1156 Shure Dr., Suite 130
 Arlington Heights, IL 60004
 Tel+1 847.483.9403
  April 30, 2012
  Steven Sabes Chief Operating Officer
 GWG Life
 220 South Sixth Street, Suite 1200
 Minneapolis, MN 55402
  Steven:
  You have asked that we prepare a quarterly valuation of a GWG portfolio of life insurance settlements. The valuations were prepared under the assumptions described below, which you provided in phone conversations and e-mails. We utilized the policy portfolio data you provided to project potential cash flows monthly following the 3/31/2012valuation date, based on the premiums and other values in the policy records provided.
  Data Reliance
  In preparing these valuations, we relied upon:
  ●
Policy Data – We have relied on the portfolio file as provided to us by GWG Life. We assume that this file and the policy data contained in it was prepared accurately and reflects current company supported product performance. The 3/31/2012file contained 181policies with total face amounts of $482,455,081. This was a net increase of 6policies and $6,050,000face amount over the 12/31/2011portfolio file.
  ●
Future Premiums Data – We have relied on GWG Life’s data regarding the future premiums to be paid on each policy. It is our understanding that GWG Life uses the MAPS software package along with data gathered from the actual premium payments to the life insurance carriers for each policy for projecting future minimum premium streams.
  ●
Life Expectancy, Values, plus any adjustments – We have relied on the life expectancy values provided by GWG Life. It is our understanding that GWG Life obtained these LE values using the following industry experts: 21stServices, AVS Underwriting, Fasano Associates, and/or ISC Services.
  Results
  Using the assumptions stated below, we calculated the net present values as of the valuation dates using the specified discount rates of 13.45% and 4.75%.These results will be e-mailed to you in the Excel reports generated, including a Portfolio Summary and List of Policies, from the MAPS Portfolio valuation model.
   GWG Portfolio as of 3/31/2012Number of Policies:
181Total Net Death Benefit:
$482,455,081Discount Rate:
13.45%
4.75%
12.00%
15.00%Expected Net Present Value:
$128,112,092
$220,843,554
$138,944,154
$117,892,314Stochastic Analysis - 10,000 Scenarios95%ile Net Present Value:
$106,278,534
$197,967,664
$116,750,789
$96,411,65995% CTE Net Present Value:
$100,361,937
$191,699,780
$110,789,222
$90,602,096
    
  The Expected Net Present Value is the probabilistic average value of the portfolio. These values are calculated actuarially; assuming that the amount of premiums paid and death benefits received are proportional to the probabilities of survival. Since death will occur at an unknown discrete point in time, the actual return for a policy, and a portfolio of policies, may vary significantly from the Expected Value.
  The Stochastic analysis can return information about the range of values that might be achieved along with probabilities that the results might be better or worse than the Expected or a specified level. The Stochastic analysis creates random scenarios where a discrete date of death is independently projected for each life based on its mortality curve. The net present value of the portfolio for each scenario is calculated as the present value of projected death benefits minus the present value of projected premiums. The scenarios are ranked by value. The 95thPercentile Net Present Value is the portfolio value exceeded by 95% of the stochastic scenarios. The 95% CTE is the Contingent Tail Expectation for the 95thpercentile, and is the average of the 5% of scenarios with the lowest net present value.
  The valuations (1) do not include premiums paid before the valuation date, and (2) assume that the insured remains alive at the valuation date. The valuations also assume that the policy COIs and policy expense charges remain at current levels in the future. If these charges are increased, the projected values would decrease. The above values do not consider any federal income or other taxes.
  Summary of MAPS Model Settings and Assumptions
  We used the following assumptions as discussed with you:
  ·
Insurance Policy Characteristics:Per portfolio data file as provided.
  ·
Policy Issue Date:Per portfolio data file as provided.
  ·
Insured Date of Birth and Gender:Per portfolio data file as provided.
  ·
Extended Death Benefit After Policy Maturity Age:Per portfolio data file as provided.
  ·
Optimized Premium Levels and Timing:Monthly premiums unadjusted per the portfolio data file as provided.
  ·
Per Policy and Portfolio Administrative Expenses: None, per the portfolio data file as provided.
  ·
Collection of Death Benefit Delay:0 months, with 0.00% statutory interest credited.
  ·
Mortality:2008 VBT Select & Ultimate Primary tables, by Age, Sex, and Tobacco Use.
  ·
Age Basis:Age Nearest Birthday.
  ·
Mortality Improvement:None.
  ·
Life Expectancy:One blended LE and corresponding UW effective date per life in the portfolio data file as provided.
  ·
Adjustment Applied to Stated LE:None.
  ·
Improvement Used by Underwriters:No.
  ·
Valuation Discount Interest Rates:13.45% and 4.75% as specified, plus 12%, 15%, 16.50%, and 18%.
  ·
Number of Stochastic Scenarios:10,000.
  ·
Stochastic Percentile Ranks and Contingent Tail Expectations:95%, with additional reporting at 75%, 85%, 90%, 97%, and 99%.
  Notice on Mortality and Volatility
  Parties engaged in life settlements commonly obtain and use "life expectancies" in their considerations. While life expectancies are provided for individuals, they are developed from expected patterns of mortality of large groups of similar individuals. No one knows exactly when any one individual will die, nor is a life expectancy intended to
  
  suggest the time until death will be near the life expectancy. Any one individual may live much longer than his or her estimated life expectancy or that projected by applying a mortality rating to any particular mortality table. Even for a large group of lives, the actual mortality for the group may be less than expected for a variety of reasons (such as improvements in medical technology, unanticipated general mortality improvement, or misestimation of the life expectancy). Stochastic simulation and sensitivity testing can help to quantify these risks, but such tests should not be interpreted as a guarantee of any particular financial outcome. Investors will earn less than expected on the policy of any individual who lives longer than his life expectancy.
  Background
  Model Actuarial Pricing Systems, LP is a subsidiary of Cantor Fitzgerald (a leading global financial services firm) providing life settlement software and services worldwide to a variety of customers including life settlement brokers, providers, consultants and investors.
  Since its inception in late 1990’s, the MAPS Single Policy Valuation Model, developed by Milliman, Inc., has been the industry standard life settlement valuation model for both single life and joint life insurance contracts. Incorporating sophisticated analysis and valuation algorithms, the MAPS Model transformed the life settlements industry by providing actuarially correct valuation of the premium and death benefit cash flows associated with a life settlement transaction.
  I am an Associate of the Society of Actuaries, and a Member of the American Academy of Actuaries. I have extensive experience in testing and verifying the MAPS model, as well as using it for client valuation projects. Prior to my employment with MAPS, I worked for a US life insurance company supervising the actuarial department’s policy valuation and financial reporting processes.
  Very truly yours,
  
 Jonathan Close, ASA, MAAA 
Actuary