South African Male Mortality Rates...ASSA Male CIA 9704 Last - MC ultimate CIP2014 2001 CDN Population 0% 20% 40% 60% 80% 100% 120% 140% 160% 0 10 20 30 40 50 60 70 80 Male Mortality

0.00050

0.00500

0.05000

0.50000

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qx (l

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Age x

South African Male Mortality Rates

SA85-90 q[x] SA85-90 qxSAIML98 GL05-09 Aggregate MaleGL05-09 Male Light - Salary Band 5 (Best Class) GL05-09 Male Heavy - Salary Band 1 (Worst Class)ASSA Male SA New Gen 03 - 10

5E-05

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Canadian Male Mortality Rates

CIA 9704 Last - MC select 0 CIA 9704 Last - MC ultimate CIP2014 2001 CDN Population 2011 CDN Population

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South Africa versus Canadian Male Mortality Rates

SA85-90 qx SAIML98 ASSA Male CIA 9704 Last - MC ultimate CIP2014 2001 CDN Population

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Male Mortality Rates as a Percentage of South African ASSA2008 Rates

SA85-90 qx SAIML98 ASSA Male CIA 9704 Last - MC ultimate CIP2014 2001 CDN Population

SA 85-90 Assured Lives Mortality Table

The volume of data is as follows:

Exposure 1 287 172 407 058 306 098 2 000 328Deaths 3 544 1 513 802 5 859Exposure 1 008 829 275 248 215 522 1 499 599Deaths 2 870 1 202 585 4 657Exposure 824 037 202 093 169 001 1 195 131Deaths 2 414 995 469 3 878Exposure 7 762 188 702 305 809 398 9 273 891Deaths 58 441 4 600 4 295 67 336

Total Exposure 10 882 226 1 586 704 1 500 019 13 968 949Deaths 67 269 8 310 6 151 81 730

New Generation Mortality 2003 - 2010

Gender DurationTotal

Deaths% of Total

DeathsTotal Central

Exposure

% of Total Central

Exposure0 1 865 21.22% 1 221 258 30.30%1 1 708 19.44% 927 063 23.00%2 1 571 17.88% 679 851 16.86%

3+ 3 643 41.46% 1 203 011 29.84%Total 8 787 78.0% 4 031 183 58.4%

0 465 18.73% 884 873 30.86%1 489 19.69% 662 272 23.10%2 393 15.83% 482 608 16.83%

3+ 1 136 45.75% 837 700 29.21%Total 2 483 22.0% 2 867 454 41.6%

11 270 6 898 637

Total

The SA85-90 mortality tables are based upon data collected by the Actuarial Society of South Africa for the continuous investigation into the mortality of assured lives in the Republic of South Africa.

The experience over the period 1985-1990 represented probably the last which could be assumed to contain negligible AIDS related deaths (and thus provided some sort of benchmark).

The data are in respect of whole life and endowment policies, not lives assured. Policies accepted after a medical examination and those accepted on a non-medical basis are included in the investigation. The experience was divided into two, the first, and largest group, representing the experience of lives from traditional life insurance markets, the second group representing the experience of lives from emerging markets of the most recent years. A table was produced for each group, the first being designated "light" the second, "heavy", and for the experience as a whole, designated SA85-90.

The experience represents the first which was not based solely on White lives.

Light Heavy Other

Duration 0

Duration 1

Duration 2

Duration 3+

Males

Females

Grand Total

<60 60+ Total <60 60+ TotalDeaths 2 270 29 828 32 098 956 8 173 9 129 Exposure 150 968 816 482 967 450 173 196 393 191 566 387 % of Total 16% 84% 31% 69%

Data collected from 6 companiesgraduated rates from age 40 - 95Used SA85-90 rates below age 65 (blended with graduated rates between age 60 & 65)Used Coale and Kisker Method to extrapolate from age 86 to 110.

The data are in terms of numbers of lives and distinguish between the following classes of lives:– voluntary and compulsory purchases of immediate annuities by individuals;– men and women;– annuities within the guarantee period and those beyond this period;– amount of annuity (at year end) in six bands:– less than R1000;– R1000 to R2999–99;– R3000 to R9 999–99;– R10 000 to R29 999–99;– R30 000 to R99 999–99; and– R100 000 and greater.

Salary Band Light Mid Heavy All Light Mid Heavy1 306 814 209 957 103 996 620 767 2 596 2 157 1 302 2 286 415 100 152 63 538 450 105 1 425 629 551 3 427 093 73 303 92 521 592 917 1 034 216 312 4 391 653 56 900 73 558 522 111 748 115 184 5 155 921 22 970 15 688 194 579 226 28 17

Total 1 567 896 463 282 349 301 2 380 479 6 029 3 145 2 366

Central Exposure DeathsIndustry Industry

Male Female

Covers the mortality experience of South African assured lives covered by new generation products (also referred to as pure risk products) from 2003 to 2010.

Mortality of members of Group Schemes in South Africa

SOUTH AFRICAN ANNUITANT STANDARD MORTALITY TABLES 1996–2000 (SAIML98 and SAIFL98)

The main difference between new generation products and their predecessor, universal life products, is that they are risk only products and have no investment element. As such there are no surrender or maturity values payable on these products. In addition, stricter underwriting requirements and standards have been implemented on assessing the risk of individual lives taking out new generation products.

ASSA2008 AIDS MODEL

Projection for 1 July 2004

Total population 47 071 291Total HIV infections 4 997 456

Deaths:S deaths (in the year starting 1 July) 387210

S deaths (in the year starting 1 July) 147907 S deaths (in the year starting 1 July) 167962

S deaths (in the year starting 1 July) 315870Crude extra mortality rate 0.0067

Aids Deaths (to middle of the year) 1161686Adult AIDS deaths (15+) 270002Child AIDS deaths (0-14) 45867

Mortality statisticsInfant Mortality Rate (1000*Q0) 52

Child Mortality Rate (1000*5Q0) 82Adult mortality:

45Q15 0.537145Q15 male 0.5939

45Q15 female 0.4803Life expectancy:

The data was submitted by six South African life insurance companies for compulsory group life insurance over the five-year period from 1 January 2005 to 31 December 2009. The data consist of information on individuals employed in the formal sector aged 20 to 69. The data include information on age, annual salary, industry grouping and sex.

Information on the occupations of individuals was not captured by the life offices and therefore entire group schemes had to be allocated to one of five industry groupings labelled A to E. The five industry groupings were then regrouped and reclassified as light, mid or heavy. Light industries included companies operating in financial services, business administration and other services such as retail, education, healthcare and information technology. Mid industries included light manufacturing and other bluecollar work that does not involve heavy machinery. Heavy industries included companies operating in mining, transport and other heavy manufacturing.

Although data were available for ages 20 to 69, only data for ages 25 to 65 were used because of the sparseness of data outside this range when accounting for sex, industry group and salary band. To provide a summary of the relative size of the dataset for ages 25 to 65, the total central exposure was 4396388 for males and 2380479 for females and total deaths was 42925 for males and 1154 for females.

The model makes provisions for migration of the population, separate modeling of racial groups, and separate modeling of provincial populations

The Actuarial Society of South Africa HIV/AIDS models, also known as ASSA AIDS models, are a series of mathematical models developed to assist the actuarial profession and the Actuarial Society of South Africa in assessing and addressing the

impact of the HIV and AIDS epidemic in South Africa

e0 51.4e0 male 49.3

e0 female 53.5e20 37.2

e20 male 35.2e20 female 39.2

e65 13.3e65 male 11.6

e65 female 14.8

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South African Male Mortality Rates (All Ages)


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South African Male Mortality Rates (Ages 16 - 65)


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South African Male Mortality Rates (Ages 60+)


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South African Male Rates as a % of ASSA Rates

SA85-90 q[x] SA85-90 qxSAIML98 GL05-09 Aggregate MaleGL05-09 Male Light - Salary Band 5 (Best Class) GL05-09 Male Heavy - Salary Band 1 (Worst Class)ASSA Male

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South African Female Mortality Rates (All Ages)

45% of SA85-90 q[x] 45% of SA85-90 qxSAIFL98 GL05-09 Aggregate FemaleGL05-09 Female Light - Salary Band 5 (Best Class) GL05-09 Female Heavy - Salary Band 1 (Worst Class)ASSA Female SA New Gen 03 - 10

0.00030

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South African Female Mortality Rates (Ages 15 - 65)


0.00300

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South African Female Mortality Rates (Ages 60+)


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South African Female Rates as a % of ASSA Rates

45% of SA85-90 q[x] 45% of SA85-90 qxSAIFL98 GL05-09 Aggregate FemaleGL05-09 Female Light - Salary Band 5 (Best Class) GL05-09 Female Heavy - Salary Band 1 (Worst Class)ASSA Female

1997-04 Canadian Institute of Actuaries (CIA) Mortality Table

Death Claims Exposures Death Claims ExposuresMales 136 804 24 136 445 3 645 771 1 846 342 869 Females 72 871 20 093 443 1 234 103 1 109 398 591 Total 209 675 44 229 888 4 879 874 2 955 741 460

Number Amounts

Experience collected from 16 companies.

Methodology: The raw mortality rates were computed based on amount of insurance. These rates were calculated using the following formula: qx=Dx/(Ex) where Dx represents the total death benefits for the persons that died between age x and x+1 and where Ex represents the total exposure amounts of those insured at age x.

The use of credibility theory to graduate the table was evaluated but the size of the data at various ages and duration periods was insufficient to allow the use of the methods to compute the mortality rates.

Standard graduation methods were analyzed; a Whittaker-Henderson model provided the most satisfactory graduation method. The method was applied separately for Male and Female, and for the different smoking status. The Whittaker-Henderson method was used for ages up to age 80 as data was sparse at the older ages. Due to the scarcity of data at advanced ages, population mortality data was used.

The method used to incorporate the population data into the insured data was adapted from the approach taken by the Society of Actuaries (SOA) to derive the VBT2008 tables (See SOA Table Identities 996-1007) The graduated ultimate mortality rates from ages 80 to 90 were made to converge linearly to the Canadian Life Tables for male and female status produced by Statistics Canada for the same observed period, with minor smoothing around age 93 to eliminate irregularities in the raw population data.

After age 90, only population data is used to derive mortality rates. The omega was set to age 120, that is, q (120) = 1.00. Once the ultimate mortality rates were computed, the select rates were calculated. The relationship between the first year mortality and the ultimate mortality at the same attained age was computed. Next the parameters σ, β, φ , γ, and δ were calculated. This analysis was performed for males and females only due to lack of data for the subclasses.

To complete the select tables, interpolation was done using a method devised by Panjer and Tan (1995). Once the tables were constructed, the ratio between actual and tabular death benefit was calculated. In order to have a ratio close to one, each graduated mortality table was adjusted by a factor. Finally, the inequalities for each age and duration between the smoker, non-smoker and combined rate (Non-Smoker ≤ Combined ≤ Smoker) were verified. Whenever the inequalities were not verified, the smoker or non-smoker rates were replaced by the combined rates. This was done for less than 200 values out of more than 5,000. The age nearest tables have been constructed from the age last tables using a formula due to the scarcity of age nearest data:

Canadian Insured Payout Mortality Table 2014 (CIP2014)

Policies Income Policies IncomeMale 1 433 571 5 955 985 761 113 761 425 881 433 Female 1 870 994 7 174 487 900 106 867 363 970 096 Total 3 304 565 13 130 473 661 220 628 789 851 529

The table is constructed from both single and joint data, adjusted for IBNR to 2011.

Canadian Population 2001 and 2011

The table is constructed from data by attained age, without regard to duration from issue.

CIP2014 is constructed using data for ages 70–100 and for years of experience 2000–2011.

The recently published CPM2014 serves this purpose. The rates for ages 106 and higher are taken from CPM2014. The rates for ages 99–105 are calculated by fitting a 4th degree polynomial to the rates for ages 96, 97, 98, 106, and 107. It should be noted that the study underlying CPM2014 did not have enough data over 100 either. It used therates from a paper by Howard presented at the Living to 100 Symposium in 2011.

The approach for the younger ages is similar. The youngest graduated rate to be retained is the rate for age 73. The rates for ages 18–65 are taken directly from CPM2014. The intervening rates are calculated by fitting a 5th degree polynomial to the rates for age 63, 64, 65, 73, 74, and 75. CPM2014 and the graduated rates are very close together around age 73 for males, and 5% apart for females. It would not be unreasonable to use 105% of CPM2014 for females, but 100% is used for consistency with the males.

The data underlying CPM2014 were credible below age 70, but not below age 55. Rates under 54 were set to a multiple of CIA9704 ultimate non-smoker, and rates for ages 54– 60 were interpolated.

It is based on data of the CIA Individual Annuitant Mortality Study for years of experience 2000–2011, but excluding data for policies with annual income of $72,000 or more. The rates for ages younger and older than those for which there are sufficient data were obtained from the CPM2014 mortality tables

Exposed Deaths

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Canadian Male Mortality Rates (All Ages)


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Canadian Male Mortality Rates (Ages 15 - 65)


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Canadian Male Mortality Rates (Ages 60+)


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Canadian Male Mortality Rates as a Percentage of 2001 Population


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Canadian Female Mortality Rates (All Ages)

CIA 9704 Last - FC select 0 CIA 9704 Last - FC ultimate CIP2014 2001 CDN Population 2011 CDN Population

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Canadian Female Mortality Rates (Ages 15 - 65)


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Canadian Female Mortality Rates (Ages 60+)


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Canadian Female Mortality Rates as a Percentage of 2001 Population


South African Male Mortality Rates...ASSA Male CIA 9704 Last - MC ultimate CIP2014 2001 CDN Population 0% 20% 40% 60% 80% 100% 120% 140% 160% 0 10 20 30 40 50 60 70 80 Male Mortality

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