Is the Fechtel Method a Better Way to Evaluate Cash Value Life Insurance Policies? Hot

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- Current life insurance illustrations are an inadequate tool for understanding and comparing cash value life insurance policies.

- State insurance regulators have been ineffective in providing useful information to consumers.

- When you review the past performance of a policy, it is useful to determine the sources of variance between illustrated and actual values.

- A comparison of term and cash value life insurance should take into account the product costs as well as the income tax treatment.


A few observations:

- Fechtel does what every buyer in the life settlement industry does: he reverse-engineers illustrations to break out the charges and credits, although with less precision than the life settlement industry would accept. Life settlement buyers deconstruct illustrations to estimate the minimum premiums that must be paid to keep a policy in force. Fechtel does it to create “informative illustrations.”

One person’s informative illustration is another person’s waste of time. Two of the eight columns in his illustration show guaranteed amounts. In an earlier post (“Life Insurance Guaranteed Values Are a Big Fat Idiot”), I explained the near-zero information content of guaranteed values.

The three annual cost columns in the illustration will take some effort for advisors to understand and explain to clients, with no advantage over a rate-of-return perspective that fits naturally with how advisors approach other investments.

And how can an informative illustration fail to indicate whether the numbers in each column are beginning-of-year or end-of-year?

- An article that claims to correct the shortcomings of a century of work on life insurance cost disclosure should contain more than a skimpy literature review. Fechtel mentions four contributors to this literature but leaves out many others. And the limited review contains a factual error: Fechtel says that M. Albert Linton developed his method in the 1960s, but in fact he began presenting it in the 1920s (see, for example, “The Material Return from Life Insurance as an Investment,”Life Association News, November 1927).

[Historical digression: Miles Menander Dawson, a consulting actuary for the New York State legislature’s landmark investigation of the life insurance industry, presented a crude version of “buy term and invest the difference” in his 1905 book, The Business of Life Insurance. The rates of return in such comparisons are now called Linton yields. S.H. Nerlove (“The Investment Element in Life-Insurance Contracts”) and Linton debated methodology in the pages of the Journal of Business in 1928.]

- Fechtel claims that an insurer’s statutory financial statements contain useful information to understand a policy’s past performance (and possibly to predict future performance), but he offers no empirical evidence. He states: “..this policy’s actual financial performance, along with that of all the insurer’s other policies, can be reconciled with the insurer’s actual financial performance as reported in its annual statement filed with regulators. Admittedly, sufficiently precise reconciliations can be tediously challenging data collection and analysis projects, but, in contrast to some practitioners’ mistaken beliefs, they are hardly impossible.”

As one of those practitioners with mistaken beliefs, I would applaud any credible research on the correlation between companywide statutory accounting information and product-specific pricing factors. This is, in fact, a frontier of life insurance product due diligence.

Fechtel has published an impressively detailed analysis of an insurer’s financial statements on, so one part of this project is already done. Now someone just has to break into insurers’ home offices and steal the profit tests. As I explained in an earlier post about the Veralytic Report, profit tests are indispensable for understanding the risks that you are taking when you buy life insurance policies with nonguaranteed values.

- Fechtel is unjustifiably dismissive of options analysis. He ignores the most important option that consumers have: the option to wait. And he complains that “practitioners who advocate viewing cash value policies as packages of options…have then either failed to provide the costs of such bundled products or have erroneously confused analysis of an illustration for analysis of a policy.” I have found that options analysis leads to useful insights despite the difficulty of quantification. However, a recent example of research that might meet Fechtel’s high standards is  Nils Rüfenacht, Implicit Embedded Options in Life Insurance Contracts: A Market Consistent Valuation Framework, Physica-Verlag, 2012.

- Where is the evidence that Fechtel’s analytical approach leads to better outcomes for consumers? It is not enough to declare, as he does repeatedly, that his way of looking at life insurance improves consumer decision-making. Declarations are not evidence. He has been promoting his preferred disclosures for at least 19 years (see “Fairer Product Comparisons,” Best’s Review, February 1993), so he has had a lot of time to assemble a robust database of results showing the power of his method. Does it do nothing more than guide advisors to ask a few of the many questions that should be on any comprehensive checklist?

One unintended lesson from this article is that financial advisors have been badly served by their educational curriculum. Instead of a detailed explanation of how life insurance policies are actually priced, provided by actuaries who do the pricing, they have to settle for one superficial description after another.

9-22-2012 update

Regarding the relationship between statutory accounting information and policy performance, here’s a research paper that I overlooked: James M. Carson and Randy E. Dumm, “Insurance Company-Level Determinants of Life Insurance Product Performance,” Journal of Insurance Regulation, Vol. 18, No. 2 (Winter 1999). A related article also appeared in the September 2000 issue of the Journal of Financial Service Professionals.

The authors looked at data compiled by A.M. Best for 73 universal life policies issued in 1985 to 45-year-old male nonsmokers. The face amount was $100,000, and the annual premium was $1,500. They examined the relationship between actual policy performance, measured by 10-year cash surrender value, and selected company-level information, mostly based on insurers’ statutory annual statements. Their regression analysis showed that three company-level data items were significantly related to policy performance: lapse rate (statistically significant at the 0.01 level) and general expenses and investment yield (significant at the 0.10 level). Variables that showed no significant relationship included company size, organizational form (mutual vs. stock), A.M. Best’s financial strength rating, net gain as a percentage of total income, and change in product mix.

It makes sense that lapse rates should have the strongest relationship; that data item is for life insurance only, whereas other items are aggregates for all lines of business.

11-3-12 update

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