Enterprise Risk Management and Stochastic … and Actuarial Advisory Services Insurance and Actuarial Advisory Services Enterprise Risk Management and Stochastic Embedded Value Modeling

Insurance and ActuarialAdvisory Services


Enterprise Risk Management and Stochastic Embedded Value ModelingEnterprise Risk Management and Stochastic Embedded Value Modeling

ALM Joint Regional Seminar, June 27, 2005 – July 4, 2005

Jonathan Zhao, FSA, FCIA, MAAA, MCA

ALM Joint Regional Seminar, June 27, 2005 – July 4, 2005

Jonathan Zhao, FSA, FCIA, MAAA, MCA

1

AgendaAgenda

• Need for Enterprise Risk Management and Modeling

• Embedded Value Concept

• European Embedded Value and CFO Forum Principles

• Stochastic EV Modeling Case Study

• Practitioners Guide & Closing Remarks

• Need for Enterprise Risk Management and Modeling

• Embedded Value Concept

• European Embedded Value and CFO Forum Principles

• Stochastic EV Modeling Case Study

• Practitioners Guide & Closing Remarks

2

Throughout history the industry has misjudged the behavior of its

environment, distribution channels and customers.

Throughout history the industry has misjudged the behavior of its

environment, distribution channels and customers.

• The unbundling of life insurance with the introduction of universal life policies

• “Vanishing premium” concept and no lapse guarantees• Single premium capital guaranteed products backed by inappropriate

assets• Embedded guarantees (e.g., GMDB, GMAB, GMWB) in variable and

unit-link products

• The unbundling of life insurance with the introduction of universal life policies

• “Vanishing premium” concept and no lapse guarantees• Single premium capital guaranteed products backed by inappropriate

assets• Embedded guarantees (e.g., GMDB, GMAB, GMWB) in variable and

unit-link products

“Adverse” customer behavior has cost the industry dearly in terms of lost profits.Enterprise risk management and modeling is now necessary in order to run the business.

“Adverse” customer behavior has cost the industry dearly in terms of lost profits.Enterprise risk management and modeling is now necessary in order to run the business.

3

Enterprise Risk Management Enterprise Risk Management

• Risk is an event or condition that impairs the organization’s ability to meet its goals

• Risk Management is a process of balancing the consequences of risk with the cost of avoiding it or hedging it

• Enterprise Risk Management is risk management that considers all the enterprise’s various business in aggregate and by considering the correlation of the risks

• Risk is an event or condition that impairs the organization’s ability to meet its goals

• Risk Management is a process of balancing the consequences of risk with the cost of avoiding it or hedging it

• Enterprise Risk Management is risk management that considers all the enterprise’s various business in aggregate and by considering the correlation of the risks

Enterprise Risk Modeling is the part of the enterprise risk management that you can model and quantify, and use as a benchmark to take appropriate actions

Enterprise Risk Modeling is the part of the enterprise risk management that you can model and quantify, and use as a benchmark to take appropriate actions



Enterprise Risk ModelingEnterprise Risk Modeling

5

Enterprise Risk Modeling Follows a Logical PathEnterprise Risk Modeling Follows a Logical Path

ResponseAvoidMinimizeMitigate risks

Qualify and PrioritizeUse actuarial tools Management judgment

ExploitOptimize RiskOther competitive advantages

Catalog & Identify– Enterprise concerns

& exposures– Universe of risks

• Catalog & Identify

• Qualify and Prioritize

• Response

• Exploit

• Catalog & Identify

• Qualify and Prioritize

• Response

• Exploit

6

Catalog the Universe of RisksCatalog the Universe of Risks

• Financial– Interest rates, equity market volatility, credit, liquidity

• Business– Mortality, morbidity, economic activity, management failure

• Operational– Process failure, fraud, litigation

• Event– Regulatory change, war, natural disaster

• Financial– Interest rates, equity market volatility, credit, liquidity

• Business– Mortality, morbidity, economic activity, management failure

• Operational– Process failure, fraud, litigation

• Event– Regulatory change, war, natural disaster

Assess the organization’s risk tolerance realistically. “What risk levels can we take on comfortably?”

Assess the organization’s risk tolerance realistically. “What risk levels can we take on comfortably?”

7

Quantify and prioritizeQuantify and prioritize

• Determine tolerance level

• Create accountability

– Who’s on first?

• Evaluate RBC, rating agency perspectives

– To see ourselves as others see us

• Use available resources first, such as cash flow testing models

– Do better, if possible

• Determine tolerance level

• Create accountability

– Who’s on first?

• Evaluate RBC, rating agency perspectives

– To see ourselves as others see us

• Use available resources first, such as cash flow testing models

– Do better, if possible

The internal and external audit functions may be valuable in this processThe internal and external audit functions may be valuable in this process

8

Respond - defensively firstRespond - defensively first

• Avoid• Avoid

• Diversify• Diversify

• Reinsure

• Offset

• Hedge

• Strengthen capital

• Reinsure

• Offset

• Hedge

• Strengthen capital

Monitor continuously …Monitor continuously …

9

Exploit!Exploit!

• Risk management is a tool for competitive advantage:

– PRICING PRECISION

– SELF HEDGING

– PRODUCT RATIONING

– NICHE EXPANSION

– PRICING ARBITRAGE

– OTHER– OTHER

• Risk management is a tool for competitive advantage:

– PRICING PRECISION

– SELF HEDGING

– PRODUCT RATIONING

– NICHE EXPANSION

– PRICING ARBITRAGE

10

Enterprise Risk Modeling needs a “Global” Financial Reporting

and Measurement Framework

Enterprise Risk Modeling needs a “Global” Financial Reporting

and Measurement Framework

• Actuarially robust

• Reflects local reserving, capital requirements and taxation

• Acts as an early warning system

• Reconciles with product pricing

• Allows for enterprise consolidation

• Actuarially robust

• Reflects local reserving, capital requirements and taxation

• Acts as an early warning system

• Reconciles with product pricing

• Allows for enterprise consolidation

Embedded Value reporting is that framework …Embedded Value reporting is that framework …



Embedded ValueEmbedded Value

12

Embedded Value … measures management’s added value.Embedded Value … measures management’s added value.

• Embedded Value

– Use of Embedded Value

– Basics, Deterministic vs. Stochastic EV

– European Embedded Value (EEV) Principles

• Embedded Value at Risk (EV@Risk)

– Financial statements that help optimize risk

• Embedded Value

– Use of Embedded Value

– Basics, Deterministic vs. Stochastic EV

– European Embedded Value (EEV) Principles

• Embedded Value at Risk (EV@Risk)

– Financial statements that help optimize risk

13

Uses of Embedded ValueUses of Embedded Value

• Published financials (China, UK, Europe)

• Incentive compensation (China, US)

• Information for rating agencies, analysts (Canada)

• Pricing and planning validation (Global)

• Enterprise risk modeling (New)

• Published financials (China, UK, Europe)

• Incentive compensation (China, US)

• Information for rating agencies, analysts (Canada)

• Pricing and planning validation (Global)

• Enterprise risk modeling (New)

14

Basic Components of Embedded ValueBasic Components of Embedded Value

Actuarial

Appraisal

Value

Actuarial

Appraisal

Value

Value ofFuture

Business

Value ofFuture

Business

AdjustedNet WorthAdjusted

Net Worth

Value ofInforce

Business

Value ofInforce

Business Embedded

Value

Embedded

Value

15

Value of InforceValue of Inforce

• Present Value (PV) of Distributable Earnings

= PV after-tax book profits

– PV Cost of Capital

+ Target Surplus (TS) at the valuation date

• Distributable Earnings (year t)

= After-tax Book Profit (year t)

+ TS Released (year t)

+ After-tax earnings on TS (year t)

• Present Value (PV) of Distributable Earnings

= PV after-tax book profits

– PV Cost of Capital

+ Target Surplus (TS) at the valuation date

• Distributable Earnings (year t)

= After-tax Book Profit (year t)

+ TS Released (year t)

+ After-tax earnings on TS (year t)

Allocate assets to business lines, project cash flows using best estimate assumptions, include statutory reserves, discount distributable earnings to

present value at a hurdle rate reflecting risk

Allocate assets to business lines, project cash flows using best estimate assumptions, include statutory reserves, discount distributable earnings to

present value at a hurdle rate reflecting risk

16

PV Cost of CapitalPV Cost of Capital

• Timing difference between TS now and future TS releases net of after-tax investment earnings on assets backing TS

= Target Surplus

– PV Future Target Surplus Released

– PV Future after-tax earnings on assets backing TS

• Timing difference between TS now and future TS releases net of after-tax investment earnings on assets backing TS

= Target Surplus

– PV Future Target Surplus Released

– PV Future after-tax earnings on assets backing TS

17

Adjusted Net WorthAdjusted Net Worth

= Statutory capital and surplus

+ Allocations of surplus, such as AVR

+ Non-admitted assets with realizable value

+ Reduced for the value of any obligations not considered in the value of inforce

+ Adjustment for difference between market value and book value of assets backing adjusted net worth

= Statutory capital and surplus

+ Allocations of surplus, such as AVR

+ Non-admitted assets with realizable value

+ Reduced for the value of any obligations not considered in the value of inforce

+ Adjustment for difference between market value and book value of assets backing adjusted net worth

Target surplus is usually included in adjusted net worth for presentation purposesTarget surplus is usually included in adjusted net worth for presentation purposes

18

Embedded Value Change = “Value Added” or “Achieved Profits”

Embedded Value Change = “Value Added” or “Achieved Profits”

AdjustedNet

Worth

AdjustedNet

Worth

Valueof Inforce

Valueof Inforce

S/H DividendS/H Dividend

AdjustedNet WorthAdjusted

Net Worth

Value fromNew SalesValue fromNew Sales

Value ofSurvivingInforce

Value ofSurvivingInforce

Achieved Profit Amount

Achieved Profit Amount

EndingEmbedded

Value

EndingEmbedded

ValueStarting

EmbeddedValue

StartingEmbedded

Value

19

Deterministic EVDeterministic EV

• Best estimate assumptions

• Reflects overall risks in the discount rate – higher discount rate for riskier products

• Easy to implement and understand

– Can often use the existing actuarial model

• However,

– Focused primarily on interest-rate risk

– Does not reflect tail exposure

– Unable to measure the interaction of risks

• Best estimate assumptions

• Reflects overall risks in the discount rate – higher discount rate for riskier products

• Easy to implement and understand

– Can often use the existing actuarial model

• However,

– Focused primarily on interest-rate risk

– Does not reflect tail exposure

– Unable to measure the interaction of risks

20

Stochastic EVStochastic EV

• Enables companies to capture the interaction of risks

• Quantifies risks (total enterprise basis & by line of business)

• Helps management to determine a comfortable level of risk and tooptimize the risk/reward relationships

• An approach that distinguishes a company from its competitors in terms of enterprise risk measurement and management

• Enables companies to capture the interaction of risks

• Quantifies risks (total enterprise basis & by line of business)

• Helps management to determine a comfortable level of risk and tooptimize the risk/reward relationships

• An approach that distinguishes a company from its competitors in terms of enterprise risk measurement and management

21

What is Stochastic EV?What is Stochastic EV?

• Examples of simple application– Various interest rate scenarios used in asset adequacy testing

– Sensitivity testing (e.g., increasing lapse rate, lower mortality rate)

• Formal stochastic approach– Identify risk elements (e.g., interest, mortality, and default)

– Use a stochastic process to define a range of selected risk elements (e.g., Monte Carlo, Economic Scenario Generator)

– Run EV model over a range for selected risk elements

• Start with a deterministic model

• Stochastic Assumption = deterministic assumption x stochastically generated factor

• Examples of simple application– Various interest rate scenarios used in asset adequacy testing

– Sensitivity testing (e.g., increasing lapse rate, lower mortality rate)

• Formal stochastic approach– Identify risk elements (e.g., interest, mortality, and default)

– Use a stochastic process to define a range of selected risk elements (e.g., Monte Carlo, Economic Scenario Generator)

– Run EV model over a range for selected risk elements

• Start with a deterministic model

• Stochastic Assumption = deterministic assumption x stochastically generated factor



European Embedded Value PrinciplesEuropean Embedded Value Principles

23

European Embedded Value PrinciplesEuropean Embedded Value Principles

• The CFO Forum launched European Embedded Value (EEV) Principles in 2003

• EEV principles are aimed to provide transparent and consistent financial information to the investors across major European insurance companies

• The CFO Forum launched European Embedded Value (EEV) Principles in 2003

• EEV principles are aimed to provide transparent and consistent financial information to the investors across major European insurance companies

24

European Embedded Value Principles - continuesEuropean Embedded Value Principles - continues

25

Embedded Value at Risk ConceptEmbedded Value at Risk Concept

• EV@Risk TM : Difference between the mean EV value and the fifth percentile EV for each risk element (other levels ofEV@Risk TM could also be used)

• Shows variance in EV over a range of economic and non-economic scenarios

– Quantifies impact to EV for each individual risk elements and in aggregate

– Demonstrates the correlation effect between different risk elements (i.e., sum of individual risk components is greater than when all the risks are run together)

• Allows management to determine a comfortable level of risk

• Requires stochastic EV modeling in order to determine different levels of EV@Risk TM

• EV@Risk TM : Difference between the mean EV value and the fifth percentile EV for each risk element (other levels ofEV@Risk TM could also be used)

• Shows variance in EV over a range of economic and non-economic scenarios

– Quantifies impact to EV for each individual risk elements and in aggregate

– Demonstrates the correlation effect between different risk elements (i.e., sum of individual risk components is greater than when all the risks are run together)

• Allows management to determine a comfortable level of risk

• Requires stochastic EV modeling in order to determine different levels of EV@Risk TM

26

EV@Risk™EV@Risk™• Embedded Value at Risk is similar to “VAR”

• Difference between mean and 5th percentile

• Embedded Value at Risk is similar to “VAR”

• Difference between mean and 5th percentile

Universal Life - Comparison of Stochastic Runs

$796

$798

$800

$802

$804

$806

$808

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

Embe

dded

Val

ue a

t 12/

31/0

1 (m

)

Stoch Lapse Only

Stoch Mortality Only

Stoch Lapse &MortalityBase Run

5th Percentile5th Percentile

MeanMean



Stochastic EV Case StudyStochastic EV Case StudyFor a Sample Universal Life ProductFor a Sample Universal Life Product

28

Ernst & Young Stochastic EV Case Study for ACLIErnst & Young Stochastic EV Case Study for ACLI

• Universal Life (UL) product from the Ernst & Young deterministic EV case study developed for the American Council of Life Insurer (ACLI) CFO Roundtable

• UL risk elements selected– Interest

– Mortality

– Asset Default

• Used 100 iterations for each of the risk elements– Too many = long run time

– Too few = reduce accuracy

– Experience and testing will help identify optimal number of runs

• Universal Life (UL) product from the Ernst & Young deterministic EV case study developed for the American Council of Life Insurer (ACLI) CFO Roundtable

• UL risk elements selected– Interest

– Mortality

– Asset Default

• Used 100 iterations for each of the risk elements– Too many = long run time

– Too few = reduce accuracy

– Experience and testing will help identify optimal number of runs

29

Generating UL Stochastic Risk AssumptionsGenerating UL Stochastic Risk Assumptions

• The stochastic assumptions are generated for each risk element as follows:

– Stochastic Assumption (other than stochastic interest) = Deterministic Assumption x Stochastic Generated Factor

– Distribution Assumption = Normal Distribution

• Stochastic Interest– Economic Interest Rate scenarios were generated using Ernst & Young Economic

Scenario Engine (ESE)

• Stochastic Mortality– Mean =1, Standard Deviation = 5%, – Max = 2, Min = 0.5

• Stochastic Asset Default– Mean =1, Standard Deviation = 100%, – Max = 4, Min = 0

• The stochastic assumptions are generated for each risk element as follows:

– Stochastic Assumption (other than stochastic interest) = Deterministic Assumption x Stochastic Generated Factor

– Distribution Assumption = Normal Distribution

• Stochastic Interest– Economic Interest Rate scenarios were generated using Ernst & Young Economic

Scenario Engine (ESE)

• Stochastic Mortality– Mean =1, Standard Deviation = 5%, – Max = 2, Min = 0.5

• Stochastic Asset Default– Mean =1, Standard Deviation = 100%, – Max = 4, Min = 0

30

Results of Ernst & Young Universal Life ModelResults of Ernst & Young Universal Life Model

Deterministic Financial Results

• GAAP Earnings for 2004 $ 179,232• GAAP Earnings 2004-2007 $ 778,388*• Embedded Value 12/31/2004 $ 1,324,758*• Embedded Value without Target Surplus $ 561,872*

* Discounted @ 9.00%

Deterministic Financial Results

• GAAP Earnings for 2004 $ 179,232• GAAP Earnings 2004-2007 $ 778,388*• Embedded Value 12/31/2004 $ 1,324,758*• Embedded Value without Target Surplus $ 561,872*

* Discounted @ 9.00%

31

Results of Ernst & Young Universal Life ModelStochastic Mortality Results


Deterministic Mean EaRisk 5th 25th 50th 75th 95thGAAP Eanings 2004 179,232$ 178,764$ 4,112$ 174,652$ 177,198$ 178,679$ 180,672$ 182,711$ GAAP Earnings 2004-2007 778,388$ 777,142$ 8,756$ 768,387$ 773,511$ 777,888$ 780,208$ 785,330$ Embedded Value 1,324,758$ 1,321,195$ 56,637$ 1,264,557$ 1,298,890$ 1,319,582$ 1,344,527$ 1,377,207$ Embedded Value w/o TS 561,872$ 558,309$ 56,637$ 501,672$ 536,004$ 556,696$ 581,641$ 614,321$

PercentileUniversal Life Results - Stochastic Mortality

Observations:2004 GAAP earnings at risk is about 2.3% versus the deterministic results while EV@Risk TM is about 4.3%

EV@Risk TM is 10.1% when calculated without target surplus

Observations:2004 GAAP earnings at risk is about 2.3% versus the deterministic results while EV@Risk TM is about 4.3%


32



Universal Life - Stochastic Mortality

(200,000)

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

0.01 0.11 0.21 0.31 0.41 0.51 0.61 0.71 0.81 0.91

Valu

e of

Exi

stin

g Bu

sine

ss

Stochastic Mortality Deterministic

Result % of MeanMean 558,309 100.00%Median 556,696 99.71%Minimum 484,634 86.80%Maximum 653,832 117.11%Deterministic 561,872 100.64%

Statistics

Result % of Mean5th 501,672 89.86%25th 536,004 96.00%50th 556,696 99.71%75th 581,641 104.18%95th 614,321 110.03%

Percentile

33

Results of Ernst & Young Universal Life ModelStochastic Interest Results


Deterministic Mean EaRisk 5th 25th 50th 75th 95thGAAP Eanings 2004 179,232$ 179,014$ 56$ 178,958$ 178,996$ 179,018$ 179,034$ 179,077$ GAAP Earnings 2004-2007 778,388$ 777,616$ 1,362$ 776,254$ 777,206$ 777,605$ 778,256$ 779,014$ Embedded Value 1,324,758$ 1,324,485$ 398,295$ 926,190$ 1,226,844$ 1,384,108$ 1,463,875$ 1,538,830$ Embedded Value w/o TS 561,872$ 561,599$ 398,295$ 163,304$ 463,958$ 621,222$ 700,990$ 775,944$

Universal Life Results - Stochastic InterestPercentile

Observations:2004 GAAP earnings at risk is about 0.0% versus the mean results while EV@Risk TM is about 30.1%.




34



Universal Life - Stochastic Interest

(200,000)

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

0.01 0.12 0.22 0.33 0.43 0.54 0.64 0.75 0.85 0.96

Valu

e of

Exi

stin

g Bu

sine

ss

Stochastic Interest Deterministic

Result % of MeanMean 561,599 100.00%Median 621,222 110.62%Minimum (142,731) -25.42%Maximum 879,532 156.61%Deterministic 561,872 100.05%

Statistics


Percentile

35

Results of Ernst & Young Universal Life ModelStochastic Default Results


Deterministic Mean EaRisk 5th 25th 50th 75th 95thGAAP Eanings 2004 179,232$ 178,931$ 2,349$ 176,582$ 177,572$ 178,645$ 180,024$ 182,448$ GAAP Earnings 2004-2007 778,388$ 777,722$ 9,758$ 767,964$ 774,227$ 777,732$ 782,375$ 786,094$ Embedded Value 1,324,758$ 1,320,977$ 213,532$ 1,107,445$ 1,234,911$ 1,314,282$ 1,443,076$ 1,554,656$ Embedded Value w/o TS 561,872$ 558,091$ 213,532$ 344,560$ 472,025$ 551,397$ 680,191$ 791,770$

Universal Life Results - Stochastic DefaultPercentile





36



Universal Life - Stochastic Default

(200,000)

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

0.01 0.11 0.21 0.31 0.41 0.51 0.61 0.71 0.81 0.91

Valu

e of

Exi

stin

g Bu

sine

ss

Stochastic Default Deterministic

Result % of MeanMean 558,091 100.00%Median 551,397 98.80%Minimum 244,159 43.75%Maximum 828,807 148.51%Deterministic 561,872 100.68%

Statistics


Percentile

37

Results of Ernst & Young Universal Life ModelStochastic All Results


Deterministic Mean EaRisk 5th 25th 50th 75th 95thGAAP Eanings 2004 179,232$ 179,116$ 5,254$ 173,862$ 177,220$ 179,126$ 180,976$ 184,316$ GAAP Earnings 2004-2007 778,388$ 778,128$ 14,723$ 763,404$ 772,176$ 778,936$ 785,524$ 788,847$ Embedded Value 1,324,758$ 1,323,899$ 369,460$ 954,438$ 1,137,281$ 1,337,677$ 1,514,501$ 1,692,022$ Embedded Value w/o TS 561,872$ 561,013$ 369,460$ 191,553$ 374,396$ 574,791$ 751,616$ 929,136$

Universal Life Results - Stochastic AllPercentile





38



Universal Life - Stochastic All

(200,000)

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

0.01 0.11 0.22 0.32 0.43 0.53 0.64 0.74 0.84 0.95

Val

ue o

f Exi

stin

g B

usin

ess

Stochastic Interest/Mortality/Defaults Deterministic

Result % of MeanMean 561,013 100.00%Median 574,791 102.46%Minimum (115,201) -20.53%Maximum 1,070,196 190.76%Deterministic 561,872 100.15%

Statistics


Percentile

39

Results of Ernst & Young Universal Life ModelSummary of Stochastic Results

Results of Ernst & Young Universal Life ModelSummary of Stochastic Results

Percentile All Interest Mortality Defaults5th 173,862 178,958 174,652 176,58225th 177,220 178,996 177,198 177,57250th 179,126 179,018 178,679 178,64575th 180,976 179,034 180,672 180,02495th 184,316 179,077 182,711 182,448

Mean 179,116 179,014 178,764 178,931EaRisk 5,254 56 4,112 2,349

Correlation (1,264) Deterministic 179,232

2004 Operating Earnings - Universal Life

Percentile All Interest Mortality Defaults5th 954,438 926,190 1,264,557 1,107,44525th 1,137,281 1,226,844 1,298,890 1,234,91150th 1,337,677 1,384,108 1,319,582 1,314,28275th 1,514,501 1,463,875 1,344,527 1,443,07695th 1,692,022 1,538,830 1,377,207 1,554,656

Mean 1,323,899 1,324,485 1,321,195 1,320,977EVaRisk 369,460 398,295 56,637 213,532

Correlation (299,004) Deterministic 1,324,758

2004 Embedded Value - Universal Life

40

Recap of Key LearningRecap of Key Learning

• Can be used to create an effective decision support framework

– Competitive advantage

– Risk optimization

• Risk measurement and management across risks & product lines is doable

• Profitability and productivity metrics for distribution system

• Facilitate capital allocation

• Segue to IAS accounting and Risk Adjusted Return on Capital (RAROC)

• Can be used to create an effective decision support framework

– Competitive advantage

– Risk optimization

• Risk measurement and management across risks & product lines is doable

• Profitability and productivity metrics for distribution system

• Facilitate capital allocation

• Segue to IAS accounting and Risk Adjusted Return on Capital (RAROC)

41

Closing RemarksClosing Remarks

• Stochastic valuation has arrived– Stochastic EV (EEV), ERM (EV @ Risk)

– RBC C3 Phase II, Economic Capital, Statutory Reserves, SOP Reserves

• Actuaries need to elevate their game to be ready– Finance theory and conceptual frameworks

– Modeling & valuation tools

– Technology

– Quality assurance

– Analysis and presentation

– Communications

• Stochastic valuation has arrived– Stochastic EV (EEV), ERM (EV @ Risk)

– RBC C3 Phase II, Economic Capital, Statutory Reserves, SOP Reserves

• Actuaries need to elevate their game to be ready– Finance theory and conceptual frameworks

– Modeling & valuation tools

– Technology

– Quality assurance

– Analysis and presentation

– Communications

42

Questions !Questions !

Documents

Enterprise Risk Management and Stochastic … and Actuarial Advisory Services Insurance and Actuarial Advisory Services Enterprise Risk Management and Stochastic Embedded Value Modeling