Life Settlement Mortality Considerations

and Their Effect on Portfolio Valuation

Ed Mohoric, FSA, MAAAMILLIMAN USA

Robert O. Kinney, M.D., FLMIPHOENIX LIFE SOLUTIONS

March 1, 2008

ii XXXXXX | X X X X X X

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 USA

Robert O. Kinney, M.D., FLMI

PHOENIX LIFE SOLUTIONS

March 1, 2008

Preface

1

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 12

2002–2004 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

2

The value of a life settlement policy — or the value ofan investment portfolio comprising multiple settlements— will often be determined by the provider’s estimateof the policyholder’s life expectancy. That is why apotential investor should start by understanding whatthe provider believes is the underlying mortality of thatportfolio.

In evaluating life expectancy estimates and analyzingpotential investment pools, we have determined thefollowing:

> Life expectancy estimators generally use life insurance underwriting approaches in their analyses,but apply them to ages and load levels typically notencountered by life insurance companies, and thenattempt to compensate for that in their analyses.

> Usually, life expectancy estimates are expressed asmortality 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.

> Life expectancy estimates are points in time, basedon current health conditions. Actual health statuscan change over time, resulting in worsened orimproved mortality expectations.

> Ultimate mortality on life settlements is not yetknown because life settlements is a new and evolving industry. The 2001 Valuation Basic Table(VBT), sometimes with adjustments, is the mostcommonly used table as a basis for life expectancyestimates in the United States. However, recentunderwriting experience of life insurers is signifi-cantly lower than that table’s estimates.

I Executive Summary

> Understanding the select and ultimate nature of underwritten or medically reviewed mortality isimportant to determining the value of life settlements. For example, the 2001 VBT’s 25-yearselect period may be too long for senior lives, which is why some life expectancy underwritershave adjusted the table to use a steeper slope. Newer insured experience seems to confirm this;however, little evidence exists that life settlementmortality actually shows a steeper slope than the2001 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 likelyincrease estimates of life expectancies but may more accurately reflect expected mortality.

> Clearly, mortality variations that lengthen lifeexpectancies can have a significant impact on thevalue of a portfolio.

3

Viaticals

The roots of the life settlement industry date to the1980s 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 incometo meet their medical or living expenses. Selling theirlife insurance policies to investors became a commonsource 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 24months, with some up to 36 months. Early investorscould realize strong returns in this market, as the lifespan 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 beganto increase substantially. In fact, the selling (viaticat-ing) of life insurance policies often became a way tobuy these life-extending drugs — creating a self-fulfill-ing investment failure.

By the late 1990s, being diagnosed as HIV-positive no longer meant death was imminent for the typicalviator who was only in his 40s. In fact, because thesedrugs extended life considerably more than the initialestimate, maturities in a non-liquid asset were muchlonger as well. Therefore, in many cases, more premiums needed to be paid to maintain an expectedreturn of principal. The combination of additional premiums and delayed payoffs created substantiallyreduced returns on the policies.

II Background on Life Settlements Mortality

Eventually, the viability of investments in viatical settlements declined and investing in the life insurancepolicies of senior citizens with impairments began. The term “life settlement” was soon coined. Yet evenas the life settlements industry evolved, estimating lifeexpectancies 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 witha definition. LE is an actuarial calculation and is bestrepresented in formulaic form. An LE is a determinationof 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 agex+t, and includes calculations through the end of theassumed mortality table (age x=oo), which is some agegreater than 100 for all recent tables. On average, people will live halfway through their year of death soexpectancy is modified to add 0.5 to denote a completelife expectancy.

Another way of viewing the life expectancy is this: if1,000 people were alive at age x, then roughly half ofthem would still be alive at their life expectancy, or agex + ex. By inference, then, roughly half would also havedied 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 fewmonths due to the actual shape of the mortality curveso that slightly more than 50 percent of a populationwill typically die before their life expectancy is reached. This is particularly noticeable for older ages and/ormortality assessed with impairment ratings.)

4

Several companies have been established to perform the service of calculating life expectancies. While theterm “underwriter” is often used to describe these companies, we prefer the term “LE estimator,” as services are sometimes provided by health personnelother than life insurance underwriters. Other professionalbackgrounds, including medical doctors, can be foundamong LE estimators. In addition to these establishedfirms, a few life settlement buyers and investors havebeen known to use in-house underwriting staff.

LE estimators are typically compensated on a per-case-reviewed basis. The life expectancy analysis may be done by life insurance underwriters with life industryexperience. Some firms have doctors on staff to do theirassessments. Some use nurses to do the assessments.Some firms cross train to bring a broader, moreinformed approach to the subject — e.g., medicalpersonnel are trained in the underwriting debit and cred-it approaches and underwriters are trained in medical criteria.

The estimators’ assessments are based upon theirreview of medical records, as provided by the insured’srepresentative(s). These firms generally do not requestadditional tests or documents, but confine their opinions to the information provided. (Life insurancecompanies will typically obtain motor vehicle records,conduct paramedical or full medical exams, and usedrug data-mining firms to track prescription histories.)Describing the information provided is a part of theservice provided in their final report. Each review isspecific to the documents provided, and it is possibleto have an individual rated differently by the samefirm when different information is supplied in the two requests or if sufficient time has elapsed to eitherreflect (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 areinsurance manual as a starting point for their procedures. These manuals are adjusted by the firm(medical director or chief underwriter) to reflect its viewon the applicability of the manual to seniors. This isnecessary because reinsurance manuals have focusedprimarily on the ages of greatest insurance sales,approximately ages 40 to 60. As the life settlementindustry is focused on ages above 65, the debits andcredits applied often need to be adjusted for reasonsthat 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 providequantifiable 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 addedto the base figure of 100, a mortality ratio is calculated.For example, 150 debits added to the tabular basis of100 yields a 250 percent ratio. This means that theevaluator expects the individual’s probability of dyingto be 2.50 times the basic mortality rates at all agesfrom the date of the review forward.

Many of the life expectancy estimators also includejudgment along with the debit/credit system to reflectnuances that can be seen in individual cases. All of theestimators will periodically update their methodologyto reflect enhancements and new data. Also, some estimators will, in certain circumstances, reflect analternative expected mortality pattern (usually withsevere impairments) to better project life expectancywhere fitting to a pre-defined mortality table is deemedinappropriate.

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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 LEis 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 calculatesLEs 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 lifeexpectancy by assuming that mortality rates will showimprovement in the future due to medical advances.Future improvements in mortality will lead to a longerlife expectancy, all other variables being equal.Similarly, some LE estimators may use an approachother than a pure multiple (such as a partial flat extraor a roll-off of the multiple).

It should be noted that while the majority of LE estimators currently use the same starting table — the2001 Valuation Basic Table (VBT), whose developmentis discussed more in Section IV — some make adjustments to it. Consequently, the underlying tablemay be different among the various underwriting firms— both as to calculations and underlying assumptionsfor mortality and mortality improvement. Therefore, as indicated previously, any investors must make theirown 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 LEtable lookup. An actual table would have a row for each individual age and would show additional columnsfor 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 reasonabledepiction of the pattern and level of LEs associated withmale nonsmokers at various mortality rating levels.

EXCEPTIONS TO THE DEBIT/CREDIT METHODOLOGY

While most LE estimates are based on a debit/creditmethodology, there are exceptions. Some conditionsare evaluated on a more subjective basis, with cancerand HIV being common examples. In these cases, oneor more evaluators at the firm will review the file in itsentirety and make an assessment based on professionaljudgment.

LIFE EXPECTANCY IN MONTHS, BY MORTALITY MULTIPLE

BASED ON 100 PERCENT OF 2001 VBT MALE NONSMOKER

Age 100% 150% 200% 250% 300% 350% 400%

65 240 208 187 172 160 151 143

70 200 171 153 139 128 120 114

75 166 140 123 111 102 94 88

80 128 105 91 81 74 68 63

85 93 74 62 54 49 44 40

90 59 43 34 29 25 22 19

6

LE quotes are presented by the estimators as valid fora limited time frame, such as 90–180 days. However,in practice, these estimates are often used by the recipi-ent over longer periods of time. A common practiceamong users of LE estimates is to assume that themortality multiplier that produced the LE continues tobe accurate beyond the estimator’s declared period ofvalidity. The assumption is that unless health changesafter the review, the multiple itself will not decrease. By applying the mortality multiple to the insured’sincreased age, a new LE can be found in the lookuptable, 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 lengtheningsteadily in the market since assessments began for lifesettlements in the late 1990s. Anecdotally, it is believedthat all the major evaluators issue longer LE estimatestoday than they did in the past. Reasons for this may include:

> Adjusting techniques to recognize medical advancements

> Adjusting debits/credits based on emerging experience

> Adjusting the underlying mortality table for secular improvements1

The above comments reflect our knowledge andunderstanding of the major LE estimators. Nothing inthe above should be construed as a recommendationfor or against any of the estimators, nor should it beconstrued as an endorsement of their procedures. Inorder to establish confidence in an estimator, theinvestor 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.

Mortality assumption importance

Given that the LE estimates are typically derived byapplying an underwriting load factor to a table, the choice of the underlying table is critical to a resulting LE determination. Making that choice is notstraightforward. The long-term experience in life settlement mortality is unknown. Short-term experienceis 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 ofmortality tables.

7

While medical underwriting in the life settlement arenahas much in common with life insurance underwriting,there are substantive differences that should be recognized by life expectancy estimators and potentialinvestors.

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 focusis on the absence or presence of disease and the severityof the disease. Underwriting manuals provide usefulguidance 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 longerwhether 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, themortality impact of a given condition often correlatesbetter with functional status than with diagnostic testresults 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 coronaryartery disease. Beyond age 70, however, functionalmeasures such as exercise capacity may be a moreaccurate predictor of longevity.

Assessing functional status is challenging because the assessment tools are not well standardized andlong-term data on their predictive value are not yetavailable. Most long-term-care insurers and many lifeinsurers have added assessments of activities of daily

III Underwriting

living (ADLs), mobility, and/or tests of cognitive func-tion to the underwriting process for older applicants; thisinformation is frequently not available to the lifeexpectancy estimator. Vigor or frailty among the elderlyhas a substantial effect on mortality risk, but this hasnot been extensively studied so quantifying this aspectbased on review of medical records is more art thanscience at this point.

Tentative diagnoses

Medical underwriters are often called upon to assessthe mortality risk of an individual who may have certain symptoms or laboratory tests that suggest thepossibility of a disease. To use a common example, an older individual may visit his doctor noting occasional memory lapses. The life insurance under-writer must consider whether this symptom is the firstmanifestation of early Alzheimer’s disease (even thoughthat diagnosis has not been made with certainty) and apply debits to generate extra premium. From alife settlement perspective, however, the LE estimatorshould consider whether this symptom simply reflects normal aging with no adverse impact on lifeexpectancy. 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 tentativediagnoses in medical records when objective evidencesupporting the diagnosis is absent.

8

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 insurancecompany (at least not at the individual policy level).While this works to the benefit of life settlementinvestors 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 bloodpressure 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’shealth or risk-related behavior improves, but the clientmust generally initiate the process and submit updatedevidence. Once a policy has been sold, however, theinsured is no longer motivated to participate in therate reduction review process. This would potentiallyleave 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 generalimprovement in life expectancies over time, thoughtfulmedical underwriting of longevity risk should includean assessment of the likelihood of significant medicaladvances 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 associatedwith morbid obesity. Advances in transplantation medicine, including refinements in surgical techniquesas well as more effective and less toxic antirejectionmedicines, have altered the prognosis of kidney andliver failure. More effective chemotherapy regimenshave 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 aboutreflecting mortality improvement from new treatmentsin their underwriting guidelines until the benefit hasbeen well documented in long-term studies. For the life insurer, this approach is prudent because the histo-ry of medicine is replete with examples of promising therapies that in hindsight were ineffective or evenharmful. In contrast, life expectancy estimators need tobe up to date on current developments on the frontiersof medicine and remain vigilant for potential treatmentbreakthroughs. This form of longevity risk can be mitigated somewhat by diversifying a portfolio amongdiffering disease categories, but consideration of potential treatment breakthroughs for specific diseasescould help optimize returns.

9

The 2001 Valuation Basic Table (VBT)

The most common currently used table in the life settlement industry today is the 2001 VBT. In ourexperience, nearly all the LE providers either use the2001 VBT directly or some variation of it for a majority of their LE calculations.

The 2001 VBT table was developed by the Society ofActuaries from life insurance industry experience from1990–1995, projected forward to 2001 for assumedmortality improvement, and was published for usebeginning in 2001. The name “Valuation Basic Table”indicates that it is intended to be a good approximationof the expected level of insured mortality. This con-trasts with the 2001 CSO (Commissioners StandardOrdinary) Table, which contains significant loads tomortality for conservatism, since it is intended for usein 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 tableof 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 issueage) was limited. As such, two other sources were used to augment the data. Specifically:

“To supplement the 1990-95 Basic MortalityTables[*] at older ultimate ages, male insured datafrom the Veterans Administration WWII table wasobtained. ...Older issue age select data was obtained from a special Old Age Mortality study that was commissioned for this committee and performed byBragg and Associates.” 2

IV Mortality Tables

* Source table for the 2001 VBT

2 Society of Actuaries Report of the Individual Life InsuranceValuation Task Force, November 2001.

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 maleswho 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 the2001 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 nonsmokerdistinctions.

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 thestandard of the life settlement industry, it is not the predominant table used by the life insurance industry.The recent Society of Actuaries (June 2007) MortalityTable Construction Survey Report stated that 33 percent of companies surveyed used the 2001 VBT.The survey showed 42 percent used the 1975–80 Basic Tables and 25 percent used other tables (whichcould include the company’s own experience). In allcases, the underlying table was modified in some wayto reflect expected experience.

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Select and ultimate

The life insurance industry underwrites its new business,attempting to screen out both imminent deaths andthose insureds with significantly more than averageprobability of death. Therefore, actuaries and under-writers believe and have observed that there is a “select period” to mortality patterns. This means thatthe mortality of a 70-year-old recently approved fornew life insurance is, on average, expected to be lowerthan the mortality of a 70-year-old approved for insurance many years ago. The 2001 VBT is said tohave a 25-year select period for most ages, as the effectsof underwriting are seen in the table for 25 years post-issue. While the select period can be as long as25 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 shorteningof the select period for representative ages.

LENGTH OF THE SELECT PERIOD IN YEARS

(MALE NONSMOKER)

Issue Age 70 75 80 85 90 95

Select Period 25 21 19 19 14 9

Mortality rates are, for adult ages, monotonicallyincreasing 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, themortality rate at age 121 is 1.0000.

The effects of underwriting selection are profound forthe life insurance industry. First-year mortality afterunderwriting is a fraction of the mortality that wouldbe expected for a group of insureds of the same agewho were not recently underwritten. Using male nonsmokers as an illustrative group, the table belowshows the percentage of the ultimate mortality rate expected to be experienced by more recentlyunderwritten males of the same age. Note that readingacross any line in the table shows percentages that

grow toward 100 percent. All ratios in year 26 are100 percent. This shows that the effect of underwriting“wears off” over time and eventually can no longer bedetected in the experience of a group of insureds of thesame age. As a general rule, the table below showsthat the industry considers this time period to be thelesser of attainment of age 100 or 25 years.

SELECT MORTALITY AS A PERCENTAGE OF

ULTIMATE MORTALITY FOR MALES

Issue Year Year Year Year Year YearAge 1 6 11 16 21 26

65 18% 53% 68% 90% 96% 100%

70 18 53 70 91 96 100

75 30 48 66 92 100 100

80 23 58 78 100 100 100

85 28 66 99 100 100 100

90 48 99 100 100 100 100

The above table illustrates the expected impact ofunderwriting. For example, the 18 percent at age 65 year 1 means that the mortality rate for a newlyissued, recently underwritten individual is 18 percentof the mortality rate of an age 65 male who was underwritten at age 40 or lower.

Reasons for the significance of the select periodinclude:

> The effectiveness of underwriting creates a population of newly approved insureds that ishealthier than average. Over time the group’s riskprofile increases as the insureds are exposed to the risks of aging and disease.

> The tendency of insureds who contract a disease ordisabling condition to maintain their policy, whilethose who remain healthy might lapse. Over timethis removes the best risks and accumulates theworst risks.

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The slope of the 2001 VBT is significantly steeper than earlier tables; i.e., it reflects more selection. Following isan 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 olderunderwriting techniques (less blood testing and policies that did not distinguish smokers from non-smokers) such that the data is not homogeneous.

> The separation of data into smoking and nonsmokingclasses could have changed the slope from earliertables that were not smoker-distinct. This may be further shifted by the more recent separation of datainto preferred and standard classes.

We note that more recent life insurance experiencefrom 2002–2004 as compiled by the Society ofActuaries shows an even steeper slope — both for allages combined and for ages 70-plus. This could reflect continuance of the above three rationales or could befor other reasons.

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The increasing steepness of the mortality curve overtime would seem to indicate that either one or both ofthe following are true:

> Underwriting techniques have improved and havelowered early duration mortality;

> Younger age mortality has improved more thanolder age mortality.

More discussion on the application of this curve tolife settlements is contained in the next section.

Mortality improvement

The 2001 VBT, while based upon data from 1990–1995,has an assumption for mortality improvement built into adjust the expected mortality rates from 1992 (themid-point of the experience data) to 2001. This annualimprovement for the ages most relevant to life settle-ments is:

AGE MALE FEMALE

55-80 1.0% 0.5%

81 0.9 0.5

82 0.8 0.5

83 0.7 0.5

84 0.6 0.5

85 0.5 0.5

86 0.4 0.4

87 0.3 0.3

88 0.2 0.2

89 0.1 0.1

90+ 0.0 0.0

As will be discussed below, the actual mortality experience during 2002–2004 was significantly lowerthan 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 improvementis not a constant and will vary due to medical andlifestyle improvements. By trending improvements since1985 — which may not continue at the same level —the male mortality improvements used in the 2001 VBThave been conservative from a life insurance perspective;i.e., actual improvements have been between 0.5 percentand 2 percent depending on the source and ages used.Female improvements, however, have been aggressive;i.e., actual improvements have been between 0.5 percentand -1.0 percent depending on the source and ages used.

2002–2004 experience

A new mortality table is expected to soon be releasedbased primarily on 2002–2004 data. This data is based on 35 insurance companies’ contributions andcontains $7.4 trillion of exposure and $15 billion ofdeaths (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 currentlyavailable from the Society of Actuaries and is availableto 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 2001VBT. Aggregating across all data points the overallactual result is 74 percent of the 2001 VBT.

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Sub-segments give a better idea of how the experiencemay affect life settlement mortality. Following aresome 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 thatrecent experience has been, in aggregate, 26 percentlower than the 2001 VBT and that, for older age, fully underwritten, higher-face-amount policies, theexperience has been generally between 30 percent and 35 percent better.

There are variances by sub-segment. Males haveimproved more than females (particularly at olderages); nonsmokers have improved more than smokers.Large-face-amount policies have improved more thansmall policies. But a consistent trend shows significantimprovement — more than would be expected usingsimply anticipated annual mortality improvements.

Several causes could be effectuating the change andthese have not been analyzed:

> The 2002–2004 experience had 35 companies participating; the data underlying 2001 VBT hadonly 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 recentlyissued business was written on a preferred life basis that involves additional underwriting; a

2002 Society of Actuaries Survey on PreferredUnderwriting showed that 40 percent to 55 percentof the 2001 VBT is being expected by insurancecompany pricing actuaries for these classes of individuals.)

> Improvement in underwriting techniques.

> Underlying improvement in mortality due to lifestylechanges and medical advances.

It should be noted that the underlying data includesstandard and preferred issues only — not impairedlives. Impaired lives are a small enough portion ofissued business as to not add much data while addingthe complexity of evaluating underwriting effectivenessto the analysis; i.e., the expected mortality would need to be adjusted to include the underwriting loads andflat extras.

The experience data adjusted to a 2007–2008 expectedlevel is reasonably consistent with current insurancecompany pricing for standard lives in fully underwrittenbusiness. 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 othercharacteristics such as age, duration, and class).

Age Smoking Face Amount Observed Face Amount Actual-to-ExpectedGender Range Class Range Deaths Exposed (In Millions) Ratio*

ALL All All ALL 699,844 $7,374,165 74%

M 60+ NS ALL 26,075 178,866 72

F 60+ NS ALL 20,980 100,410 78

ALL All NS $250,000+ 8,852 4,247,049 63

ALL 60+ NS $250,000+ 2,460 178,250 67

ALL 70+ NS $250,000+ 1,011 37,689 69

ALL 60+ NS $1,000,000+ 454 100,460 64

ALL 70+ NS $1,000,000+ 211 25,711 65

*Weighted by amount. Ratio is 2002–2004 data compared to 2001 VBT

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The prior section discussed current levels of baseinsured 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 publiclyavailable data on life settlement experience. There isno life settlement mortality table; in our experience,most life expectancy providers have used the 2001VBT or a variation of this table in their analyses.

Additionally, any data that could be available wouldhave only five to six years of credible experience. Andas the life expectancy providers have continually andquickly evolved their standards, an actual-to-expectedanalysis on business from four, or even three, yearsago may no longer be representative of current stan-dards and approaches.

Specific adjustments that may be considered in estimating life settlement mortality are:

Selectness of the life

Life settlement underwriting generally works frommedical records only. Current physicals and prescription drug data are usually not obtained as theyare for insurance applicants. As such, it is arguablethat 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 makepurchased policies more select.

Regarding this second point, we note that not all policies that are reviewed by life expectancy providersare actually sold. It would seem that a reason for notselling would be ill health. Therefore, there could be adifference between mortality experience as measuredby a life expectancy provider and actual experience asoccurs in a life settlement pool.

V Other Life Settlement Mortality Considerations

Steepness of the curve

While the 2001 VBT (and the soon-to-be-published2008 VBT) both use a 25-year select period, it isunclear whether this should apply to older ages. Resultsfrom the 2002–2004 data would seem to indicate atleast a steeper curve as illustrated by the following:

2002–2004 ACTUAL-TO-EXPECTED* RATIO BY DURATION

Both Genders | Both Smoking Statuses | All Face Amounts

Duration Ages 60+ Ages 70+

1 73% 52%

2 69 56

3 92 102

4-5 75 77

6-10 80 84

11-15 74 80

16-20 76 78

21-25 86 81

Ultimate 89 97

Total 77% 79%

*Expected is the 2001 VBT Select and Ultimate; Actual is the 2002-2004 Experience Study.

(Most characteristics were combined above, as the data becomessparse 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 the2001 VBT would indicate, while other durations follow roughly a constant level of improvement. If lifesettlements follow a similar trend, it could indicate that if low mortality in the early durations is beingexperienced, then higher mortality might be expectedlater. (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 insureddata consists entirely of preferred or standard lives.Many of the policies in a life settlements portfolio arerated at greater than 100 percent expected mortalityrating 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 betterexpressed as flat extras; i.e., a fixed excess mortalityload that is additive to the underlying curve, ratherthan multiplicative.

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We note that some LE estimators have developedadjusted tables with steeper slopes than the pure 2001 VBT Select and Ultimate. While the 2002–2004experience seems to give some support to this, caution must be urged for the following reasons:

> Life settlement transactions are often based upon animpaired insured, whose mortality does not logicallybegin 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 insuredindividuals due to their relative health profiles.

> Insured tables show some deterioration in mortalityduring the select period due to the tendency of healthyindividuals to lapse their insurance coverage, at whichpoint they are removed from the mortality study aswell. Life settlements are characterized by institutionalinvestors with no intention to allow a policy to lapse,thereby maintaining greater homogeneity throughfuture years (i.e., less deterioration).

Mortality multiples vs. flat extras

Mortality multiples (table ratings) and flat extras aretwo distinctly different methods for assessing mortalityrisk. The major differences between these twoapproaches 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 isquite sensitive to the significant increase in expectedmortality at older ages. The graph below comparesexpected mortality for an 80-year-old male nonsmokerat 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.

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Even if the substandard mortality multiple accuratelyreflects the increased risk initially, the impact of themultiple may become excessive as the select rates transition toward ultimate rates. Moreover, to theextent that the underlying mortality table overstatestrue 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 reflectthe actual mortality risk over time.

Understanding the nature of the impairment andaccounting for it correctly is critical in life settlementlife expectancy assessment. Whether this involvesblending mortality multiples with flat extras, gradingoff table ratings over time, or some other innovativeapproach, the key point is that the traditional lifeinsurance underwriting approach may not be adequatein 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 ofthe 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 howmuch variability of life expectancies could be predictedbased 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 isdepicted graphically below. Both conditions have amedian life expectancy of seven years, but theCondition B curve is flatter with fatter tails.

Individuals with Condition B would add more volatilityto a portfolio than those with Condition A. Other medical conditions might have different skew, or insome forms of cancer the curve would be bimodal withtwo relatively discrete humps. If LE estimators addedthis extra dimension to their LE estimates, investorsand actuaries would be better able to project cash flowcharacteristics for a given portfolio.

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In order to test portfolio value sensitivity to mortalitychanges, we created a sample portfolio of 250 policiesand measured value changes using different mortalityassumptions.

The analysis was done on both a probabilistic basisand a stochastic basis.

> Probabilistic — Assumes deaths occur in proportionto the mortality that would be expected from thedirect use of a mortality table and mortality multiple. Death benefits and premiums are thereforereduced proportionately over time. While this doesnot represent the result for any one life, it is usefulto calculate an expected result and is useful forexpected value determination.

> Stochastic — The time each life dies is determinedbased on a number of random trials. In each trial,the date of death is the randomly generated variablewith the same expected value as in the probabilisticvaluation. However, instead of having fractionaldeaths each year based on probabilities, each lifeeither lives or dies based on the results of the randomly generated variables.

VI Mortality Analysis

In stochastic analysis, premiums are terminated for alife when death occurs. Because the time of death israndomly drawn, the value of each policy is likely tobe different in each scenario. As such, a multitude ofscenarios are typically run. Results are typicallyexpressed in ranges that show the average expecta-tion but also confidence limits, such as 90 percent or95 percent confidence, which indicate the percentageof trials that produced a result at that level orgreater. Stochastic analysis is useful to giving rangesof possible values, understanding the range of risksinvolved, and developing confidence intervals toachieving a level of results.

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: 70 percent Male

Smoking: 94 percent Nonsmoker

Life Expectancies: Range from 14 to 161 months. Average is 79 months weighted by policy and 89 months weighted by face amount.

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When applied to 100 percent of the 2001 VBT Selectand Ultimate, Age Nearest Birthday basis, with nomortality improvement (used here as a starting point),this created a mortality multiple for each life. Forexample, one of the lives in the portfolio was a 77-year-old male whose life expectancy was estimated at77 months. The calculated mortality ratio for this person was 403 percent, or roughly four times whatwould be expected using the 2001 VBT directly. Intotal, these loads in the portfolio ranged from 75 percent to 9,000 percent and averaged 470 percent bypolicy and 348 percent by face amount. [We note thatthe maximum multiple appears to be large; it is themathematical 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 theinsurance company’s contesting a claim. We also didnot consider insurance company nonpayment orreduced payment for any reason (such as bankruptcy).

> We did not consider the impact of an insurerincreasing 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 simplicityso that the results would focus on mortality changes.The table below shows the expected range of resultsfor the above described portfolio and using the above

described assumptions. We also tested the results usingseven 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 themortality multiple in the third duration.

> Scenario 3 measures the impact of a mortalityimprovement assumption. In this case, 1 percent improvement per year is utilized.

> Scenario 4 measures the impact if the actual lifeexpectancy is 10 percent longer than that utilized in the pricing.

> Scenario 5 measures the impact if the actual lifeexpectancy is 20 percent longer than that utilized in the pricing.

> Scenario 6 uses the same target life expectancy butapplies a volatility factor to the target. The volatilityfactor is set to allow the actual mortality multiple tovary by using a standard deviation equal to a 100percent mortality load; i.e., the average mortalityload for this portfolio is approximately 350 percent,and we randomly generated a result using a normalcurve with a standard deviation of 30 percent, whichis approximately equal to a 100 percent mortalityload. One standard deviation has results within 250percent to 450 percent, and two standard deviationswould be within 150 percent to 550 percent.Therefore, the expected results are the same, but therange of results varies more than would be producedin the base scenario.

> Scenario 7 uses a combination of the above tests.Here we have used 70 percent of VBT as the under-lying starting mortality, combined with 1 percentannual mortality improvement and combined withthe broader volatility factor described in Scenario 6.

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SAMPLE PORTFOLIO VALUES UNDER VARYING MORTALITY ASSUMPTIONS

PV Future Cash FlowsPortfolio Avg LE Discounted @ 10%(months) (amounts in $millions) IRR

Scenario by by face 90th 90th 95thScenario Description policy amount Expected Percentile Expected Percentile Percentile

Base Run 84 97 $170 $152 10.0% 8.1% 7.7%

1 70% of 2001 VBT 101 116 $125 $107 5.5% 3.7% 3.3%

2 Excess Mortality is Flat Extra 97 110 $145 $136 7.4% 6.2% 5.7%

3 1% Mort Improvementper Annum 90 105 $153 $135 8.2% 6.3% 5.8%

4 Actual LE=110% of Target LE 92 107 $147 $130 7.6% 5.8% 5.3%

5 Actual LE=120% of Target LE 101 117 $126 $108 5.5% 3.7% 3.2%

6 Mortality Rating Volatility 84 97 $170 $143 10.0% 7.2% 6.6%

7 Combination of Scenarios 1, 3 and 6 108 125 $107 $ 89 3.6% 1.7% 1.0%

Each of the results shows the expected value and internal rate of return (IRR) and the stochastic results at the90th percentile. For the IRR, the 95th percentile is also included.

The above shows the effect that the actual mortalityresult can have on the value of the portfolio if it is different from the assumed mortality. Changes in themortality assumptions change the expected results from10 percent to 37 percent depending on the scenario.Expected IRRs are reduced from 10 percent to a rangeof 4 percent to 8 percent under the varying scenarios.

When considering stochastic variation at a 90th percentile (i.e., 90 percent of the scenarios, using thedefined assumptions, are greater or equal to this level),the results illustrated drop by $18 million or 11 percentin the base scenario with an IRR of 8.1 percent. In theother scenarios the 90th percentile ranges from a dropof 20 percent to 48 percent of expected; the IRRs rangefrom 1.7 percent to 7.2 percent.

The above analysis shows possible ranges of potentialresults under several scenarios. Only worse mortalityscenarios are shown consistent with the purpose ofthe analysis; actual life expectancies could also beshorter than anticipated and results would be betterthan expected. Additionally, the above scenarios donot exhaust possibilities. If the underlying mortalitywere to be lower than the assumptions used above orcombinations of the above tests were run, the resultscould be lower and more volatile than shown.

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Life settlement portfolios are relatively new asset classeswith little actual experience available. We recommendthat investors use caution and consider the followingbefore deciding to invest in life settlement portfolios:

> Understand the estimated mortality and gain a level ofconfidence for the expected results and the potentialvariances. The actual mortality of a portfolio is a keydeterminant of its ultimate value, but that will not beknown until the last pool member has died, whichcould be in 30 years or more.

> Understand the life expectancy estimator’s approachto the underlying mortality of the portfolio and theassumptions used for developing and applyingimpairment loads. It is important to also identifywhich mortality table was used, and whether adjust-ments were made for slope, multiplicative or flatextra loads, and mortality improvement.

> Measure life expectancies on existing portfoliosagainst one’s own beliefs of underlying mortality,including the appropriate starting level, considerationof potential mortality improvements, and the wear-off, if any, of impairment loads. Expected portfolioexperience should be measured periodically and compared to actual experience developing deviations,if credible, should be studied for an emerging trend. If actual experience is sufficiently different from expect-ed, updated medical records could be obtained and theportfolio could be re-reviewed for underwriting.

> Recognize that the data that are available are still intheir early durations since purchase, and each poolhas unique characteristics that need to be taken intoaccount. As such, judgement is required to develop an understanding of emerging experience and the ultimate effect on the portfolio value.

VII Conclusions

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VIII About the Authors

Ed Mohoric, FSA, MAAA,Consulting Actuary, Milliman USA.

Mr. Mohoric is a consulting actuary and a principalwith the Philadelphia office of Milliman USA. A member of the firm since 1982, he has consulted forlife and health insurance companies, life settlementcompanies, banks and agencies on projects includinglife, annuity, long term care, and health product development; analysis of life settlement purchases;analysis of direct response and traditional distribution;capital and surplus analysis; risk management andasset/liability analysis; and illustration actuary reviews.His experience also includes extensive work in actuarialappraisals for mergers and acquisitions. Mr. Mohoric,who graduated from Pennsylvania State University witha bachelor’s degree in mathematics, is a Fellow, Societyof Actuaries; Member, American Academy ofActuaries; and Member, International ActuarialAssociation and has served on the Society of Actuaries’Non-Traditional Section Council and the AmericanAcademy of Actuaries’ Life Insurance Task Force. Healso served for three years on Milliman USA’s Board of Directors.

Milliman is among the world’s largest independentactuarial and consulting firms, with revenues of morethan 45 offices worldwide and a staff of 2,000 peoplethat includes more than 900 qualified consultants and actuaries. Through consulting practices in employeebenefits, healthcare, investment, life insurance andfinancial services, and property and casualty insurance,Milliman serves the full spectrum of business, financial, government, union, education, and nonprofitorganizations.

Robert O. Kinney, M.D., FLMI, Vice President and Medical Director,Phoenix Life Solutions.

Dr. Kinney joined The Phoenix Companies, Inc. in1992 as Assistant Medical Director and was namedAssociate Medical Director in 1993 before assuminghis current position in 1997. Dr. Kinney received hisB.A. with high honors in Biology/Psychology fromWesleyan University and received his M.D. from theYale University School of Medicine. He completed hisinternal medicine residency at the University ofMinnesota Affiliated Hospitals and was in private practice in internal medicine at Park Nicollet MedicalCenter in Minneapolis prior to joining Phoenix. Dr. Kinney is board certified in Internal Medicine andInsurance Medicine, and is active in the AmericanAcademy of Insurance Medicine, serving as a facultymember for the Introduction to Insurance MedicineCourse and the Triennial Course in InsuranceMedicine. He also serves on the Risk ClassificationCommittee for the American Council of Life Insurers.

Phoenix Life Solutions was founded in 2007 by ThePhoenix Companies, Inc. (NYSE:PNX) to use its strongcapabilities in managing mortality and longevity to pursue product solutions that respond to the evolvingfinancial needs of its high-net-worth customers. Withroots dating to 1851, Phoenix helps individuals andinstitutions solve their often highly complex personalfinancial and business planning needs through its broadarray of life insurance, annuities and investments.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying,recording or any information storage and retrieval system now known or to be invented, without permission in writing from Phoenix LifeSolutions, Inc., 610 W. Germantown Pike, Suite 150, Plymouth Meeting, PA 19462.

L4973W © 2008 Phoenix Life Solutions, Inc., All rights reserved.Phoenix and its logo are registered service marks of Phoenix Life Inurance Company and are being used by permission.

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