NAVIGATING A PRINCIPLE-BASED WORLDWelcome to the Fall 2020 edition of our Prophet modeling newsletter. We hope you and yours remain happy and healthy. Principle-Based Approaches (“PBA”) for annuity and life products place increased demands on actuarial departments and models. This edition discusses Prophet model optimization techniques for life principle-based reserves (“PBR”) and Prophet capabilities for modeling variable annuity principle-based reserves (“VM-21”). You will find helpful tips and tricks on US 360 table structures and Prophet results aggregation techniques, along with a feature on new Flexible Results functionality. We hope you enjoy the newsletter.

IN THIS ISSUEExecutive CornerOptimizing Prophet PBR models: The balancing act of accuracy and speed

In the SpotlightIntroduction to VM-21

What’s New in ProphetFlexible Results

Tips & Tricks

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Volume 4 | Fall 2020

Consulting Actuaries

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© Oliver Wyman 2

Executive Corner

OPTIMIZING PROPHET PBR MODELS: THE BALANCING ACT OF ACCURACY AND SPEED

Exhibit 1. PBR modeling diagram

Inner loop projections t = * Revaluation pointsModel points Outer loop projection

t = 1 t = 2 t = 3 t = 4 t = 5

Stochastic reserve(SR)

Deterministic reserve(DR)

Net premium reserve(NPR)

Source: Adapted from Principle-Based Approach Projections Practice Note

of individual life new business. These efforts were performed under a compressed timeline and in parallel with optimizing the design of new products; thus, it should come as no surprise that a recent Oliver Wyman survey1 reported all participating insurers expect to make updates to their initial PBR implementation.

PBR RUNTIME CONSIDERATIONSCompanies have made significant updates to their pricing and valuation models to meet the mandatory January 1, 2020, implementation deadline for principles-based reserving (“PBR”)

1 2020 Oliver Wyman Life PBR Emerging Practices survey, with more than 50 participants covering 95% of the individual life insurance market.

© Oliver Wyman 3

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PBR necessitates modeling assets and liabilities across a range of scenarios to assess the sufficiency of assets, which is considerably more complex and runtime intensive than the prior formulaic statutory reserving framework. As seen in Exhibit 1, PBR requirements have a compounding impact on model complexity and runtime.

Without carefully balancing efficiency and accuracy, PBR modeling can create unnecessarily long runtimes without a corresponding increase in the precision of results. This article focuses on four approaches that can be implemented in Prophet PBR models to optimize runtime while still maintaining accuracy and meeting PBR requirements.

COMPRESSING MODEL POINTSThe number of records included in a PBR model has a roughly linear relationship with runtime. Model point compression is a widely adopted technique for reducing records without sacrificing fidelity of results.

Prophet provides user flexibility with respect to model point grouping algorithms, including applying grouping:

• Externally through FIS Data Conversion System

• Within Prophet Enterprise

• “On the fly” during runs, compressing the innerloop while fully projecting the outer loop

Robust validation should be undertaken to ensure the modified model points accurately represent the pre-compression state.

CONDENSING THE INNER LOOPAnother way to improve runtime is to reduce either the number of revaluation points or the computational intensity of each revaluation point. The former can be implemented in Prophet by enabling “Structure Link Skipping” and specifying more frequent revaluation points earlier in the (outer loop) projection and less

frequent revaluation points later in the projection. The latter can be implemented in Prophet by limiting the period over which assets and liabilities are projected in support of the reserve calculation (i.e., the inner loop).

Prophet does not interpolate between calculated reserve points by default; thus, users will want to utilize Asset Liability Strategy products or a summary product to define any such interpolation.

REDUCING SCENARIOSReducing the number of valuation scenarios in the Stochastic Reserve calculation is the most common method employed by insurers to improve model runtimes. Though not perfectly linear, scenario management can produce significant performance benefits with potentially limited impact to precision.

While current PBR requirements state the Stochastic Reserve requires modeling of 10,000 scenarios, no insurers in Oliver Wyman’s PBR survey reported modeling the full 10,000. For products where liabilities are sensitive to the economic environment, such as indexed universal life, universal life, and variable universal life, modeling 1,000 scenarios was the most common choice.

Currently, any scenario reduction technique must be applied outside of Prophet.

INCREASING PROCESSING CAPACITYFinally, insurers may choose to increase their PBR modeling capacity through the purchase or expansion of grid or cloud computing capabilities. Oliver Wyman’s PBR survey found 50% of participants have considered such an approach. For those looking to expand capacity, FIS offers a flexible platform (Prophet Managed Cloud Service) to scale capacity up or down on demand.

© Oliver Wyman 4

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CONCLUSIONAs companies become more comfortable with PBR modeling, their focus is shifting away from compliance and toward optimization. Effective model management and utilization of efficiency techniques within permitted

guidelines is essential to limiting strain on resources and systems. Understanding this need, FIS continues to adapt and expand Prophet’s capabilities. With multiple tools available in Prophet to support runtime reduction techniques and/or increase processing capacity, insurers are well equipped to thrive under the new statutory valuation standard.

© Oliver Wyman 5

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What’s New in Prophet

FLEXIBLE RESULTSIntroduced in the second quarter of 2019, Flexible Results increases the scalability and control of Prophet results output and offers potential runtime efficiencies when used with ArangoDB, a distributed database management system. Flexible Results is envisioned as being part of the solution to the increased data management and reporting requirements posed by US GAAP LDTI, IFRS 17 and other reporting regimes.

A Flexible Results license is required to access this functionality.

Below is a timeline and description of Flexible Results components and releases:

ArangoDB introduced with the potential to improve runtime compared to a traditional fileserver. The efficiencygains come from increased read/write speed to the database, meaning models with large model point filesmay realize the largest gains; however, most North American and European models should expect minimalimprovement due to the read/write time being insignificant compared to the calculation runtime

New aggregation results type introduced that utilizes user-defined variables assigned at the model point levelfor customized grouping, similar to sub-product code reporting. Aggregation results have some limits:

• Two user-defined aggregation variables per run pairing• No stochastic or dynamic processing• Only new business in model point file method• Not available for variables in modules• 2500 values per aggregation variable

New individual results type introduced that allows individual policy level results to be stored, making auditingand debugging easier. The current limit is 100 policies

Q2 2019 Flexible Results introduced for Prophet Enterprise New results file system formatting introducedfor both Prophet Professional and Prophet Enterprise

Q1 2021 Additional support for aggregation results for dynamic and stochastic runs and to allow custom aggregationmodule outputs

2021+ Future Flexible Results enhancements under consideration:

• Unlimited individual policy results to ArangoDB• Additional flexibility in results frequency for individual policy results• Expansion of Flexible Results API capabilities to additional target databases beyond MS SQL IDR

Q2 2020 Flexible Results introduced for Prophet Professional Flexible Results API introduced to retrieve resultsdirectly from Prophet Enterprise and Prophet Professional, replacing the need for Prophet Results Database

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Spotlight

INTRODUCTION TO VM-21VM-21 refers to Section 21 of the National Association of Insurance Commissioners (“NAIC”) Valuation Manual, which specifies requirements for principle-based reserves (“PBR”) for variable annuity (“VA”) contracts.

VM-21 addressed several shortcomings in the prior Actuarial Guideline 43 (“AG 43”) framework by better aligning reserve and NAIC risk-based capital (“RBC”) C3 calculations, reducing the disincentives to hedging, and mitigating non-economic statutory volatility. The revised framework achieves these goals by improving AG 43’s stochastic projection calculations and overhauling the

standard scenario by replacing it with the new standard projection concept.

This article provides an overview of Prophet’s handling of VM-21 modeling components, with a focus on considerations in transitioning from AG 43. A summary of out-of-the-box VM-21 functionality is provided first, followed by a deep dive into the stochastic reserve and the standard projection.

Exhibit 1 illustrates the reserve and capital components to arrive at the total funding requirement under VM-21.

Exhibit 1. VM-21 total funding requirement

Stochastic AmountDistribution of GPVADs

New C3 charge formula reduces impact of voluntary reserve

Minimun weight:5% for both explicit and implicit reflections of hedgingWeight referred toas “error factor”

Additional Standard Projection Amount is an add-on — but does not materially change market-sensitivity of funding requirement

C3 calculation

Add-on

CTE (best efforts)Reflecting CDHS1

CTE (adjusted)Not reflecting CDHS1

Total Asset Requirement (“TAR”) Reserve

Additional Standard Projection Amount

Total statutory funding required

Add-on

Weighted average

CTE 98 CTE 70

1

1

2

23

3

1. Clearly defined hedging strategy (“CDHS”)

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PROPHET VM-21 FUNCTIONALITY OVERVIEW

OVERVIEW & SUPPORT

Prophet has traditionally offered robust out-of-the-box VA functionality, and VM-21 is no exception. All material VM-21 modeling requirements are supported in modern Prophet libraries, with details outlined in Exhibit 2. FIS continues to support ongoing development through monthly updates for those using the US 360 Life & Annuity (“US 360”) solution. At the time of writing, additional features related to VM-21 are planned in 2020, including cumulative decrement analysis and Greatest Present Value of Accumulated Deficiencies (“GPVAD”) calculations at the contract level for aggregation benefit analysis.

For those looking to bootstrap an implementation, the US 360 example model office (“EMO”) includes a demonstrative set of products showcasing both nested and non-nested VM-21 modeling approaches along with supporting documentation.

Exhibit 2. FIS support for VM-21

Library

VM-21 functionality availability

Update frequency

Yes – full functionality

Yes – delayed functionality

Monthly

Annual

US 360 Life & Annuity

US Life & Annuity

Key indicators and variablesfor the VM-21 construct

US 360 LIFE & ANNUITY

Key indicators:STAT_VM21_TOTAL_RUN

Key variables:STAT_BASE_PBR_EXCRES_PPVM21_NYFL_FLOOR_METHODPBR_EXCESS_RES_PC

ASSET LIABILITY STRATEGY

Key indicators:PBRPBR_CALCSPBR_CALCS_LINKBASE_PRODUCT

Key variables:F_PBR_RES_CALCF_PBR_CTE_CALCI_PBR_NONEST_PARAMPBR_EXCESS_RES_*PBR_SCEN_RES_EXCL_CSV_FL

STRUCTURE

The structure of VM-21 is jointly supported by the US 360 and Asset Liability Strategy (“ALS”) libraries in Prophet. The multiple product and run requirements of VM-21 reserves and capital can be orchestrated using Prophet Nested Structures for simplicity or to allow future reserves projections.

Let’s first have a look at the non-nested approach to produce VM-21 reserves.

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NON-NESTED VM-21 RUN STRUCTURE

Generation of VM-21 balances is very similar to Prophet’s solution for VM-20 reserving. A series of ALM runs are first completed to produce reserve component results. Then a final Prophet run is performed to aggregate the pieces within a specialized ALS bottom product.

A PBR_NONEST_PARAM table is required to warehouse the parameters used to link results and hold VM-21 run component information, such as:

• Products and run numbers from which to pull results

• Timing of reserve component values

• Which components to read from each run

While straightforward, there are some drawbacks with this approach:

• Static mapping of run numbers and information for component runs

• Manual execution of sequential runs in Prophet

• Only a single point-in-time reserve is calculated

Executing this multi-run setup at any point in time within a projection can be done using Prophet Nested Structures, addressing the three drawbacks above while using the same shared codebase.

Exhibit 3. Prophet run requirements for VM-21

Stochastic reserve run  

ALS base (NAER)

ALS base (CE)

Liability

ALS top

Final run

Liability

ALS top

The ALS base product, tagged with the PBR_CALCS indicator, contains the logic to combine the final VM-21 reserve:

Stoch_Valn_Ind = eSTOCH_VALN_IND.VM21

Separate series of runs are used to produce results for stochastic reserve and standard projection amounts

Standard projection amount run

CSMP(2 runs)

or

CTEPA variant(1 run)

ALS base (NAER)

ALS base (CE)

Liability

ALS top

VM-21 reserve allocation:

Prophet performs the allocation of excess reserves back to the individual policy level, as required by VM-21

VM-21 component aggregation done here

ALS base

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NESTED VM-21 RUN STRUCTUREExhibit 4 illustrates the differences between a nested and non-nested VM-21 run. Note that for a nested run, the naming relies on the Scenarios object, with a range

of VM-21 related Scenarios pre-defined out-of-the-box.The user will need to manage the appropriate mapping of the above Scenarios to file names through the PBR_SCENARIO_MAP ALS table. This table allows the modeler to select an appropriate SCENARIO_LIST_NAME.

Exhibit 4. Prophet run options for VM-21

Standard projection amount

Prophet runs

Read prophet results from supporting runs PBR_NONEST_PARAM table used to warehouse results locations, run numbers, etc.

Stochastic reserve

Non-nested run structure

0 1 2 3Final run

TimeVM-21 reserve and allocation as of t = 0 only

Standard projection amount

Read structure links results to acquire information for reserve components

Stochastic reserve

Standard projection amount

Stochastic reserve

Standard projection amount

Stochastic reserve

Nested run structure

0 1 2 3Outer loop run

TimeContinuous stream of VM-21 reserves from the inner loops, at parameterizable points in time

Structure links (inner loop Prophet runs)

Exhibit 5. VM-21 nested structure Scenarios

Key indicators and variables for nesting VM-21 run

ASSET LIABILITY STRATEGY

Key indicators:NESTED_STRUCTURES

Key variables:F_PBR_RES_CALCF_PBR_RES_NSNS_* variables

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STOCHASTIC RESERVE DEEP DIVEVM-21 mechanics for the stochastic reserve remain largely consistent with AG 43 requirements. Many required changes are supported by the out-of-the-box Prophet solution.

SCENARIOS

VM-21 prescribes the use of the Academy Interest Rate Generator (“AIRG”) for interest and fund returns and only allows a proprietary scenario generator when it does not materially reduce the TAR. Given the extra scrutiny placed on proprietary scenarios, the industry has leaned towards using the AIRG.

Integration of AIRG outputs to standard Prophet ALS scenarios is a well-understood process, while future integration of ESG functionality into Prophet models to offer on-the-fly AIRG scenario generation for both outer and inner loops is under consideration.

DISCOUNT RATES

Under VM-21, accumulated deficiencies must be discounted at the net asset earned rate (“NAER”), defined as the earned rate for a “closed portfolio” of general account assets available at the model start date but are not part of starting assets. Alternatively, companies can use the Direct Iteration method where the amount of starting assets is iteratively solved to eliminate accumulated deficiencies at the end of any projection year.

In Prophet, the PBR_SR_DISC_RATE_IND switch in the FUND_ASS table defines the discount rate method.

Key indicators and variablesfor the stochastic reserve componentt

US 360 LIFE & ANNUITY

Key indicators:STAT_VM21STAT_VM21_ASSUMPTIONS

ASSET LIABILITY STRATEGY

Key indicators:PBRSHOW_PBR_VARSRBC_C3P2_CALCS

Key variables:F_PBR_SCEN_RESPBR_STOCH_RES_FLPBR_NAER_PC_FLPBR_ADD_ASSETS_FL

REFLECTION OF CDHS

For companies utilizing a CDHS, the stochastic reserve is calculated by weighting the CTE (best efforts) and the CTE (adjusted) via an “E” factor, or error factor. This is illustrated in Exhibit 6. VM-21 aligns the minimum allowable error factor under both reserve and capital calculations at 5%. Companies must perform formal back-testing (based on the most recent 12 months of data) to support the choice of error factor.

Separate Prophet runs are used to model the “adjusted” scenario reflecting only current hedges and the “best efforts” scenario reflecting current and future hedges.

Prophet fully supports the interpretation of a stochastic reserve component that incorporates CDHS and the final aggregation of reserves. Key controls include the PBR_CDHS_REQ switch for toggling CDHS calculations and PBR_CDHS_ERR_FACTOR input for supplying the “E” factor; both are set through the FUND_ASS table.

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Exhibit 6. VM-21 stochastic reserve requirements

Stochastic Reserves = CTE70 (best efforts) + E x max [0, CTE70 (adjusted) – CTE70 (best efforts)]

Includes current &future hedges

Company to specify a value for E (the “error factor”) in the range of 5% to 100%The greater the ability of the stochastic model to capture all risks, the lower value of E

Includes onlycurrent hedges

Formal back-testing is required over at least the most recent 12 months

Implicit method (for companies that model hedges implicitly by quantifying the cost/benefit of hedging)

Explicit method (for companies that model hedge cash

flows directly)

RBC C3

In addition to reserve changes, VA statutory reform also modified the RBC C3 framework. The revised framework uses the same stochastic distribution of GPVAD to calculate capital and reserves, thus providing better alignment between the two measures. The RBC C3 formula has been updated to reduce the impact of voluntary reserves. Two RBC C3 calculation methods are permitted, as outlined in Exhibit 7.

Prophet supports all required C3 changes within the RBC_C3P2_CALCS indicator. Implementation is straightforward and mostly handled out-of-the-box, but requires certain key controls:

• RBC_C3P2_CALC_TYPE in the FUND_ASS table selects between prior framework or VM-21 RBC C3 charge calculation methods (Std_Scen, MTA, STR)

• RBC_TAR_ADJ_IND in the FUND_ASS table indicates whether to apply an adjustment to TAR for RBC C3 due to tax liability

• Establishing a separate run (mapped either to RBC_ NONEST_RUN_NUM_TAR_CDHS in the non-nested context or to RBC_SCENRES_LINK Structure Link alias in the nested context) to support the required results

Unlike VM-21 reserves, the final calculation of the RBC C3 amount is performed within the top ALS product (F_RBC_C3P2_CALC variable) rather than the bottom, to reflect tax adjustments based on the aggregated tax reserves.

ADDITIONAL PROPHET ENHANCEMENTSOther notable VM-21 changes supported out-of-the-box are summarized in Exhibit 8.

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Exhibit 7. VM-21 capital framework

Priorframework

C3 = max (CTE 90C3P2, SSAC3P2 ) − Stat. Reserve

• Setting aside voluntary reserve can effectively eliminate C3 charge• There are numerous differences between the C3 Phase II and AG 43 calculations (tax basis, reflection

of hedging, market path in standard scenario)

Specific Tax Recognition(STR) Method

or

C3=25%×(CTE 98After−tax + Add’l Std Proj Amt × (1 − FIT) − Stat. Reserve)

• The effect of FIT is reflected in the projection of Accumulated Deficiencies for each scenario• Reflect evolution of tax reserves in the projection, taking into account restrictions around the size of

tax reserves (e.g., floored at CSV of each contract)

Macro Tax Adjustment(MTA) Method

C3 = 25%

• Modeled cash flows ignore the effect of FIT

(CTE 98Pre−tax + Add’l Std Proj Amt − Stat. Reserve) × (1 − FIT) −(Stat. Reserve − Tax Reserve) × FIT

×

Capped at amount of non-admitted DTAs attributable to VA portfolio

Revised framework

Exhibit 8. Other notable VM-21 changes

The specific prescribed treatment of VM-21 general account spreads and defaults are supported out-of-the-box in the F_PBR_DEFAULTS variable. The application of these inputs is automatically enabled whenever STOCH_VALN_IND = eSTOCH_VALN_IND.VM21

Asset portfolios (ASSET_BUY_XXX tables) must be reviewed for compliance with VM-21 requirements

The working reserve is removed from the GPVAD calculation

Under VM-21, general account assets assumptions are now aligned with VM-20. This entails:• Prescribed defaults based on NAIC rating class for

currently held assets• Prescribed defaults and spreads for reinvestment

assets• A reinvestment strategy cap of a 50/50 blend of A/AA

non-callable corporate bonds

Revenue sharing margins for non-guaranteed revenue sharing income:• Updated multiples linearly grade from 100%

of best-estimate in year 1 to 80% in years 5+• Removed the 25bps cap after year 6 previously

required by AG 43

VM-21 requirement Prophet support

The F_PBR_SCEN_RES variable innately supports this change without any user interaction

The specific prescribed treatment of VM-21 general account spreads and defaults are supported out-of-the-box in the F_PBR_DEFAULTS variable. The application of these inputs is automatically enabled whenever STOCH_VALN_IND = eSTOCH_VALN_IND.VM21

Asset portfolios (ASSET_BUY_XXX tables) must be reviewed for compliance with VM-21 requirements

The PBR_SA_REV_SHARE_NONGUAR_MULT_TBL table is used to define the appropriate percentage multiplier to apply for non-guaranteed SA revenue sharing, and will be applied to downstream revenue sharing assumptions

© Oliver Wyman 13

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STANDARD PROJECTION DEEP DIVEVM-21 overhauled the standard scenario component of AG 43 and introduced a new standard projection.

Two standard projection approaches are available to companies:

CTE with Prescribed Assumptions (“CTEPA”): Projection using prescribed liability assumptions, defined under VM-21, and the same scenarios used for the stochastic reserve.

Company Specific Market Path (“CSMP”): Projection under two defined scenarios (Path A and Path B)

and prescribed liability assumptions. Path A and Path B are selected from a prescribed set of at least 40 scenarios.

An Additional Standard Projection Amount is then calculated from the results of the standard projection and treated as an “add-on” to the stochastic reserve, as illustrated in Exhibit 9 below.

In Prophet, the PBR_STD_PROJ_TYPE switch in the FUND_ASS table allows the standard projection amount method to be selected, with new VM-21 options including:

• eSTD_PROJ_TYPE.CTEPA reads a CTEPA reserve through F_PBR_CTE_CALC

• eSTD_PROJ_TYPE.CSMP triggers specialized CSMP calculations, mostly handled through F_PBR_CSMP

Exhibit 9. VM-21 standard projection framework

or –

––=

+

=

Additional Standard Projection Amount

Prescribed projection amount

CTE 70(adjusted) Buffer

CSMP1method

CTEPA2method

CTE 70 (adjusted), without CSV floor

CTE 65 (adjusted), without CSV floor

Stochasticreserves

Final reported reserves

Floored at 0

1. The CSMP and CTEPA methods use prescribed assumptions calibrated to industry data2. CSMP method uses deterministic market paths while CTEPA uses the same stochastic scenarios as the CTE 70 adjusted run; companies can elect either method

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© Oliver Wyman 14

CTEPA

The CTEPA method is most often elected, given that the modeling requirements are easy to adapt from the stochastic reserve configuration and allow for intuitive comparison to the stochastic reserve.

The user simply needs to select the appropriate PBR_STD_PROJ_TYPE input and Prophet will perform all required calculations.

CSMP

The CSMP method is less frequently elected. Difficulties in modeling CSMP include:

• Dependency against the Stochastic Amount for picking the scenarios

• Complex configuration with an additional run construct

To mechanically produce results under the CSMP approach, two sets of runs are required for the Standard Projection Amount Run — a run under

company assumptions and a run under prescribed assumptions. Prophet functionality for preparing the scenario selection and performing the final reserving runs are outlined below in Exhibit 10.

Despite the relative complexity, there are notable advantages from using the CSMP method:

• The deterministic path allows for an easier-to-explain outcome at the scenario level as compared to a stochastic outcome

• The prescribed 40 scenarios are much less onerous on runtime as compared to a typical 1000+ scenario CTEPA run

Key indicators and variables for CSMP support

ASSET LIABILITY STRATEGY

Key indicators:PBR_CALCS

Key variables:F_PBR_CSMPPBR_CSMP_PATH_[A|B]_FLPBR_CSMP_PSPS_PATH_[A|B]_AMT_FL

Exhibit 10. Prophet support for CSMP

The algorithm for selecting the CSMP Path A and Path B are both supported natively in ALS (F_PBR_CSMP), without requiring user customization

With the two CSMP supporting runs completed, the final weighted reserve is calculated by F_PBR_CSMP and made available for use in final aggregation by F_PBR_RES_CALC

Mechanics Scenarios

A pre-shocked example of CSMP scenarios, PBR_CSMP_70_201912.fas, along with supporting documentation, is embedded in the US 360 PBR EMO; this example demonstrates a process for deploying more than the 40 prescribed scenarios

The CSMP_EQUIT_STANDARD_PATHS and CSMP_INTEREST_STANDARD_PATHS controls in COMPANY_ASS can be used to overwrite the prescribed 8/5 equity/interest rate shocks respectively

Source: Oliver Wyman Analysis

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PRESCRIBED ASSUMPTIONS

VM-21 standard projection requires the use of prescribed assumptions, which have been refined from the prior AG 43 framework to better align with industry experience. Full coverage of the prescribed assumptions is beyond the scope of this article.

In Prophet, the implementation of VM-21 prescribed assumptions is handled within the US 360 library and EMO.

By setting the VM21_PRESCRIBED_ASSUMP_FLAG flag to eBOOL.isTrue, the wide range of VM-21 prescribed assumptions, commonly prefixed with VM21_PA_*, is automatically made available. The US 360 EMO packages a VM21PrescribedAssumptions.fac table along with supporting tables that serves as the recommended starting point for any VM-21 implementation.

Key indicators and variables for prescribed VM-21 assumptions

US 360 LIFE & ANNUITY

Key indicators:STAT_VM21_ASSUMPTIONSSTAT_VM21_WDCM

Key variables:VM21_PA*VM21_PRESCRIBED_ASSUMP_FLAG

Exhibit 11. Prophet modeling of prescribed assumption

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© Oliver Wyman 16

A NOTEWORTHY CONSIDERATION: WITHDRAWAL DELAY COHORT METHOD (WDCM)The Withdrawal Delay Cohort Method (“WDCM”) is the prescribed method for determining policyholder election timing for withdrawal benefits for hybrid GMIBs and GMWBs. The WDCM is particularly challenging from a modeling point of view and is required for both CSMP and CTEPA methods.

The WDCM requires the determination of withdrawal election timing cohorts and weights for each applicable cohort. The same VM-21 Prophet product used for liability projections is capable of pre-calculating the required policy-level cohort weights through a staging run. FIS has designed a DCS configuration to support the WDCM run and assist with model point file generation.

The WDCM functionality is enabled through the STAT_VM21_WDCM indicator, which is automatically included for VM-21 products that have GMWBs. The VM21_WDCM_FLAG triggers a series of GMWB election timings, executed through the Prophet calculation loop mechanic, for every age between a maximum and a minimum parameter. (This is similar to the construct used for AG 33).

The Guaranteed Actuarial Present Value (“GAPV”) required to support election point weighting is calculated within the VM21_GAPV_GMWB_LINK module and compiled through the VM21_WDCM_WEIGHTS_CALC extended formula, with no required user input.

The final output, carrying weights for each cohort (tracked through the ‘vm21_cohorts’ dimension), can be used to construct weighted model point files with model point rows representing each cohort.

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Tips & Tricks

RESULTS AGGREGATION TECHNIQUESNew regulatory requirements necessitate insurers generate results at varying levels of granularity. Prophet offers several results aggregation techniques to accommodate these requirements and companies’ internal reporting needs. Four such techniques are outlined below.

SAME-AS PRODUCTS

Same-as products can be used to aggregate results at varying levels of granularity; these use the same code base as another master product, but allow greater granularity in model inputs and results, as shown below:

This technique requires (1) creating new model point files with desired results groupings and (2) updating the structure and tables that use product name as an index (e.g., parameter table) to include the new same-as products.

Accumulations can be used to aggregate results from multiple master and same-as products.

SUB-PRODUCT CODE (“SPCODE”)SPCODE can be used to define the granularity of results via the model point file. Results will be grouped by SPCODE and also available in aggregate:

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The SPCODE ranges to be accumulated from multiple products can be defined, as shown below:

The SPCODE must be assigned an integer value between 1 and 9999 inclusive, effectively limiting the number of results groupings to 9999. For dynamic runs, the SPCODE limit is reduced to 999.

ARRAY VARIABLES WITH CUSTOM DIMENSIONS

Array variables, when used with custom dimensions, can be leveraged to report granular results. For example, a new array variable MATH_RES_IF_SEX can be introduced with its dimension defined as 1 for males and 2 for females. Using the variable code shown below will generate two sets of results: MATH_RES_IF_SEX(1) for reserves for male policies and MATH_RES_IF_SEX(2) for reserves for female policies.

AGGREGATION RESULTS

Aggregation results, a new results type within Prophet’s Flexible Results, provides the user greater control in aggregating results and allows the user to report results by customized results groupings.

Similar to SPCODE, the user can use aggregation variable(s) in the model point file to specify a desired level of granularity. The aggregation variables are currently limited to two user-defined variables per run pairing and 2500 unique groupings each. Aggregation results are expected to be extended to dynamic and stochastic runs in the Prophet 2021 Q1 release.

Aggregation results functionality was introduced in the Prophet 2020 Q2 release and requires a Flexible Results license.

© Oliver Wyman 19

Profiting From Prophet

Tips & Tricks

US 360 TABLE ORGANIZATIONThe US 360 library makes use of a library-specific table structure for configuring products. This marks a departure from traditional liability modeling within the older US Life & Annuity Library, where no default table structure is recommended. Although common to deviate from a library-specific table structure for product-dependent tables (in favor of company-specific customizations), the US 360 default structure is typically adhered to for reserve control tables given code within reserving modules is largely locked-down.

Prescribed tables generally follow a table-of-tables format, with the ability to set a mapping table that is used to drive individual configuration tables by key dimensions. For each key dimension combination, a specific table is used to store actual assumptions.

The following exhibit breaks down the US 360 default structure into three hierarchies and annotates a variable name and key dimension(s) for each table. It can serve as a helpful reference when developing a new US 360 model or converting older libraries to US 360.

GLOBAL TABLE

Run number

Product control tableTable of product assumptions

PRODUCT_CONTROL_TBL

Plan code

(x)

Product features tableTable of product features

PRODUCT_FEATURE_TBL

Plan code

(x)

GMxB features tableTable of GMxB features

GMxB_FEATURE_TBL

GMxB plan code

(x)

Surrender charge control tableSurrender charge tables

SCHG_*_TBL

Varies

(x)

Reserve parameters tableTable of reserve parameters

RES_VALN_SETTINGS_TBL

Reserve parameter key, reserve basis

Mortality control tableMortality assumption control table

MORT_CONTROL_TBL

Mortality control key, mortality basis

Lapse control tableLapse assumption control table

LAPSE_CONTROL_TBL

Lapse control key, lapse basis

Interest control tableInterest assumption control table

INT_CONTROL_TBL

Interest control key, interest basis

Product tableTable of product-dependent tables

PRODUCT_TABLE_MAP

Product family

No

(x)

(x)

(x)

(x)

(x)

(x)

(x) Variable

Reserve control tableReserve master control table

RES_CONTROL_TBL

Reserve control key

Partially

Key dimension Locked-down

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