PBR Reserve Movement and Earnings Analysis
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1 PBR Reserve Movement and Earnings Analysis Rich Harris, FSA, FCIA, MAAA VP & US Appointed Actuary John Hancock Session 25PD PBR Attribution Analysis August 27, 2018
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4 Agenda Fundamental Tools of Cashflow-based Valuation Reserve Movement Analysis (RMA) Source of Earnings (SOE) Unique Aspects of PBR Projecting PBR Results Presenting Results to Management vs Board 4
5 RMA/SOE Background RMA can be used to understand your inforce policies and ensure that the reserves booked are accurate and appropriate Helps mitigate risk of data errors, reserve miscalculations, user error and other material risk factors General formula for RMA: V 1 = V 0 + Premiums + InvIncome - Expenses + (Expected Rsv Release - Actual Reserves Released) SOE helps quantify and identify sources of income 1 Shows the difference between actual income and expected income by component 1. SOE Disclosure from OSFI - 5
6 RMA Pre PBR Consistency of previous reserve regime made RMA more straightforward Pre-PBR RMA example: Step Reserve ($M) Opening System Balance 100 Expected Reserve Movement on Inforce 2 Actual Reserve Movement on Inforce -1.5 New Business 2.1 Closing System Balance Unexplained.2 6
7 Unique Aspects of PBR PBR Regime Change Reserve regimes will change at different rates NPR SR PBR Reserves DR This can cause VM-20 booked reserve to change methods between quarters Changing calculation bases for booked reserves is a new issue Reserve s CRVM provided consistency over various points in time Different reserve calculations for final reserve makes comparison and attribution of reserve changes difficult Time Prior Qtr Current Qtr 7
8 RMA - PBR Adjusting RMA exhibit to account for regime-switching allows for better analysis Unknown which reserve is dominant prior to quarter end, so calculate RMA for all three reserve calculations Once the dominant reserve has been identified, the appropriate RMA can be constructed Will likely require more system overhead to set up Step NPR ($M) DR ($M) SR ($M) Opening System Balance Expected Reserve Movement on Inforce Actual Reserve Movement on Inforce New Business Closing System Balance Unexplained
9 RMA - PBR Assume NPR was dominant last quarter and DR dominant this quarter Add information to exhibit to illustrate when dominant reserve changes Line 2 shows the impact of switching reserve regimes If reserve regime remains constant over RMA time period, this would be 0 Step RMA ($M) Opening System Balance (NPR) 100 Change due to Change in Dominant Reserve (DR NPR) -2 Expected Reserve Movement on Inforce (DR) 24.9 Actual Reserve Movement on Inforce (DR) New Business (DR) 4.1 Closing System Balance (DR) Unexplained (DR) -.3 9
10 SOE Basics Traditional Source of Earnings (SOE) analysis rearranges RMA and Income Statement items into different components of earnings drivers Takes Expected elements of RMA and experience assumptions and actual Income Statement lines to create Actual less Expected experience gains or losses 10
11 SOE - PBR SOE for NPR would be similar to current CRVM SOE, if available Only able to attribute earnings from actual reserve movement to interest or mortality SOE for DR/SR will have more explicit cashflow assumptions and greater potential for detailed earnings results breakdown Mortality Interest Lapse Expenses Other More assumptions allows for more detailed SOE which allows for greater understanding of the drivers of the product group However, SOE has similar issues to RMA Changes in dominant reserve can cause consistency issues if not accounted for Without adjusting SOE, impossible to compare results A new line item could be added to allocate earnings impact of any dominant reserve change 11
12 Future Projections Under PBR A practice note from the Academy on PBR projections is coming Possible uses for projections AAT, Planning or Forecasting Asset or liability assumptions prescribed in VM-20 will not be a company s actual asset mix or liability expectation Discontinuity between inner and outer loop Outer loop is on company expectation Inner loop is base on VM-20 prescriptions New concepts to consider in projections Sufficient data period and how it changes in the future Grading to industry table in future when more credibility is likely 12
13 Explaining PBR Results Different audiences will require different presentation of results Focus and expertise of each group will vary Actuarial Audience RMA will cover actuary s concerns and interest Will probably require the most detailed level of explanation of results Management SOE will cover management team s interest Will be interested in how block is performing, but may not have detailed actuarial knowledge to review RMA in depth Board Broadest explanation and highest level results presented (total reserves and earnings) Focus on controls implemented for PBR to ensure accuracy of results Comfortable that both internal and external audit items addressed via controls 13
14
15 PBR ATTRIBUTION ANALYSIS MODELING CONSIDERATIONS 2018 VALUATION ACTUARY SYMPOSIUM SESSION 25PD Christopher Halloran, ASA Oliver Wyman
16 Agenda Modeling challenges Modeling strategies Model structure and process 4 Reporting Oliver Wyman 16
17 Section 1 Modeling challenges
18 Attribution analysis challenges (1/3) PBR increases model runs and model run-time Multiple reserve components 1 Net premium reserve Deterministic reserve Stochastic reserve More complex assumptions 2 Multiple components to assumptions Assumptions updated more often Emerging guidance could cause changes 3 Assets Addition of assets More complex asset liability model Same challenges as liability assumptions 5 Increased model strain Complex asset liability model Track multiple reserve calculations More assumptions to track and quantify movements Assumption movements can come from multiple components Increased run times Increased number of runs to produce an attribution 4 Stochastic runs Each calculation of the reserve could require multiple scenario runs of the model Oliver Wyman 18
19 Attribution analysis challenges (2/3) Aggregation segments increase number of attributions At a minimum, segments are defined at the Product Group level (Term, ULSG, Other). An attribution analysis will be needed for each segment. Oliver Wyman 19
20 Attribution analysis challenges (3/3) Resource constraints during reporting periods EV model runs IFRS model runs Oliver Wyman 20
21 Section 2 Modeling strategies
22 Strategies to overcome challenges Planning is key Strategies Considerations 1 Reducing runtime More computing power Compression techniques Selective model runs, SR may not always be needed, NPR and DR only runs Selective attribution steps Expensive Loss of granularity 2 Scheduling Automated processes Set up multiple model steps at once Schedule multiple runs to run in succession Requires good planning Need good processes and peer review not just for multiple output but for model set up Potential to lose multiple runs due to errors 3 Flexibility Borrow computing power from other departments Example: pricing processing grid used by valuation Stagger attribution runs Other departments may be less productive Requires good controls Oliver Wyman 22
23 Section 3 Model structure and process
24 Model structure production process Complete multiple steps of attribution analysis in succession Production process, governance, and controls Attribution steps (always) Attribution step results Step 1 Step 1 Step 2 Modeling Automate d process Model calculation Step 2 Reporting tool Attribution analysis Step 3 Step 3 Step 4 Step 4 Additional attribution steps (sometimes) Step 1a Step 2a Step 3a Oliver Wyman 24
25 Process structure Controls ensure efficiency Group 1 Group 2 Group 3 Attribution steps Step 1 Step 1 Step Step 2 Step 2 Step Step 3 Step 3 Step Model set up tech review Automated scheduled process Calculation engine Production Model Production Model Production Model Summarization process Attribution analysis tool Results Results Results Results Results Results Results Results Results Model results review and confirmation Oliver Wyman 25
26 Section 4 Reporting tool considerations
27 Model results reports Considerations Practice 1 End goal in mind 2 Drill down capability 3 Evolve over time Description Model report structure is dictated ultimately by the desired information to show in the attribution analysis Level of granularity will follow what is needed to create the attribution tool Who is the audience? Is the level of detail appropriate? Different levels of granularity may be appropriate for different audiences More detailed information is needed to confirm model results from each attribution step Regulation is still emerging Reports must have ability to evolve over time Reports may shrink as management becomes more comfortable with PBR Oliver Wyman 27
28 Attribution Analysis: Data Requirements August 27 th, 2018
29 Agenda Attribution Analysis for PBR Recap Data needs for Attribution Analysis Data needs for Source of Earnings Example Architecture Forecasting Data Requirements PwC
30 Attribution Analysis Overview Purpose to analyze movements in reserves over a period due to changes in inputs over the quarter Possible Methods Run each attribution step through the model Confidence in model No unexplained Estimate each attribution step through sensitivities End up with an unexplained amount that you d want to keep minimal May be difficult if cannot accurately estimate the movement in reserves Combination of running through model and estimating Only estimate the items that can accurately/practically be estimated Still end up with an unexplained amount PwC 30
31 Sample Term PBR Attribution Analysis Term Reserves (Gross of reinsurance) NPR Reserve Deterministic Reserve Final Reserve Reported reserve as of prior quarter 1,100 1,300 1,300 New business/update Liability Inforce (Q) Update Asset Inforce (Q) Mortality (A) Lapse (A) Expense (A) Update Reinvestment Spreads/Defaults (Q/A) Update reinvestment strategy (A) Total change in reserve Reported reserve as of current quarter 1,200 1,410 1,410 Q: Quarterly update PwC A: Annual update or more frequent 31
32 Source of Earnings Data Flow Policy Admin Systems CLAIMS IFE CLAIM EXTRACT LIABILITY DATA ASSUMPTIONS (AWS CLOUD) ASSET ESG SCENARIO Software LIABILITY Cloud/Grid computing Reinvest. Attribution Analysis Data Requirements: Prior quarter inforce file New policies issued Change in inforce policies Current quarter inforce file TAI RESINURANC E EXTRACT Prior quarter/current quarter reinsurance Assets systems ASSET EXTRACTS SEGMENTED ASSETS Change in inforce assets (i.e. rating changes) Ledger Transaction Extract NPR DETERMINISTIC STOCHASTIC PROJECTIONS SENSITIVITIES Source of Earnings Data Requirements: CONSOLIDATE RESULTS Seriatim allocation of Deterministic and Stochastic reserves Merge with IFE data Actual income statement claim information
33 Source of Earnings Data Flow Asset Update the inforce assets for rating changes, prepayment schedules, sinking funds etc. New Assets Reflect new asset purchases and asset divestitures Attribution Analysis What to action now Prior Quarter Reporting Prior period inforce assets and liabilities allow for the calculation of the PBR reserve Liability Changes Reflect impact of policyholder change (lapse, death, premium payments, account values) New Business Add marginal new business to capture impact to reserves of marginal new policies What to action in the coming months Yield Curve Update the yield curve, corporate spreads, and default rates for new market information Data Storage Cube based storage for attribution analysis, database tools for model runs Communicate Prepare attribution analysis and report results to management Repeat Final PBR model, fully updated is then used as the starting point for the next quarter. PwC
34 Illustration of PBR Reserve Projection Top level scenario is Management s best estimate scenario for plan At each projection period, PBR reserves (deterministic and/or stochastic) must be recalculated for changes in the assets/liability over the best estimate scenario n uses prescribed PBR scenarios n could equal 1 if only need deterministic reserve If projection period is monthly, could make simplifications by calculating reserves annually and interpolating Top-level Scenario t = 0 t = 12 t = 24 t = 360 n = # of scenarios. : n. : n V12 V24 V360. : n PwC 34
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