the mortality was actually higher than the assumptions reflected in standard pricing based only on three variables.

(SEE ATTACHED TABLE)

The Bigger Picture

What are the implications for a group life writer? It's simple: Companies that can successfully incorporate additional variables into their group life pricing practices should gain a substantial competitive advantage. Knowledge of groups with a probability of better mortality than current systems predict offers the opportunity for more competitive rates and improved margins. Conversely, these companies should be equipped to make more informed decisions in situations where the profile is not as attractive.
For a glimpse of how changes in the group life market may occur, consider the individual life insurance market. For decades, profit growth was largely predictable and stable, with whole life the dominant product. In the 1970s and early 1980s, greater consumer awareness, the emergence of aggressive new companies focusing on term insurance, and a significant run-up in interest rates combined to change the nature of the business forever. Non-smoker rates emerged, then preferred, then multiple classes of preferred. Universal life was developed, then variable life and variable universal life. Rate competition, particularly in the term business, has not abated. Companies slow to adapt, like those who continued to sell undiffer-entiated products when competitors offered non-smoker discounts, saw their performance deteriorate.
In similar fashion, group life insurers that begin using more precise risk classification before their competitors should achieve a distinct competitive advantage. They will also begin to attract the better risks, leaving competitors with the remainder.

Why Change the Status Quo?

In a relatively stable, predictable market, the existing pricing metho-dology produces acceptable results. In the past, group life business margins have been large enough to absorb the inherent imprecision. However, today's increased competitive pressure, not only on rates, but also on guarantee issue amounts and rate guarantee durations, has eliminated much of the cushion that insurers enjoyed.
Assuredly, not all group life insurers will welcome the idea of asking clients for more data. Some will argue that additional data is not readily available. However, almost all employers include this information in their human resource databases, and it's often used for underwriting other products in the employers' benefits programs. For example, occupation and income are required to underwrite disability income insurance. As the leading insurers begin to ask for this information, the remaining companies will see the need to do so as well.
Others may believe that results are acceptable with the current process, so there's no need to make changes. Because group life is one-year coverage, they feel that any problems can be addressed at renewal. However, customers today are much less likely to readily accept rate increases, and the threat of competitive bids keeps such an approach a risky proposition at best.
These arguments miss the larger point: unmistakable signs of a more competitive, volatile group life insurance marketplace are popping up everywhere. Competition drives companies to seek differentiation, to refine and improve products, and to innovate; surely the group life market will take a similar path in this natural evolution.

Building an Edge Starts Now

While the group life marketplace becomes more competitive than ever, the companies most likely to emerge as leaders are identifying opportunities for differentiation now. Rather than simply bemoaning the situation or trying to stay in the game with ever more aggressive bids for business, the companies that choose a more strategic approach should see the profitable results of their foresight.
Our recent analysis demonstrated the added value of incorporating additional factors when estimating future mortality cost. The group life companies applying this information more effectively can gain competitive advantage. Those with the foresight to embrace this opportunity can be the winners in the new battlegrounds of the group life marketplace.

Credibility Fundamentals too Often Overlooked

In today's group life marketplace, it's not uncommon for insurers to give 100 percent credibility to employer groups based on as few as 20,000 or even 10,000 employee years of exposure. With the age and sex distribution of a typical employee population, the number of expected deaths for 10,000 years of exposure is in the range of 20.
What can we learn from statistical analysis about confidence limits and credibility?
According to statistical theory, credibility is a function of the number of claim events being measured (in this case deaths), not the number of exposures. The table below shows confidence limits based on number of observed deaths, using a Poisson distribution with a Byar's approximation. The limits are calculated to result in a 90 percent probability that the actual results will be within the range of the lower and upper limits. Estimated exposures were calculated assuming a 0.20 net monthly rate and rounded to the nearest hundred.

(SEE ATTACHED TABLE)

In other words, if your company's level of risk tolerance is such that you want 90 percent certainty that actual claims will be within 5 percent of expected (a traditional actuarial definition of full credibility), 1,000 observed deaths would be required. The estimated exposure necessary for this many deaths is more than 400,000 employee years. For 90 percent certainty that claims will not vary more than 10 percent from expected, you need 250 observed deaths or an approximate exposure of over 100,000 employee years.
It seems clear that many insurers are not basing their credibility factors on an appropriate level of exposure, choosing instead to rely more upon their experience. In so doing, however, they may be accepting a much higher level of risk than their companies are prepared to accept.
Given the increasing pressure on rates, group life insurers may want to reassess their use of credibility. Considering your company's risk tolerance, are you introducing the potential for more volatility than you really want? Sound actuarial analysis can help you determine an appropriate level that will allow both profitability and peace of mind.


Bibliography
1. N.E. Breslow and N.E. Day, Statistical Methods in Cancer Research Vol. II, 1987.
2. L.H. Longley-Cook, An Introduction to Credibility Theory, New York, Casualty Actuarial Society, 1962.
3. Valuation Technique Paper No.6 - Expected Mortality Experience for Individual Insurance, Canadian Institute of Actuaries, June 1989.
4. S.W. Philbrick, An Examination of Credibility Concepts, CAS Proceedings, Vol. LXVIII.


(1) Ed Martin is vice president of group life and Karen Edgerton is associate actuarial director at Lincoln Re. Published in Reinsurance Reporter, 2nd. Quarter 2001. We thank the publishers for their kind authorization to reprint this article.

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