Dynamic, Not Static, Projections Serve The Client Best

However, to assess it properly, we need to distill the results reflected in the curve into a few understandable numbers. And, we need to compare those numbers with those of other investment strategies.

Here are some figures that I find most helpful in doing just that. To compare the risks, show the:

1. Average result: The results added up and divided by number of scenarios.

2. Probability of failure: This is the chance that Dannys income is below his target.

3. Average amount of shortfall: Determine this, if his income (in item 2) does come up short.

4. Poor performance: Whats the one-in-100 chance the VA performance will yield income at a rock bottom level?

5. Absolute worst case: What is the least amount of income the client can get in the absolute worst case?

In Dannys case, the VA program that invests 100% in equities produces an average $3,578 per month for Danny. This sounds great.

However, theres a 17% chance hell fall short, and if so, the average amount short is $176. Danny has one chance in a 100 that his income will only be $248 per month, a disaster. And theoretically, the VA could produce zero income.

Summary: Great on average, but high risk.

Lets try this approach on the second VA program I mentioned, the one that puts 40% into a fixed account and 60% into equities.

In this scenario, the average income drops to $2,890, a big sacrifice. The failure percent improves to 12%, and the shortfall on average is only $90. Thats good. The one-in-100 figure, however, isnt great–$1,109–and the worst case is $743 (the fixed account comes through but the variable account is wallpaper).

Now, lets move on to the final VA program, the one that includes the GLB. Here, the average result is intermediate between the other two programs, producing $3,263 per month. The failure rate goes up to 21%, with an average shortfall of $195. So far, not exciting. But the worst cases come up with $1,534 per month, much better than the alternatives. This is the amount of GLB. Further math shows the GLB might pay off about 24% of the time.

This leaves an interesting decision for Danny, involving risk tolerance. But now it will be an informed opinion on relative merits.

One decision might be to devote more funds to his purpose, or lower his expectations or target for income.

With different parameters, one program might come to the front as superior. If Dannys minimum target were $1,500 per month, for example, the GLB would shine, always producing his target or better, while achieving an average result fairly close to the VA with 100% in equities. Furthermore, GLBs will generally outperform mixed investment strategies in reducing “downside” risks. We also learn that the long-term variations in equity performance can be staggering!

Again, I agree that communicating risk characteristics of financial programs is not easy. However, the above shows it can be one of the most essential elements of good advice in this age of variable products. Therefore, its a good idea for producers to look for allocation software that helps them run such computations.

G. Thomas Mitchell is president of Aurora Consulting Inc., an actuarial and insurance consulting firm in St. Louis, Mo. He can be contacted at [email protected].

Reproduced from National Underwriter Life & Health/Financial Services Edition, August 20, 2001. Copyright 2001 by The National Underwriter Company in the serial publication. All rights reserved.Copyright in this article as an independent work may be held by the author.

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