Life Settlement Mortality Considerations and Their Effect on Portfolio Valuation

Transcription

1 Life Settlement Mortality Considerations and Their Effect on Portfolio Valuation Ed Mohoric, FSA, MAAA MILLIMAN Robert O. Kinney, M.D., FLMI PHOENIX LIFE SOLUTIONS March 1, 2008

2 Preface Life expectancy estimates may be the single most important factor in calculating the value of a life settlement policy or investment portfolio of settlement contracts. To determine life expectancy estimates fully, it is essential to understand the underlying mortality assumptions and underwriting processes used to develop them. This study attempts to evaluate these critical variables and draws on our professional experience in both the life insurance and life settlement fields to do that. In Section VI, we have analyzed a hypothetical portfolio of life settlements to show performance under alternative assumptions for experienced mortality. We believe there is no inherent bias in our discussion or analysis of the hypothetical portfolio; however, actual performance may vary considerably. The views expressed here reflect our analysis and assumptions and do not necessarily reflect the views of Milliman or Phoenix Life Solutions. Ed Mohoric, FSA, MAAA MILLIMAN Robert O. Kinney, M.D., FLMI PHOENIX LIFE SOLUTIONS March 1, 2008 ii XXXXXX XXXXXX

3 Table of Contents PREFACE Inside Front Cover I EXECUTIVE SUMMARY 2 II BACKGROUND ON LIFE SETTLEMENTS MORTALITY 3 Viaticals 3 Current approach to life expectancies 4 - Life expectancy (LE) and LE estimators 4 - Insurance underwriting methods 5 - Application to a mortality table 5 - Exceptions to the debit/credit methodology 5 LE estimation trend 6 Mortality assumption importance 6 III UNDERWRITING 7 Older age underwriting 7 Tentative diagnoses 7 Changes in risk over time 8 Medical advances 8 IV MORTALITY TABLES 9 The 2001 Valuation Basic Table (VBT) 9 Select and ultimate 10 Mortality improvement experience 12 V OTHER LIFE SETTLEMENT MORTALITY CONSIDERATIONS 14 Selectness of the life 14 Steepness of the curve 14 Mortality multiples vs. flat extras 15 Variability of life expectancies 16 VI MORTALITY ANALYSIS 17 Sample portfolio characteristics 17 Other assumptions 18 VII CONCLUSIONS 20 VIII ABOUT THE AUTHORS 21 1

4 I Executive Summary The value of a life settlement policy or the value of an investment portfolio comprising multiple settlements will often be determined by the provider s estimate of the policyholder s life expectancy. That is why a potential investor should start by understanding what the provider believes is the underlying mortality of that portfolio. In evaluating life expectancy estimates and analyzing potential investment pools, we have determined the following: > Life expectancy estimators generally use life insurance underwriting approaches in their analyses, but apply them to ages and load levels typically not encountered by life insurance companies, and then attempt to compensate for that in their analyses. > Usually, life expectancy estimates are expressed as mortality loads applied to a mortality table and then translated into life expectancies. These loads are usually expressed as mortality multiples. As such, appropriate understanding of the underlying mortality table both level and slope is important. > Understanding the select and ultimate nature of underwritten or medically reviewed mortality is important to determining the value of life settlements. For example, the 2001 VBT s 25-year select period may be too long for senior lives, which is why some life expectancy underwriters have adjusted the table to use a steeper slope. Newer insured experience seems to confirm this; however, little evidence exists that life settlement mortality actually shows a steeper slope than the 2001 VBT, and there are good reasons to argue that the slope should be flatter than the 2001 VBT. > For some impairments, flat extra adjustments an additive load to mortality rather than a multiplicative load or a blend of both should be considered. A move toward this would likely increase estimates of life expectancies but may more accurately reflect expected mortality. > Clearly, mortality variations that lengthen life expectancies can have a significant impact on the value of a portfolio. > Life expectancy estimates are points in time, based on current health conditions. Actual health status can change over time, resulting in worsened or improved mortality expectations. > Ultimate mortality on life settlements is not yet known because life settlements is a new and evolving industry. The 2001 Valuation Basic Table (VBT), sometimes with adjustments, is the most commonly used table as a basis for life expectancy estimates in the United States. However, recent underwriting experience of life insurers is significantly lower than that table s estimates. 2

5 II Background on Life Settlements Mortality Viaticals The roots of the life settlement industry date to the 1980s when AIDS first appeared in the United States. Back then, the disease was always terminal and treatment costs were high. The AIDS victims often had no sizable savings and depended on earned income to meet their medical or living expenses. Selling their life insurance policies to investors became a common source of cash for patients seeking a more dignified end of life. The market that developed for these early life settlements or viaticals was based on reviewing T-cell counts of life insurance policyholders viators who were interested in selling their policies. The T-cell count was a reasonably accurate predictor of life expectancy, which typically ranged from six to 24 months, with some up to 36 months. Early investors could realize strong returns in this market, as the life span of an AIDS victim was relatively predictable and the transaction could therefore be valued with a reasonable degree of precision. This changed as the treatment of AIDS progressed in the 1990s. With protease inhibitors and other antiretroviral drugs readily available, life spans began to increase substantially. In fact, the selling (viaticating) of life insurance policies often became a way to buy these life-extending drugs creating a self-fulfilling investment failure. By the late 1990s, being diagnosed as HIV-positive no longer meant death was imminent for the typical viator who was only in his 40s. In fact, because these drugs extended life considerably more than the initial estimate, maturities in a non-liquid asset were much longer as well. Therefore, in many cases, more premiums needed to be paid to maintain an expected return of principal. The combination of additional premiums and delayed payoffs created substantially reduced returns on the policies. Eventually, the viability of investments in viatical settlements declined and investing in the life insurance policies of senior citizens with impairments began. The term life settlement was soon coined. Yet even as the life settlements industry evolved, estimating life expectancies remained a standard approach to determine policy values. Current approach to life expectancies LIFE EXPECTANCY (LE) AND LE ESTIMATORS Life expectancy (LE) is a term often used and sometimes misunderstood. Therefore, we will start with a definition. LE is an actuarial calculation and is best represented in formulaic form. An LE is a determination of the average future lifetime of someone currently at age x, and is typically denoted by the symbol ex. In formula form then, life expectancy is: where tpx is the probability of living from age x to age x+t, and includes calculations through the end of the assumed mortality table (age x=oo), which is some age greater than 100 for all recent tables. On average, people will live halfway through their year of death so expectancy is modified to add 0.5 to denote a complete life expectancy. Another way of viewing the life expectancy is this: if 1,000 people were alive at age x, then roughly half of them would still be alive at their life expectancy, or age x + ex. By inference, then, roughly half would also have died by that age. (While this is a reasonable analogy a 50/50 chance to live to one s life expectancy, the theoretical calculation does differ from this by a few months due to the actual shape of the mortality curve so that slightly more than 50 percent of a population will typically die before their life expectancy is reached. This is particularly noticeable for older ages and/or mortality assessed with impairment ratings.) 3

6 Several companies have been established to perform the service of calculating life expectancies. While the term underwriter is often used to describe these companies, we prefer the term LE estimator, as services are sometimes provided by health personnel other than life insurance underwriters. Other professional backgrounds, including medical doctors, can be found among LE estimators. In addition to these established firms, a few life settlement buyers and investors have been known to use in-house underwriting staff. LE estimators are typically compensated on a per-casereviewed basis. The life expectancy analysis may be done by life insurance underwriters with life industry experience. Some firms have doctors on staff to do their assessments. Some use nurses to do the assessments. Some firms cross train to bring a broader, more informed approach to the subject e.g., medical personnel are trained in the underwriting debit and credit approaches and underwriters are trained in medical criteria. The estimators assessments are based upon their review of medical records, as provided by the insured s representative(s). These firms generally do not request additional tests or documents, but confine their opinions to the information provided. (Life insurance companies will typically obtain motor vehicle records, conduct paramedical or full medical exams, and use drug data-mining firms to track prescription histories.) Describing the information provided is a part of the service provided in their final report. Each review is specific to the documents provided, and it is possible to have an individual rated differently by the same firm when different information is supplied in the two requests or if sufficient time has elapsed to either reflect (1) a change in the individual s prognosis or (2) a change in the procedures to reflect newer information. INSURANCE UNDERWRITING METHODS Most of the life expectancy estimators typically use a reinsurance manual as a starting point for their procedures. These manuals are adjusted by the firm (medical director or chief underwriter) to reflect its view on the applicability of the manual to seniors. This is necessary because reinsurance manuals have focused primarily on the ages of greatest insurance sales, approximately ages 40 to 60. As the life settlement industry is focused on ages above 65, the debits and credits applied often need to be adjusted for reasons that will be discussed in more detail later. In addition, there are conditions for which the insurance answer is decline, rather than to estimate the mortality associated with the condition. As a result, there are sections of the reinsurance manual that do not provide quantifiable guidance to an estimator. At the end of the process, the debits and credits are tallied. A net credit/debit of 0 indicates that the evaluator found no significant conditions and no reason to expect the individual s life expectancy to be reduced or increased beyond what would be considered a normal, or standard, insurance risk. Positive figures indicate that extra mortality is expected. When the sum of the debits/credits is added to the base figure of 100, a mortality ratio is calculated. For example, 150 debits added to the tabular basis of 100 yields a 250 percent ratio. This means that the evaluator expects the individual s probability of dying to be 2.50 times the basic mortality rates at all ages from the date of the review forward. Many of the life expectancy estimators also include judgment along with the debit/credit system to reflect nuances that can be seen in individual cases. All of the estimators will periodically update their methodology to reflect enhancements and new data. Also, some estimators will, in certain circumstances, reflect an alternative expected mortality pattern (usually with severe impairments) to better project life expectancy where fitting to a pre-defined mortality table is deemed inappropriate. 4

7 APPLICATION TO A MORTALITY TABLE From this mortality ratio, a life expectancy is calculated. In practice, therefore, the LE is a derivative calculation first the mortality ratio is calculated, then the LE is derived. (This is a different approach from the early viatical work, wherein the LE was a direct estimate.) The assessor may either use a program that calculates LEs or may look it up in a table. As age increases or as the mortality ratio increases, the life expectancy decreases. Some LE estimators will calculate life expectancy by assuming that mortality rates will show improvement in the future due to medical advances. Future improvements in mortality will lead to a longer life expectancy, all other variables being equal. Similarly, some LE estimators may use an approach other than a pure multiple (such as a partial flat extra or a roll-off of the multiple). It should be noted that while the majority of LE estimators currently use the same starting table the 2001 Valuation Basic Table (VBT), whose development is discussed more in Section IV some make adjustments to it. Consequently, the underlying table may be different among the various underwriting firms both as to calculations and underlying assumptions for mortality and mortality improvement. Therefore, as indicated previously, any investors must make their own determination as to their comfort level with a life expectancy result calculated by an estimator. The table below is a sample of the format of an LE table lookup. An actual table would have a row for each individual age and would show additional columns for the intermediate mortality ratios, such as 125 percent. The numerical entries are taken from the 2001 VBT male nonsmoker table and are meant to be illustrative only. They are intended to be a reasonable depiction of the pattern and level of LEs associated with male nonsmokers at various mortality rating levels. EXCEPTIONS TO THE DEBIT/CREDIT METHODOLOGY While most LE estimates are based on a debit/credit methodology, there are exceptions. Some conditions are evaluated on a more subjective basis, with cancer and HIV being common examples. In these cases, one or more evaluators at the firm will review the file in its entirety and make an assessment based on professional judgment. LIFE EXPECTANCY IN MONTHS, BY MORTALITY MULTIPLE BASED ON 100 PERCENT OF 2001 VBT MALE NONSMOKER Age 100% 150% 200% 250% 300% 350% 400%

8 LE quotes are presented by the estimators as valid for a limited time frame, such as days. However, in practice, these estimates are often used by the recipient over longer periods of time. A common practice among users of LE estimates is to assume that the mortality multiplier that produced the LE continues to be accurate beyond the estimator s declared period of validity. The assumption is that unless health changes after the review, the multiple itself will not decrease. By applying the mortality multiple to the insured s increased age, a new LE can be found in the lookup table, just as was produced in the initial report. LE estimation trend While it is difficult to do an empirical analysis, it is generally believed that LE quotes have been lengthening steadily in the market since assessments began for life settlements in the late 1990s. Anecdotally, it is believed that all the major evaluators issue longer LE estimates today than they did in the past. Reasons for this may include: Mortality assumption importance Given that the LE estimates are typically derived by applying an underwriting load factor to a table, the choice of the underlying table is critical to a resulting LE determination. Making that choice is not straightforward. The long-term experience in life settlement mortality is unknown. Short-term experience is only beginning to emerge. Sections IV through VI of this paper outline key considerations in selecting a mortality table and measure sensitivity to returns by different choices of mortality tables. > Adjusting techniques to recognize medical advancements > Adjusting debits/credits based on emerging experience > Adjusting the underlying mortality table for secular improvements 1 The above comments reflect our knowledge and understanding of the major LE estimators. Nothing in the above should be construed as a recommendation for or against any of the estimators, nor should it be construed as an endorsement of their procedures. In order to establish confidence in an estimator, the investor should perform his own due diligence. 1 That is, overall improvements from lifestyle or general medical advances that are not easily attributable to a specific source, but which have been historically observed. 6

9 III Underwriting While medical underwriting in the life settlement arena has much in common with life insurance underwriting, there are substantive differences that should be recognized by life expectancy estimators and potential investors. Older age underwriting Underwriting individuals over the age of 70 is challenging for both life insurance underwriters and LE estimators. At younger ages, the risk selection focus is on the absence or presence of disease and the severity of the disease. Underwriting manuals provide useful guidance on rating substandard risks at younger ages. At older ages, however, the prevalence of certain common diseases (such as cardiovascular disease) increases to the point that the question is no longer whether it is present or absent, but rather how severe it is in comparison to the baseline level of disease in the insured population. Moreover, in older people, the mortality impact of a given condition often correlates better with functional status than with diagnostic test results that are typically used to determine severity of a disease. For example, underwriters typically use coronary angiogram and echocardiogram results to assess mortality risk for individuals with coronary artery disease. Beyond age 70, however, functional measures such as exercise capacity may be a more accurate predictor of longevity. Assessing functional status is challenging because the assessment tools are not well standardized and long-term data on their predictive value are not yet available. Most long-term-care insurers and many life insurers have added assessments of activities of daily living (ADLs), mobility, and/or tests of cognitive function to the underwriting process for older applicants; this information is frequently not available to the life expectancy estimator. Vigor or frailty among the elderly has a substantial effect on mortality risk, but this has not been extensively studied so quantifying this aspect based on review of medical records is more art than science at this point. Tentative diagnoses Medical underwriters are often called upon to assess the mortality risk of an individual who may have certain symptoms or laboratory tests that suggest the possibility of a disease. To use a common example, an older individual may visit his doctor noting occasional memory lapses. The life insurance underwriter must consider whether this symptom is the first manifestation of early Alzheimer s disease (even though that diagnosis has not been made with certainty) and apply debits to generate extra premium. From a life settlement perspective, however, the LE estimator should consider whether this symptom simply reflects normal aging with no adverse impact on life expectancy. In the current managed care environment, physicians in clinical practice frequently include tentative diagnoses in medical records in order to justify diagnostic testing or therapeutic trials of medications. It is important that LE estimators appropriately consider the significance of tentative diagnoses in medical records when objective evidence supporting the diagnosis is absent. 7

10 Changes in risk over time One of the key benefits of the secondary market for life insurance policyholders is that an adverse change in health of the insured increases the economic value of the policy, in part because the COI portion of the premium cannot be increased by the issuing insurance company (at least not at the individual policy level). While this works to the benefit of life settlement investors when health status declines, there are potential problems if health status improves after the policy is issued, which can occur in a variety of scenarios: > Favorable response to medical or surgical treatments, such as better control of high blood pressure or diabetes with medication or surgical correction of a leaking heart valve; > Favorable changes in risk-related behaviors, such as quitting smoking or ceasing private aviation activities; > Spontaneous stabilization and/or remission of certain diseases with unpredictable patterns of relapse or remission, such as systemic lupus erythematosus or multiple sclerosis; > Medical advances (discussed in more depth in the next section). Life insurers can lower premiums if the insured s health or risk-related behavior improves, but the client must generally initiate the process and submit updated evidence. Once a policy has been sold, however, the insured is no longer motivated to participate in the rate reduction review process. This would potentially leave an investor paying higher than necessary premiums for a longer period of time, with a corresponding negative effect on returns. Medical advances In addition to actuarial consideration of general improvement in life expectancies over time, thoughtful medical underwriting of longevity risk should include an assessment of the likelihood of significant medical advances for any given disease process. The impact of antiretroviral drug therapies on the longevity of patients with HIV/AIDS was mentioned earlier. Another example is progress in gastric banding and other bariatric surgical procedures that can dramatically influence the mortality risk associated with morbid obesity. Advances in transplantation medicine, including refinements in surgical techniques as well as more effective and less toxic antirejection medicines, have altered the prognosis of kidney and liver failure. More effective chemotherapy regimens have significantly improved survival in several forms of cancer (e.g., the use of imatinib in chronic myelogenous leukemia). Life insurers and reinsurers tend to be cautious about reflecting mortality improvement from new treatments in their underwriting guidelines until the benefit has been well documented in long-term studies. For the life insurer, this approach is prudent because the history of medicine is replete with examples of promising therapies that in hindsight were ineffective or even harmful. In contrast, life expectancy estimators need to be up to date on current developments on the frontiers of medicine and remain vigilant for potential treatment breakthroughs. This form of longevity risk can be mitigated somewhat by diversifying a portfolio among differing disease categories, but consideration of potential treatment breakthroughs for specific diseases could help optimize returns. 8

11 IV Mortality Tables The 2001 Valuation Basic Table (VBT) The most common currently used table in the life settlement industry today is the 2001 VBT. In our experience, nearly all the LE providers either use the 2001 VBT directly or some variation of it for a majority of their LE calculations. The 2001 VBT table was developed by the Society of Actuaries from life insurance industry experience from , projected forward to 2001 for assumed mortality improvement, and was published for use beginning in The name Valuation Basic Table indicates that it is intended to be a good approximation of the expected level of insured mortality. This contrasts with the 2001 CSO (Commissioners Standard Ordinary) Table, which contains significant loads to mortality for conservatism, since it is intended for use in establishing reserves for life insurance companies. We note that even though this is the most recent (as of the date of this report) published mortality table of insured lives, the actual mortality data for issue ages 65 and over was limited; actual mortality data for issue ages 75 and over was nonexistent. Similarly, data for attained ages over 85 (regardless of the issue age) was limited. As such, two other sources were used to augment the data. Specifically: To supplement the Basic Mortality Tables[*] at older ultimate ages, male insured data from the Veterans Administration WWII table was obtained....older issue age select data was obtained from a special Old Age Mortality study that was commissioned for this committee and performed by Bragg and Associates. 2 * Source table for the 2001 VBT 2 Society of Actuaries Report of the Individual Life Insurance Valuation Task Force, November We note that the use of the Veterans Administration (VA) WWII Table does not indicate that the data is 60 years old; it is experience from the 1990s on males who served in WWII and who were insured by the VA. There is no mortality table that is correct for all situations. There are several advantages to using the 2001 VBT as a base for mortality: > It is well recognized and therefore useful as a standard; > It is the most recent insurance table currently available (although a 2008 VBT table is expected to be released soon); > It recognizes the select and ultimate nature of insured mortality (discussed further below); > The 2001 VBT recognizes smoker and nonsmoker distinctions. The 2001 VBT also has some disadvantages: > The age 65+ mortality experience is not extensive, as previously noted; > The Veterans Administration experience may not be consistent with true insured experience; > The true length of the select period for older ages is unclear (discussed further below); > Recent experience by insurance companies has been significantly better than the 2001 VBT. It is interesting to note that, while the 2001 VBT is the standard of the life settlement industry, it is not the predominant table used by the life insurance industry. The recent Society of Actuaries (June 2007) Mortality Table Construction Survey Report stated that 33 percent of companies surveyed used the 2001 VBT. The survey showed 42 percent used the Basic Tables and 25 percent used other tables (which could include the company s own experience). In all cases, the underlying table was modified in some way to reflect expected experience. 9

12 Select and ultimate The life insurance industry underwrites its new business, attempting to screen out both imminent deaths and those insureds with significantly more than average probability of death. Therefore, actuaries and underwriters believe and have observed that there is a select period to mortality patterns. This means that the mortality of a 70-year-old recently approved for new life insurance is, on average, expected to be lower than the mortality of a 70-year-old approved for insurance many years ago. The 2001 VBT is said to have a 25-year select period for most ages, as the effects of underwriting are seen in the table for 25 years post-issue. While the select period can be as long as 25 years, the period is reduced for upper issue ages, with the reductions beginning at age 72 for a male nonsmoker. The following table shows the shortening of the select period for representative ages. LENGTH OF THE SELECT PERIOD IN YEARS (MALE NONSMOKER) Issue Age Select Period Mortality rates are, for adult ages, monotonically increasing with age; i.e., rates increase each year. Mortality tables are also generally constructed with a terminal age, or the age beyond which no one is modeled to survive. The 2001 VBT and many contemporary tables put this age at 121. Thus, the mortality rate at age 121 is The effects of underwriting selection are profound for the life insurance industry. First-year mortality after underwriting is a fraction of the mortality that would be expected for a group of insureds of the same age who were not recently underwritten. Using male nonsmokers as an illustrative group, the table below shows the percentage of the ultimate mortality rate expected to be experienced by more recently underwritten males of the same age. Note that reading across any line in the table shows percentages that grow toward 100 percent. All ratios in year 26 are 100 percent. This shows that the effect of underwriting wears off over time and eventually can no longer be detected in the experience of a group of insureds of the same age. As a general rule, the table below shows that the industry considers this time period to be the lesser of attainment of age 100 or 25 years. SELECT MORTALITY AS A PERCENTAGE OF ULTIMATE MORTALITY FOR MALES Issue Year Year Year Year Year Year Age % 53% 68% 90% 96% 100% The above table illustrates the expected impact of underwriting. For example, the 18 percent at age 65 year 1 means that the mortality rate for a newly issued, recently underwritten individual is 18 percent of the mortality rate of an age 65 male who was underwritten at age 40 or lower. Reasons for the significance of the select period include: > The effectiveness of underwriting creates a population of newly approved insureds that is healthier than average. Over time the group s risk profile increases as the insureds are exposed to the risks of aging and disease. > The tendency of insureds who contract a disease or disabling condition to maintain their policy, while those who remain healthy might lapse. Over time this removes the best risks and accumulates the worst risks. 10

13 The slope of the 2001 VBT is significantly steeper than earlier tables; i.e., it reflects more selection. Following is an illustrative graph for a male age 75: The above shows that the newer tables, as expected, have lower levels of mortality, but the 2001 VBT mortality increases more rapidly (has a steeper slope) over time. Rationale for this steepness may be for the following reasons: > Inclusion of term data with more anti-selective lapsations that result in higher mortality could cause steepness. > Underwriting changed greatly during the 1980s and 1990s. As such, later durations reflect older underwriting techniques (less blood testing and policies that did not distinguish smokers from nonsmokers) such that the data is not homogeneous. > The separation of data into smoking and nonsmoking classes could have changed the slope from earlier tables that were not smoker-distinct. This may be further shifted by the more recent separation of data into preferred and standard classes. We note that more recent life insurance experience from as compiled by the Society of Actuaries shows an even steeper slope both for all ages combined and for ages 70-plus. This could reflect continuance of the above three rationales or could be for other reasons. 11

14 The increasing steepness of the mortality curve over time would seem to indicate that either one or both of the following are true: > Underwriting techniques have improved and have lowered early duration mortality; > Younger age mortality has improved more than older age mortality. More discussion on the application of this curve to life settlements is contained in the next section. Mortality improvement The 2001 VBT, while based upon data from , has an assumption for mortality improvement built in to adjust the expected mortality rates from 1992 (the mid-point of the experience data) to This annual improvement for the ages most relevant to life settlements is: AGE MALE FEMALE % 0.5% As will be discussed below, the actual mortality experience during was significantly lower than would be expected using these mortality improvement factors. The experience difference could be from a number of reasons previously mentioned, including preferred classification and improved underwriting. Actual prospective mortality improvement is not a constant and will vary due to medical and lifestyle improvements. By trending improvements since 1985 which may not continue at the same level the male mortality improvements used in the 2001 VBT have been conservative from a life insurance perspective; i.e., actual improvements have been between 0.5 percent and 2 percent depending on the source and ages used. Female improvements, however, have been aggressive; i.e., actual improvements have been between 0.5 percent and -1.0 percent depending on the source and ages used experience A new mortality table is expected to soon be released based primarily on data. This data is based on 35 insurance companies contributions and contains $7.4 trillion of exposure and $15 billion of deaths (compared to $5.7 trillion of exposure and $14 billion of deaths in the 2001 VBT study). Additionally, the newer study has $301 billion of exposure and $3.9 billion of deaths (87,000 deaths) over age 60. While the final table to be called the 2008 VBT is not yet available, the underlying data is currently available from the Society of Actuaries and is available to be downloaded as a pivot table. It can then be summarized using a number of characteristics, including age, gender, duration since issue, smoker status, policy size, and observation year. Overall, this experience is much lower than the 2001 VBT. Aggregating across all data points the overall actual result is 74 percent of the 2001 VBT. 12

15 Age Smoking Face Amount Observed Face Amount Actual-to-Expected Gender Range Class Range Deaths Exposed (In Millions) Ratio* ALL All All ALL 699,844 $7,374,165 74% M 60+ NS ALL 26, , F 60+ NS ALL 20, , ALL All NS $250,000+ 8,852 4,247, ALL 60+ NS $250,000+ 2, , ALL 70+ NS $250,000+ 1,011 37, ALL 60+ NS $1,000, , ALL 70+ NS $1,000, , *Weighted by amount. Ratio is data compared to 2001 VBT Sub-segments give a better idea of how the experience may affect life settlement mortality. Following are some key actual-to-expected ratios from the study. Some of the sub-segments have too few deaths to be credible. Nevertheless, it is important to see that recent experience has been, in aggregate, 26 percent lower than the 2001 VBT and that, for older age, fully underwritten, higher-face-amount policies, the experience has been generally between 30 percent and 35 percent better. There are variances by sub-segment. Males have improved more than females (particularly at older ages); nonsmokers have improved more than smokers. Large-face-amount policies have improved more than small policies. But a consistent trend shows significant improvement more than would be expected using simply anticipated annual mortality improvements. Several causes could be effectuating the change and these have not been analyzed: > The experience had 35 companies participating; the data underlying 2001 VBT had only 20 companies. Thus the new data reflects both more experience and broader experience. > The new data could reflect differences in the type of business and the amount of business fully underwritten. (For example, more of the recently issued business was written on a preferred life basis that involves additional underwriting; a 2002 Society of Actuaries Survey on Preferred Underwriting showed that 40 percent to 55 percent of the 2001 VBT is being expected by insurance company pricing actuaries for these classes of individuals.) > Improvement in underwriting techniques. > Underlying improvement in mortality due to lifestyle changes and medical advances. It should be noted that the underlying data includes standard and preferred issues only not impaired lives. Impaired lives are a small enough portion of issued business as to not add much data while adding the complexity of evaluating underwriting effectiveness to the analysis; i.e., the expected mortality would need to be adjusted to include the underwriting loads and flat extras. The experience data adjusted to a expected level is reasonably consistent with current insurance company pricing for standard lives in fully underwritten business. Insurance companies are often using a range of 65 percent to 75 percent of the 2001 VBT for standard mortality assumptions (although the actual figures vary by company, by product type, and by other characteristics such as age, duration, and class). 13

16 V Other Life Settlement Mortality Considerations The prior section discussed current levels of base insured mortality. Life settlements primarily deal with impaired lives. Therefore, there are other considerations that must be taken into account for life settlement mortality. At the outset, we note that there is little or no publicly available data on life settlement experience. There is no life settlement mortality table; in our experience, most life expectancy providers have used the 2001 VBT or a variation of this table in their analyses. Additionally, any data that could be available would have only five to six years of credible experience. And as the life expectancy providers have continually and quickly evolved their standards, an actual-to-expected analysis on business from four, or even three, years ago may no longer be representative of current standards and approaches. Specific adjustments that may be considered in estimating life settlement mortality are: Selectness of the life Life settlement underwriting generally works from medical records only. Current physicals and prescription drug data are usually not obtained as they are for insurance applicants. As such, it is arguable that a policy would be less select than insurance experience would show and may be considered at a start point other than duration one. On the other hand, the very unhealthy lives would be less likely to sell their policies. This would make purchased policies more select. Regarding this second point, we note that not all policies that are reviewed by life expectancy providers are actually sold. It would seem that a reason for not selling would be ill health. Therefore, there could be a difference between mortality experience as measured by a life expectancy provider and actual experience as occurs in a life settlement pool. Steepness of the curve While the 2001 VBT (and the soon-to-be-published 2008 VBT) both use a 25-year select period, it is unclear whether this should apply to older ages. Results from the data would seem to indicate at least a steeper curve as illustrated by the following: ACTUAL-TO-EXPECTED* RATIO BY DURATION Both Genders Both Smoking Statuses All Face Amounts Duration Ages 60+ Ages % 52% Ultimate Total 77% 79% *Expected is the 2001 VBT Select and Ultimate; Actual is the Experience Study. (Most characteristics were combined above, as the data becomes sparse and difficult to interpret if split into many categories.) The above shows that early durations (primarily durations 1 and 2) have been more select than the 2001 VBT would indicate, while other durations follow roughly a constant level of improvement. If life settlements follow a similar trend, it could indicate that if low mortality in the early durations is being experienced, then higher mortality might be expected later. (However, if the estimator has already made mortality table adjustments, then this phenomenon may already be taken into account in their analysis.) Despite the trend in insured lives, life settlements mortality may not follow the same trend. The insured data consists entirely of preferred or standard lives. Many of the policies in a life settlements portfolio are rated at greater than 100 percent expected mortality rating and thus generally are impaired lives. The mortality curve could be less steep for impaired lives if in some cases the mortality loads would be better expressed as flat extras; i.e., a fixed excess mortality load that is additive to the underlying curve, rather than multiplicative. 14

17 We note that some LE estimators have developed adjusted tables with steeper slopes than the pure 2001 VBT Select and Ultimate. While the experience seems to give some support to this, caution must be urged for the following reasons: > Life settlement transactions are often based upon an impaired insured, whose mortality does not logically begin at the rather low levels seen in the first year of an insurance industry select and ultimate table. > Impaired insureds would seem to have, by definition, less room for future deterioration than newly insured individuals due to their relative health profiles. > Insured tables show some deterioration in mortality during the select period due to the tendency of healthy individuals to lapse their insurance coverage, at which point they are removed from the mortality study as well. Life settlements are characterized by institutional investors with no intention to allow a policy to lapse, thereby maintaining greater homogeneity through future years (i.e., less deterioration). Mortality multiples vs. flat extras Mortality multiples (table ratings) and flat extras are two distinctly different methods for assessing mortality risk. The major differences between these two approaches are outlined below. MORTALITY MULTIPLE (TABLE RATING) > Based on mortality ratio: MR = 100 x (impaired death rate/expected death rate) > Ideally suited for risks associated with a level or slowly increasing percentage of standard mortality (e.g., diabetes) > More sensitive to changes in age FLAT EXTRA > Based on excess death rate: EDR = (impaired death rate expected death rate)/1,000 > Ideally suited for risks associated with a level number of extra deaths per year (e.g., private aviation) > Less sensitive to changes in age While the mortality multiple works well for many medical impairments at younger ages, this method is quite sensitive to the significant increase in expected mortality at older ages. The graph below compares expected mortality for an 80-year-old male nonsmoker at a 200 percent rating vs. a 30/1,000 flat extra rating. The life expectancies are 127 months at 100 percent (2001 VBT), 92 months at a 200 percent rating, and 104 months using a 30/1,000 flat extra. 15

18 Even if the substandard mortality multiple accurately reflects the increased risk initially, the impact of the multiple may become excessive as the select rates transition toward ultimate rates. Moreover, to the extent that the underlying mortality table overstates true mortality risk, these inaccuracies would be accentuated when using mortality multiples. Depending upon the specific impairment, using a flat extra may in some cases more accurately reflect the actual mortality risk over time. Understanding the nature of the impairment and accounting for it correctly is critical in life settlement life expectancy assessment. Whether this involves blending mortality multiples with flat extras, grading off table ratings over time, or some other innovative approach, the key point is that the traditional life insurance underwriting approach may not be adequate in all cases in the life settlement arena. Variability of life expectancies It is generally understood that life expectancies are estimates and that variance is to be expected. Part of the appeal of assembling a portfolio with larger numbers of insured lives is to reduce the impact of random statistical variation in life expectancies. Another possible approach would be to consider how much variability of life expectancies could be predicted based on the specific disease. As a hypothetical example, assume Condition A is associated with a relatively high risk of mortality in seven years with relatively few long-term survivors. In contrast, the timing of excess mortality in Condition B is less predictable. The distribution of life expectancies is depicted graphically below. Both conditions have a median life expectancy of seven years, but the Condition B curve is flatter with fatter tails. Individuals with Condition B would add more volatility to a portfolio than those with Condition A. Other medical conditions might have different skew, or in some forms of cancer the curve would be bimodal with two relatively discrete humps. If LE estimators added this extra dimension to their LE estimates, investors and actuaries would be better able to project cash flow characteristics for a given portfolio. 16

19 VI Mortality Analysis In order to test portfolio value sensitivity to mortality changes, we created a sample portfolio of 250 policies and measured value changes using different mortality assumptions. The analysis was done on both a probabilistic basis and a stochastic basis. > Probabilistic Assumes deaths occur in proportion to the mortality that would be expected from the direct use of a mortality table and mortality multiple. Death benefits and premiums are therefore reduced proportionately over time. While this does not represent the result for any one life, it is useful to calculate an expected result and is useful for expected value determination. In stochastic analysis, premiums are terminated for a life when death occurs. Because the time of death is randomly drawn, the value of each policy is likely to be different in each scenario. As such, a multitude of scenarios are typically run. Results are typically expressed in ranges that show the average expectation but also confidence limits, such as 90 percent or 95 percent confidence, which indicate the percentage of trials that produced a result at that level or greater. Stochastic analysis is useful to giving ranges of possible values, understanding the range of risks involved, and developing confidence intervals to achieving a level of results. > Stochastic The time each life dies is determined based on a number of random trials. In each trial, the date of death is the randomly generated variable with the same expected value as in the probabilistic valuation. However, instead of having fractional deaths each year based on probabilities, each life either lives or dies based on the results of the randomly generated variables. Sample portfolio characteristics Policies: 250 policies; 272 lives (22 joint last survivor policies) Face Amount: $464,000,000. Average size: $1.86 million. Minimum face amount: $100,000; Maximum face amount: $17,200,000 Ages at Settlement: Range from 52 97, average age = 81 Gender: Smoking: Life Expectancies: 70 percent Male 94 percent Nonsmoker Range from 14 to 161 months. Average is 79 months weighted by policy and 89 months weighted by face amount. 17

20 When applied to 100 percent of the 2001 VBT Select and Ultimate, Age Nearest Birthday basis, with no mortality improvement (used here as a starting point), this created a mortality multiple for each life. For example, one of the lives in the portfolio was a 77- year-old male whose life expectancy was estimated at 77 months. The calculated mortality ratio for this person was 403 percent, or roughly four times what would be expected using the 2001 VBT directly. In total, these loads in the portfolio ranged from 75 percent to 9,000 percent and averaged 470 percent by policy and 348 percent by face amount. [We note that the maximum multiple appears to be large; it is the mathematical result of a relatively low LE assignment (40 months) to a relatively young individual (age 56)]. Other assumptions > Stochastic analysis used 1,000 random trials. > Baseline mortality is assumed to be 2001 VBT Select and Ultimate, Age Nearest Birthday without mortality improvement. > Premium assumed to always be paid so that policies never lapse. > Discount rate of 10 percent used for illustration. > We assumed no expenses associated with the portfolio. > We did not evaluate or consider the possibility of the insurance company s contesting a claim. We also did not consider insurance company nonpayment or reduced payment for any reason (such as bankruptcy). > We did not consider the impact of an insurer increasing cost of insurance (COI) rates on an in-force block. > We did not consider or evaluate any financing connected with these policies. > We did not consider the effect of federal income tax or any other taxes. Several of the assumptions were chosen for simplicity so that the results would focus on mortality changes. The table below shows the expected range of results for the above described portfolio and using the above described assumptions. We also tested the results using seven alternative mortality scenarios: > Scenario 1 measures the impact if the actual underlying mortality is more consistent with insurance company current pricing of standard lives. While the actual expected level varies by company, we have characterized this as 70 percent of VBT. > Scenario 2 measures the impact if the calculated mortality multiple is, in fact, a flat extra. In this scenario, the initial mortality is the same as the baseline, but the future mortality has a flatter slope. The flat extra chosen was designed to replicate the mortality multiple in the third duration. > Scenario 3 measures the impact of a mortality improvement assumption. In this case, 1 percent improvement per year is utilized. > Scenario 4 measures the impact if the actual life expectancy is 10 percent longer than that utilized in the pricing. > Scenario 5 measures the impact if the actual life expectancy is 20 percent longer than that utilized in the pricing. > Scenario 6 uses the same target life expectancy but applies a volatility factor to the target. The volatility factor is set to allow the actual mortality multiple to vary by using a standard deviation equal to a 100 percent mortality load; i.e., the average mortality load for this portfolio is approximately 350 percent, and we randomly generated a result using a normal curve with a standard deviation of 30 percent, which is approximately equal to a 100 percent mortality load. One standard deviation has results within 250 percent to 450 percent, and two standard deviations would be within 150 percent to 550 percent. Therefore, the expected results are the same, but the range of results varies more than would be produced in the base scenario. > Scenario 7 uses a combination of the above tests. Here we have used 70 percent of VBT as the underlying starting mortality, combined with 1 percent annual mortality improvement and combined with the broader volatility factor described in Scenario 6. 18