How Poor Actuarial Practices result in Multi-Million dollar losses for Life Settlement Investors

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1 How Poor Actuarial Practices result in Multi-Million dollar losses for Life Settlement Investors Life Expectancy Providers are a key component of the life settlement process. These providers offer a life expectancy on a given individual who is looking to sell his life insurance on the secondary market to eager life settlement investors. For unsophisticated life settlement investors, unequivocal trust is placed in these life expectancy estimates from life expectancy providers. A short life expectancy can create the expectation of a high return on investment while a long life expectancy will do the opposite. But how accurate are these life expectancy reports and what are the chances these life expectancy reports are off? More importantly, who should be held accountable when investors lose millions of dollars by investing on the basis of one set of assumptions only to learn these assumptions were wrong? In late 2008 some of the life expectancy providers revised their mortality assumptions. These revised assumptions essentially lengthened their life expectancies on individuals of a given age in some cases by upwards of 35% i. As a result, life settlement investors who based the market valuation of their investment almost exclusively on these life expectancies saw a substantial devaluation of their portfolios. The Life Expectancy providers who made dramatic changes in 2008 were quick to distance themselves from these drastically different life expectancy results and placed full responsibility on the updated Society of Actuaries 2008 VBT Table which they used as the basis in determining their own proprietary life expectancies.

2 But who was actually to blame for these seemingly drastic changes in assumptions? The SOA 2008 VBT Table did in fact have smaller qxs and longer life expectancies than the previous 2001 VBT Table to reflect increased longevity and updated experience. But these increases primarily affected younger aged insureds. In fact for older attained ages (85+), the new 2008 VBT actually increased the qx s as the table blended insured mortality with general population mortality. Life Expectancies using the new 2008 VBT for insureds age 70 and above which make up the bulk of the life settlement market increased by less than 9%. For insureds age 80 and above, the increase was less than 4%. This is a stark contrast to the 25% to 35% changes in life expectancies reported by some LE providers in the life settlement space. Poor Life Settlement Underwriting Methodology In order to truly understand what caused such drastic changes in these LE reports it s important to examine the underwriting methodology employed. An LE provider determines the LE of an insured by assigning debits for various health conditions plaguing the insured. For example an insured might be assigned a base of 100 debits and then an additional 50 debits for having high cholesterol and 250 debits for having had a recent heart attack. These debits are then aggregated to arrive at a cumulative rating of 400%. The conditional probability of dying in any one year, or q x, is then multiplied by 400%. As can be imagined, applying successive multiples like this exponentially decreases survival probabilities and distorts the shape of the underlying curve of the mortality table. It also significantly amplifies model risk and explains the disparity between relatively small LE differences between the underlying 2001 and 2008 SOA VBT tables and the relatively large LE differences between the LE providers old LE projections versus their new ones.

3 Table 1: Life Expectancy comparisons between the two most recent SOA VBT Tables at 100% (Base Table) and 400% s Life Expectancies (years) assuming 100% (Base Table) Issue Age 2001VBT 2008VBT Life Expectancies (years) assuming 400% % Change 2001VBT 2008VBT % Change % % % % % % Table 1: Small LE lengthening in the base table is amplified and results in a greater percent change between the two tables when overly-aggressive rating multiples are applied. This is particular notable in long LE cases (age 60) and short-le cases (age 80) due to long duration and high initial q x s respectively. Since the 2008 VBT actually increased q x s for older attained ages (age 85+), middle-length LE s (age 70) are somewhat buffered against drastic LE lengthening from the use of multiples. Effect of High s on the mortality curve Multiplying each qx by a rating multiple has a compound and exponential effect that severely distorts the underlying shape of the mortality curve that makes the risk of the investment appear overly attractive to life settlement investors while not properly accounting for longevity/model risk. Table 2: Probability of living to age 90 using different Multiples on 2008 VBT Issue Age 100% (Base Table) 200% 400% % 10.94% 0.77% % 16.22% 1.83% % 30.54% 7.17% Table 2: Survival assumptions to age 90 are drastically reduced by using high-multiples making longevity risk appear almost negligible as it assumes the level of impairment is permanent and exponential over time

4 High s applied to an underlying mortality table make longevity risk appear almost negligible. When investors with finance experience, but almost no actuarial experience, model the resulting expecting cash-flows from these distorted survival curves the investment looks almost risk-free. But what investors fail to account for is model risk: what if these assumptions/methodology are wrong? Using this approach assumes that the level of impairment at day one is permanent and exponential. But this clearly incorrect. Someone may have a high risk cancer today, but if you price a policy assuming that their impairment is a permanent 800%, it severely distorts the risk and it fails to account for the actual probability of the cancer entering remission in a few years and the individual living a normal life and having only a 100% mortality rating thereafter. This methodology also fails to account for the fact that the underlying 2008 VBT and its select and ultimate mortality tables were constructed primarily for the purpose of establishing life insurance reserves for a younger population (35-65 issue ages, relatively good health) that was interested in buying a life insurance policy and therefore does not accurately describe the all-together different population that is interested in selling their life insurance policy (75+, impaired health). Actual-to-Expected Results Life insurance companies evaluate the strength of their own underwriting with the use of Actual-to- Expected (A/E) analysis. They use mortality assumptions to determine expected deaths for its insured population and then evaluate the strength of these expectations against the actual deaths at the end of the year. Due to decades of underwriting expertise, most life insurance companies can purport A/E ratios in excess of 90%. Life settlement providers, who due to the relative novelty of the life settlement industry have significantly less experience, also purport A/E ratios in excess of 90% to gain credibility with investors. However, the means which some these providers use to determine these A/E ratios is just as suspect as their methodology in determining their LEs.

5 Actual deaths in a year is a statistic that can t be fudged; either an insured died in a year or they didn t. But what can be manipulated are expected deaths in a year. You would think that the expected deaths used to determine a life expectancy provider s Actual to Expected ratio would be based on the actual LE estimates it gave to its clients. However, in the face of A/E ratios based on actual/historical data that are too low, some life settlement providers have adopted the practice of just lowering their expected deaths, ostensibly to reflect current methodologies and mortality tables, with the convenient benefit of making their adjusted A/E ratios higher and closer to 100%. Providers justify this behavior by stating that its understanding of future mortality has improved and it s more appropriate to measure the actual data against their improved i.e. lower expectations of future deaths. But discounting historical results in favor of better expectations of future mortality does no service to investors looking to understand the risk of their investment. In essence this practice provides investors one LE at inception only to tell those same investors years later: You know those LEs that you used to base your decision to invest millions of dollars? We improved our practices. They should have been 25% to 35% longer than what we told you previously. Sorry. But on the upside, by decreasing our previous expectations of mortality to match our actual experience our A/E ratios are now 98%. Increasing LE estimates not only leads to delayed receipt of death benefit payments, but also unanticipated premium payments required to keep the policy in force which increase as the insured gets older. As a result, small changes in LEs can result in significant decreases in market valuations.

6 Table 2: Changes in Market Valuation due to 10% LE lengthening at a 15% Expected Return Priced Valuation 3 year Priced LE (2125% ) $3,144,008 7 year priced LE (511% ) $1,572, year priced LE (194% ) $629,963 Market Valuation at 10% longer LE Change in Valuation $2,995,887 (3.3 yr LE) -4.71% $1,371,342 (7.7 yr LE) % $443,216 (12.1 yr LE) % Table 3: The above table shows the impact on the market valuation of a hypothetical policy for a 75 year old male NS at various priced s/les that end up being being assigned 10% longer LEs after purchase at a 15% expected rate of return. The table shows that longer LE policies suffer more significant drops in market valuation from LE lengthening. However, note that policy s with shorter LE s/higher ratings are more likely to be wrong due to model risk described previously that is associated with the over-aggressive use of high debits. Conclusion Several life settlement investment firms, who often have no actuarial or insurance expertise, have often found themselves in dire straits because of these poor and unregulated practices. Many firms found themselves having to pay additional premiums on policies several years after the overly-aggressive Life Expectancy that the Life Settlement provider had indicated. To compound matters further, due to the insureds living longer than initially anticipated the death benefits these investors expected to receive to help offset future premium costs never materialized. Due to an inability to address the liquidity issues that arise from understanding complex mortality and insurance dependent cash-flows, many life settlement firms have either failed completely (eg.hm Ruby Fund ii, SageCrest iii ), been unable to cover premium calls (eg. Lifemark iv ), or been forced to withdraw/significantly reduce their investment and/or acknowledge millions of dollars in losses on their portfolios versus previous expectations (eg AIG v, Assured Fund vi,etc).

7 Until market and regulatory forces step in to demand that those in the life settlement industry are held accountable for their actual results, providers will be increasingly incentivized to constantly revise and lower their mortality expectations in order to artificially create high A/E ratios. Allowing this to happen creates a false sense of security in the risk of the investment and allows uninformed players and participants to extol the values of an uncorrelated asset class with high returns while passing on the risk to unsuspecting end investors who expected their investment to earn anywhere between 15-22% but actually end up earning much less if anything at all. And if you re an investor, that s the Actual-to- Expected ratio that should matter the most. Rajiv Rebello is a Principal of Colva Insurance Services, which provides tailored actuarial and policy services to life settlement investors looking to maximize and protect their return through proper understanding of life settlement risks. He is a Fellow of the Society of Actuaries (FSA), a Chartered Enterprise Risk Analyst (CERA) and a Member of the American Academy of Actuaries (MAAA). He can be contacted at rajiv.rebello@colvaservices.com. i Life Policy Group. Market Rocked as 21 st Services changes mortality tables. 10 Oct Available at: ii Deal Flow Media. HM Ruby Portfolio with $1.36B in Face Amount on Block. 13 Apr Available at Block&id=nlrragfrmidjaob iii Deal Flow Media. Fortress Buys SageCrest Portfolio for $35M in Bankruptcy Auction. 09 May Available at Auction&id=lispasfrxxewpmk iv Steger, Alex. Lifemark bondholders set for showdown talks. New Model Advisor. 19 Oct Available at v Buhayar, Noah. Benmosche May Limit AIG Death-Benefit Wagers after $185 Million impairment. Bloomberg. 05 Aug Available at vi Risk Market News. Assured Fund Resets Mortality, cuts NAV. 22 Feb Available at: