Session 55 PD, When is Your Own Data Not Enough? Moderator: Robert E. Winawer, FSA. Presenters: Leonard Mangini, FSA, MAAA Timothy S.

Transcription

1 Session 55 PD, When is Your Own Data Not Enough? Moderator: Robert E. Winawer, FSA Presenters: Leonard Mangini, FSA, MAAA Timothy S. Paris, FSA, MAAA SOA Antitrust Disclaimer SOA Presentation Disclaimer

2 2018 SOA Life & Annuity Symposium Session 55- When Is Your Own Data Not Enough? PBR Developments that Might Surprise You! Tue, May 8, 2018 Leonard Mangini, FSA, FRM, FALU, CLU, MAAA President and Managing Member, Mangini Actuarial and Risk Advisory LLC

3 Presenter Biography Leonard Mangini, FSA, FRM, FALU, CLU, MAAA President and Managing Member, Mangini Actuarial and Risk Advisory LLC Mr. Mangini brings clients over 28 years of industry expertise, holding senior Product, Reinsurance, Financial, and Risk Management-related industry roles at Manulife, ACE, AXA, and USLIFE and assisting clients with Product Development, Financial Reporting, Underwriting, Reinsurance, Risk Management, M&A, and Litigation issues as a consultant with E&Y, Milliman, and nowhis own advisory firm. In his last direct company role, Leonard was Deputy Global Corporate Chief Actuary supervising principles-based assumption and margin unlocking for over 100 products sold in 19 business units across the US, Canada, and Asia and also served on the Global Product Risk and Global ALM Committees. In prior reinsurance roles, Mr. Mangini served as an internal Board member, President, Chief Actuary, Chief Pricing Officer, and Chief Risk Officer, and co-founded a US life reinsurer. He s one of the few actuaries credentialed by exam as a medical underwriter, and has priced mortality, morbidity, longevity, and policy behavior for products issued in fully underwritten and alternative distribution channels. Leonard serves on both the SOA Product Development and Joint Risk Management Section Councils, previously Chaired the Financial Reporting Section Council, and was a member of the Marketing and Distribution and Reinsurance Section Councils. He currently Chairs he Academy s PBR Life Reserve Work Group (LRWG), is a Member of the Academy Life Practice Council (LPC) and Life Valuation Committee (LVC) and serves on Academy s ASOP 11- Credit for Reinsurance- Update Committee, and is part of the Leadership team of the SOA Assumption Discussion Group. Mr. Mangini is a Fellow of the Society of Actuaries (FSA), a Certified Financial Risk Manager (FRM), Fellow of the Academy of Life Underwriting (FALU), a Member of the Academy of Actuaries (MAAA), and earned a MS in Computational Finance SOA LAS: Session 55 When is Your Own Data Not Enough? May 8,

4 Liability Disclaimer, Copyright, Use of Slides Although I ve attempted to faithfully capture the letter and spirit of legal, regulatory and Actuarial Standard of Practice (ASOP) constraints, you have a personal professional duty to familiarize yourself with the original source material and apply professional judgment as to its specific application to your own work and those working under your direction as you perform covered Actuarial Services. The nature of your work, and other professional designations you hold, may require you to be bound by additional professional requirements from other organizations as well. This material has been prepared for general informational purposes only and is not intended to be relied upon as accounting, legal, tax, or other professional advice, nor is it an Actuarial Opinion by Leonard Mangini, Tim Cardinal, Arnold Dicke or their respective firms Mangini Actuarial and Risk Advisory LLC, Actuarial Compass LLC, or AADicke LLC. Please refer to your advisors for specific professional advice. The views expressed by the presenter are not necessarily those of Mangini Actuarial and Risk Advisory LLC, Actuarial Compass LLC, AADicke LLC, or the Academy of Actuaries. Much of the original source material on VM-20/PBR and Professionalism is copyrighted material of the American Academy of Actuaries, Society of Actuaries, or National Association of Insurance Commissioners. This presentation paraphrases these for educational purposes to capture the intent of existing and proposed regulations and standards of practice or may paraphrase the results of SOA research, and every attempt has been made to identify and cite original sources, where applicable These slides may NOT be copied, redistributed, or otherwise furnished to any party without prior written consent of Mangini Actuarial and Risk Advisory LLC, other than as required to comply with an audit of the meeting attendee s annual CPE compliance LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

5 Society of Actuaries Presentation Disclaimer Presentations are intended for educational purposes only and do not replace independent professional judgment. Statements of fact and opinions expressed are those of the participants individually and, unless expressly stated to the contrary, are not the opinion or position of the Society of Actuaries, its co-sponsors or itscommittees. The Society ofactuariesdoesnotendorse orapprove,andassumesno responsibilityfor, the content, accuracy or completeness of the information presented. Attendees should note that the sessions are audio-recorded and may be published in various media, including print, audio and video formats without further notice. 4

6 SOCIETY OF ACTUARIES Antitrust Compliance Guidelines Active participation in the Society of Actuaries is an important aspect of membership. While the positive contributions of professional societies and associations are well-recognized and encouraged, association activities are vulnerable to close antitrust scrutiny. By their very nature, associations bring together industry competitors and other market participants. The United States antitrust laws aim to protect consumers by preserving the free economy and prohibiting anti-competitive business practices; they promote competition. There are both state and federal antitrust laws, although state antitrust laws closely follow federal law. The Sherman Act, is the primary U.S. antitrust law pertaining to association activities. The Sherman Act prohibits every contract, combination or conspiracy that places an unreasonable restraint on trade. There are, however, some activities that are illegal under all circumstances, such as price fixing, market allocation and collusive bidding. There is no safe harbor under the antitrust law for professional association activities. Therefore, association meeting participants should refrain from discussing any activity that could potentially be construed as having an anti-competitive effect. Discussions relating to product or service pricing, market allocations, membership restrictions, product standardization or other conditions on trade could arguably be perceived as a restraint on trade and may expose the SOA and its members to antitrust enforcement procedures. While participating in all SOA in person meetings, webinars, teleconferences or side discussions, you should avoid discussing competitively sensitive information with competitors and follow these guidelines: Do not discuss prices for services or products or anything else that might affect prices Do not discuss what you or other entities plan to do in a particular geographic or product markets or with particular customers. Do not speak on behalf of the SOA or any of its committees unless specifically authorized to do so. Do leave a meeting where any anticompetitive pricing or market allocation discussion occurs. Do alert SOA staff and/or legal counsel to any concerning discussions Do consult with legal counsel before raising any matter or making a statement that may involve competitively sensitive information. Adherence to these guidelines involves not only avoidance of antitrust violations, but avoidance of behavior which might be so construed. These guidelines only provide an overview of prohibited activities. SOA legal counsel reviews meeting agenda and materials as deemed appropriate and any discussion that departs from the formal agenda should be scrutinized carefully. Antitrust compliance is everyone s responsibility; however, please seek legal counsel if you have any questions or concerns. 5

7 AGENDA: When is Your Own Data Not Enough? A PBR Mortality Assumption Perspective That Might Surprise You! A Quick Primer onvm-20 Mortality Prudent Estimate Mortality Margins: Credibility and Other Adjustments What Happens if You Sub-Optimize Aggregation? Creating PBR reserves which are biggerthen XXX Situations that Can Cause Trouble if You re Careless New Underwriting Tests, Accelerated Underwriting, New Channels VM-20 and ASOPs- What Do They Already Say About Aggregation You May Have Enough Data!...Recently Exposed APF Aggregation of Mortality Segments for Credibility- comments until May10 Optimized Aggregate Mortality and Segment-Level Impacts Other Sources- What Might You Do If Your Own Data Really Isn t Enough 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

8 A Quick Primer on VM-20 Mortality 2018 LAS: Session AADicke LLC and Mangini Actuarial and Risk Advisory LLC May 8,

9 VM-20 Mortality Credibility and Modeled Reserves The Operative VM for 2018: Has NO Deterministic Exclusion Test for Term NO current exposed APFs to alter this in time for VM applicable in 2019 àmust calculate NPR and Modeled Reserve to see which one wins àimpacts 2018 issues, almost certainly 2019 issues, probably longer àsomething YOU have to deal with in Pricing since reserves impact pricing 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

10 VM-20 Definitions- Prudent Estimate (Section 9.A) Prudent Estimate : Used for ANY risk factor NOT prescribed or stochastically modeled Must comply with Section 12 of SVL (Included as VM-05) Anticipated Experience + Margin Adverse Deviation/Estimation Error Anticipated Experience: Company OWN experience if relevant, credible Mortality shall combine relevant company experience with industry experience data in deriving anticipated experience consistent with statistical credibility theory and accepted actuarial practice à ASOP 1: shall means that NOT doing so is a deviation! 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

11 VM-20 Definitions- Margins (Section 9.C.5) Separate Margins for Company and Industry Experience rates A percentage Increase applied to each rate Company margin varies by attained age and credibility- table lookup based on whether Company chooses Bühlmann or Limited Fluctuation Method Once Company chooses one of these two methods locked in unless Company requests and receives approval from Commissioner to switch Industry margin varies ONLY by attained age- i.e. assumed fully credible Size of Margin Increased for: Imprecise experience studies or staleness Underwriting or risk selection criteria have changed since experience study Lack of homogeneity Unfavorable environmental or health developments- e.g. a pandemic Changes in marketing or administration that create anti-selection risk Ineffectiveness of underwriting compared to expectations 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

12 VM-20 Definitions- Sufficient Data Period (Section 9.C.6) Use Company Experience in policy durations with Sufficient Data Sufficient Data Period End: Last policy duration with 50 or more claims Determines when/how to grade between Company and Industry rates 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

13 VM-20 Mortality Grading Splits Policy Period into 3 Eras Company Period: 100% Company-Based Assumptions Grading Period: Blends Company and Industry Experience Industry Period: 100% Industry-Based Assumptions If Credibility < 20% NO Company Period NO Grading Period à Applicable Industry Data 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

14 Inputs Into Calculations Are Projects in of Themselves! VM-20 Section 9.C.1.a Establishing Mortality Segments Determine Applicable Industry Table Using Relative Risk Tool to map into Relative Risk Tables (RR80, RR100 etc) Creating/Choosing an Internal Company Table Collecting relevant and credible data BY AMOUNT Performing actual/expected experience studies Taking account of considerations for underlying data and margins 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

15 VM-20 9.C.1.a: VM-20 Mortality Segments Company shall determine mortality segments for purpose of determining separate prudent mortality assumptions for groups of policies that the Company expects will have different experience than other groups ofpolices Clear thatthis means differentunderwritingclasses: Male vs. Female Non-Tobacco vs. Tobacco User Preferred vs. Standard vs. Table Rated BUT many companies issue a variety of policies using the same or similar marketing PROCESS and underwriting PROCESS 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

16 Company and Industry Credibility VM-20 Section 9.C. requires the use of Look-Up Tables to Find Loads For Company Experience Attained-Age Ranges as Rows Level of Credibility as Columns For Industry Experience Attained-Age Ranges as Rows Level of Credibility is NOT a factor- since industry is assumed 100% Credible à Output is a Percentage Load to ADD to the Raw Mortality 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

17 Determination of Company Experience (Section 9.C.2) EACH mortality segment: Company Mortality derived from Company experience data (9.C.2.a) Company experience NOT available/limited, Company can choose an applicable industry table (9.C.2.a) Company experience shall be based on (ASOP 1 meaning of shall): Actual books of business within mortality segment (9.C.2.b.i) Other books of business within Company with similar underwriting (9.C.2.b.ii) Other sources, if available and appropriate, such as actual experience data from one or more mortality pools in which the policies participate under the terms of a reinsurance agreement (9.C.2.b.iii) Other source appropriate if underwriting and expected mortality experience characteristics similar to policies in mortality segment (9.C.2.b.iii) Shall not be lower than mortality Company expects to emerge, and can justify and disclose in VM-31 PBR Actuarial Report (9.C.2.c) 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

18 What Happens If You Sub-Optimize Aggregation 2018 LAS: Session AADicke LLC and Mangini Actuarial and Risk Advisory LLC May 8,

19 Suppose Company Experience from Granular Segment Suppose set segments at Underwriting Class level for Particular Product Super-Preferred Risk Class: Expect best mortality amongst all underwriting classes BUT, few might qualifyà so low count and amount in best class Could even have < 20% credibility à forced to use Industry Table Even if > 20% credibility, prudent estimate + high marginà valuation mortality for Super-Preferred > valuation mortality for next lower Preferred Class! Few Females? à Could have Female Qx > Male Qx for same rating class Could have <50 actual claims due to great underwriting, low expected Qx which creates a very short Sufficient Data Period (SDP)à rapid grading to Industry 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

20 Other Potential Undesirable Outcomes Diversity of interpretation between Companies as to conditions when using aggregate mortality isacceptable despite having similarunderlying riskprofiles. à Different mortality margins, SDPs, and grading from Company to Industry mortality for two Companies otherwise similarly-situated Diversity of interpretation between jurisdictions conditions regarding when using aggregate mortality isacceptable despite similarunderlying riskprofile à Similarly-situated Companies but different domiciles with differing mortality credibility margins, SDPs, grading and thus different reserves patterns ThreatensIntent ofvaluation Manual: Harmonize valuation across jurisdictions Similarlysituated companies havesimilarlevels of reserves Similar products with similar risk and experience have similar level of reserves 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

21 Risk of PBR Reserves with Low Credibility > pre-pbr XXX! A Company ignoring legitimate CHOICES available to determine mortality experience at an optimal level of aggregation may even produce reserves > pre-pbr XXX! A pair of articles looking at smaller insurers* and larger insurers** illustrates graphically how the sum of the Deterministic Reserve (DR) and the Deferred Premium Asset (DPA), i.e. the DR+DPA, varies for different levels of Buhlmann Credibility. * A VM-20 Mortality and Credibility Factor Observation by Tim Cardinal, Principal, Actuarial Compass LLC September 2017, SOA Small Talk newsletter, pp ** A VM-20 Mortality and Credibility Factor Observation by Tim Cardinal, Principal, Actuarial Compass LLC December 2017, SOA Financial Reporter newsletter, pp LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

22 Situations that Can Cause Trouble If You re Careless 2018 LAS: Session AADicke LLC and Mangini Actuarial and Risk Advisory LLC May 8,

23 Situations Potentially Leading to Over-Reserving Performing Experience Studies at an overly-granular level when: Other products in the Company have similar underwriting/expected mortality Accelerated Underwriting doesn t alter the broader business acquisition and underwriting process that underlies the risk classification system being used Medical advances (e.g. Hga1c diabetes tests) upgrades a portion of an existing riskclassification system but doesn t fundamentally alter the overall process New underwriting data sources (e.g. Rx databases) collect similar risk classification information as existing sub-processes of an overall classification system Technology (Predictive Analytics) replaces portions of the risk classification process New distribution (e.g. FinTech) produce similar mortality as traditional channels You MIGHT be able to demonstrate similar expected experience to other business within the Company or perform experience studies at a more aggregate level with greater credibility 2018 LAS: Session AADicke LLC and Mangini Actuarial and Risk Advisory LLC May 8,

24 VM-20 and ASOPs: What Do They Already Say About Aggregation? 2018 LAS: Session AADicke LLC and Mangini Actuarial and Risk Advisory LLC May 8,

25 Company Discretion in the Current VM- Credibility VM-20 (Section 9.C.4.b) already currently states that: Credibility may be determined at either the mortality segment level or at a more aggregate level if the mortality for the sub-classes (mortality segments) was determined using an aggregate level of mortality experience. A single level of credibility shall be determined over the entire exposure period, rather than at each duration, within the exposure period. This overall level of credibility will beused to: i. Determine the prescribed margin for company experience mortality rates. ii. Determine the grading period (based on the credibility percentage shown in column (1) in the applicable table in Section 9.C.6.b.iii) for grading company experience mortality rates into the applicable industry basic table LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

26 Company Discretion in the Current VM: SDP and Grading VM-20 Section 9.C.6.b.ii. already currently states that: In determining the sufficient data period (SDP) the company shall first identify the last policy duration at which sufficient company experience data exists (using all the sources defined in Section 9.C.2.b). The sufficient data period then ends at the last policy duration that has 50 or more claims (i.e., no duration beyond this point has 50 claims or more) subject to the limits in Column 2 of the applicable table in Section 9.C.6.b.iii.b. The sufficient data period (SDP) may be determined at a more aggregate level than the mortality segment if the company based its mortality on aggregate experience and then used a methodology to subdivide the aggregate class into various subclasses or mortality segments LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

27 Aggregate Experience Studies that Are Subdivided The common link between Sections 9.C.4.b and 9.C.6.b.ii is the concept of performing experience studies at an aggregate level and using a conservation of deaths method to subdivide that aggregate experience into sub-classes where these sub-classes: Respect Section 9.C.1.a- i.e. separate mortality segments reflect groups of policies that are expected to havedifferent mortality Respect Section 9.C.2.c- namely, the mortality for each segment shall be no lower than the Company expects to emerge and which the Company justifies and discloses in VM-31 Comply with ASOP 12 on Risk Classification 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

28 Actuarial Standard of Practice 12 Risk Classification for All Practice Areas: Guidance when designing, reviewing, changing risk classification when classifying into groups intended to reflect relative likelihood of expected outcomes à Obviously applies for PBR Mortality Segmentation Scope- Section 1.2 Setting of rates, contributions, reserves, benefits, dividends, experience refunds Analysis or projection of quantitative or qualitative experience or results Actuaries performing activities likely to have material effect, in actuary s judgment on intended purpose Source: ASB Board Website LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

29 Actuarial Standard of Practice /3.3: Actuary SHOULD consider the following in selecting Risk Characteristics: Expected outcomes- demonstrate variation in actual/reasonable expected anticipated experience correlates with risk drivers à Causation NOT required Demonstration: may use relevant information from any reliable source, including statistical or mathematical analysis, may use clinical experience or expert opinions Equity- Rates considered equitable if differences reflect material differences in expected cost Interdependence- Should consider interdependence to extent expect to have material impact Inferences without Demonstration- sometimes appropriate to infer without demonstration Objectivity- should select risk characteristics capable of objective determination (measurable) based on readily observable facts Law and Business Practices: Should consider constraints of applicable law, industry practices Homogeneity ( 3.32): variation in outcomes within a risk class too greatà subdivide. If too granularà consider combining proposed risk classes to balance predictability and homogeneity Source: ASB Board Website LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

30 Amendment Proposal (APF) Aggregation of Mortality Segments for the Purpose of Determining Credibility 2018 LAS: Session AADicke LLC and Mangini Actuarial and Risk Advisory LLC May 8,

31 Amendment Proposal Form : Aggregation of Mortality Segments for the Purpose of Determining Credibility Proposed by Academy Life Reserve Work Group (LRWG) to clarify and provide guidance surrounding sub-dividing aggregate mortality segments ALREADY permitted by 9.C.4.b Argues Company may issue a variety of policies using the same or a similar marketing processes and the same or a similar underwriting processes which may lead them to examine mortality experience and credibility at an aggregate level AND sub-divide such aggregate experience class into sub-class mortality segments under 9.C.1.a with different expected mortality but the SAME credibility produced at the aggregate experience study level and that such practice is both SOUND and in the spirit of the current VM wording Proposes a geography change of part of VM-20 Section 9.C.4.b to a new 9.C.4.c intended merelyto separateexisting language on how credibility is USED from how it s determined Proposes changing remaining wording of Section 9.C.4.b AND adding a Guidance Note to 9.C.4.b. The intent of both of is to clarify and provide guidance on how credibility is determined in the aggregate by clarifying the level of aggregation permitted by the VM Source: L. Mangini, Chair, LRWG, Academy of Actuaries Presentation to LATF 3/22/ LAS: Session 55: When Is Your Own Data Not Enough? May 8,

32 Proposed New VM-20 Section 9.C.4.b- Opening Part The revised language for Section 9.C.4.b starts with the following language: b. Credibility may be determined at either the mortality segment level or at a more aggregate level. Experience for different mortality segments may be aggregated if the following three conditions are met: i. The company based its mortality on the aggregate experience and then used a methodology to subdivide the aggregate class into various mortality segments and a description ofthe methodology for sub-dividing isprovided inthe VM-31 PBR Actuarial Report; ii. All mortality segments to be aggregated were subject to the same or similar underwriting PROCESESES; and iii. The mortality segments were sold by similar distribution systems and to similar market segments. The first romanatte rephrases the first paragraph of the current 9.C.4.b using language from 9.C.6.b.ii, while the AND language requires that ALL three romanette conditions be met before the Companycan consider aggregation. Source: L. Mangini, Chair, LRWG, AcademyofActuaries Presentationto LATF 3/22/ LAS: Session 55: When Is Your Own Data Not Enough? May 8,

33 Proposed New VM-20 Section 9.C.4.b- Middle Part New VM-20 Section 9.C.4.b: Underwriting processes that utilize new methods, but which are expected to produce similar mortality, are considered to be similar to previously established underwriting processes if these expectations regarding mortality are supported in the VM-31 actuarial report by medical, clinical, actuarial, orindustrystudies; predictiveanalyticsorstatistical analyses; or modeled demonstrations. If the distribution system or target market for a mortality segment differs from that of the other mortality segments within the aggregate grouping, the mortality experience cannot be aggregated for credibility purposes unless the company expects and can demonstrate that the mortality experience for the segments is similar to that of the other mortality segment, and support is provided in the VM-31 Actuarial Report. We ll see on the next slide that this mortality-outcome-expectations criteria for similarity and what is considered acceptable evidence to support these expectations both appear to be aligned with relevant sections of acceptable actuarial practice in ASOP 12 on Risk Classification. The distribution system language above also addresses the underwriting dynamism reality discussed above, namely that properly designed direct-to-consumer marketing mayin actual practice produce mortalitysimilar to that of agent-sold business. Source: L. Mangini, Chair, LRWG, Academy of Actuaries Presentation to LATF 3/22/2018 and APF LAS: Session 55: When Is Your Own Data Not Enough? May 8,

34 Alignment of APF with ASOP 12 and Clarification of Evidence ASOP 12 Section 3.2.1: The actuary should select risk characteristics that are related to expected outcomes. A relationship between a risk characteristic and an expected outcome, such as cost, is demonstrated if it can be shown that the variation in actual or reasonably anticipated experience correlates to the risk characteristic. In demonstrating a relationship, the actuary may use relevant information from any reliable source, including statistical or other mathematical analysis of available data. The actuary may also use clinical experience and expert opinion. The LRWG argued that similar expected mortality IS an objective measure for demonstrating that a group of policies should be aggregated for credibility purposes, and actuarial techniques such as conservation of deaths could be used to divide develop experience assumptions for the mortality segments. They also argued that ASOP 12 language is clearly consistent with using medical, clinical, actuarial, or industry studies, predictive analytics, statistical analyses, and/or modeled demonstrations. This is important since VM-20 Section 9.C.2.f.1 governs adjusting Prudent Estimate Assumptions: supported by published medical or clinical studies or other published studies that correlate a specific risk selection criteria to mortality or longevity experience (for example, criterion and correlations determined through predictive analytics). and many Company experience studies or analyses are internal and proprietary so NOT published Source: L. Mangini, Chair, LRWG, AcademyofActuaries Presentationto LATF 3/22/ LAS: Session 55: When Is Your Own Data Not Enough? May 8,

35 Proposed New VM-20 Section 9.C.4.b- Closing Part The last portion of the new VM-20 Section 9.C.4.b language states: If the company determines mortality and credibility at an aggregate level, then the future mortality experience of each of the mortality segments within the aggregate shall be studied separately and the emerging results for each of these segments shall be presented within the VM-31 actuarial report This language reflects the prudential caution in the LRWG presentation that if the Company has developed company experience mortality rates from aggregate experience and then subdivided the aggregate class into mortality segments, that actual mortality experience that emerges from the mortality segments might diverge over time, so ongoing monitoring and disclosure is essential to demonstrate that the Company s initial expectations regarding the appropriateness of the initial aggregation continue to be wellfounded atsubsequentvaluation periods. Source: L. Mangini, Chair, LRWG, AcademyofActuaries Presentationto LATF 3/22/ LAS: Session 55: When Is Your Own Data Not Enough? May 8,

36 Proposed New VM-20 Section 9.C.4.b- Guidance Note TheAPF also proposesincluding the following guidance notefor 9.C.4.b: Guidance Note: The intent of this section is to allow aggregation of different types of life insurance products (such as term, whole life, universal life (UL), etc.) and different underwriting and risk classes within these products for purposes of determining credibility when the underlying underwriting processes are similar. In the evaluation of similarity, consideration should be given towards differences in mortality based on distribution and target market characteristics. The intent is not to allow broad aggregation of disparate underwriting methods such as guaranteed issue or simplified issue with fully underwritten products. Mortality experience of products such as corporate- and bank-owned life insurance (COLI and BOLI) products, can also be aggregated for credibility if the company expects mortality experience will be similar to that of fully underwritten products. Source: PubliclyExposed APF LAS: Session 55: When Is Your Own Data Not Enough? May 8,

37 Key Takeaways: If APF is Approved Consider performing experience studies at the highest level of aggregation you can justify in VM-31 in compliance with ASOPs 12, 23 and 41, and use conservation of deaths to subdivide into mortality segments aligned with Section 9.C.1.a, factoring in: Products targeting similar market demographic segments and underwriting processes Groups of policies with similar mortality expectations Whether a new distribution system produce similar mortality as an established one Whether a medical innovation is just a new process to measure data already being used in risk selection and correlates to an existing process with a lengthy credible study Whether a new underwriting data source is just replacing a sub-process within a broader underwriting and marketing or distribution process that otherwise hasn t changed materially over the length of an already credible experience study Whether Accelerated Underwriting is merely a sub-process of a larger system that shares the same or similar underwriting and marketing process and produces similar mortality Whether current similar expected mortality is expected to diverge over time Whether investment segments drive policyholder behavior that alters expected mortality 2018 LAS: Session AA Dicke LLC and Mangini Actuarial and Risk Advisory LLC May 8,

38 Alternative Source of Data: What to Do When Your Own Data REALLY Isn t Enough 2018 LAS: Session Mangini Actuarial and Risk Advisory LLC May 8,

39 What if Your Data Really Isn t Enough Drawbacks of direct carrier using traditional reinsurance to boost credibility High Quota Share percentages cede away profits just to optimize reserves Reluctance to share proprietary data- reveals profit margins Concentrating counter-party risk in one reinsurer M&A can boost exposure and claims volume impacting credibility and SDP Expensive and time-consuming- combining cultures, admin systems, strategy Requires regulatory approvals Direct carrier can act as a reinsurer Flow-reinsurance takes time to build scale Block acquisitions can be as complicated as M&A 2018 LAS: Session AA Dicke LLC and Mangini Actuarial and Risk Advisory LLC May 8,

40 Contact Information Leonard Mangini, FSA, FRM, CLU, FALU, MAAA President, Mangini Actuarial and Risk Advisory LLC Web: Mobile: (516)

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42 2018 Life & Annuity Symposium Session 55 When Is Your Own Data Not Enough? May 8, :00-11:15am Timothy Paris, FSA, MAAA Ruark Consulting LLC

43 Disclosures This material is not intended for general circulation or publication, nor is it to be reproduced, quoted or distributed for any purpose without the prior written permission of Ruark. Ruark does not accept any liability to any third party. Information furnished by others, upon which all or portions of this material are based, is believed to be reliable but has not been independently verified, unless otherwise expressly indicated. Public information and industry and statistical data are from sources we deem to be reliable; however, we make no representation as to the accuracy or completeness of such information. The findings contained in this material may contain models, assumptions, or predictions based on current data and historical trends. Any such predictions are subject to inherent risks and uncertainties, as future experience may vary from historical experience. The reader should consider the applicability of these models, assumptions, and predictions for the future, and whether additional margins for conservatism should be included. Ruark accepts no responsibility for actual results or future events. The opinions expressed in this material are valid only for the purpose stated herein and as of the date indicated. No obligation is assumed to revise this material to reflect changes, events or conditions, which occur subsequent to the date hereof. All decisions in connection with the implementation or use of advice or recommendations contained in this material are the sole responsibility of the reader. This material does not represent investment advice nor does it provide an opinion regarding the fairness of any transaction to any and all parties. 2

44 Source: LIMRA Variable Annuities Fixed Indexed Annuities Gross Sales (p.a.) ~$150 billion ~$100 billion Net Sales (p.a.) ~$0 billion? % Qualified 65% 55% % Guaranteed Living Benefit 3 77% 68%

45 Overview of VA Industry Experience

46 VA Industry Data 25 participating companies 2008 to present 77 million contract years of exposure $905 billion account value 5

47 Surrenders vary by living benefit type Surrender Rate 30% GMWB None GLWB GMIB 6 0% 7 or or more more Years Remaining in Surrender Charge Period

48 Experience varies by company, but why? GLWB, Normalized by Years Remaining in Surrender Charge Period 150% 100% Average 50% 7

49 Most GLWBs are actuarially out-of-the-money 89% OTM 54% 36% 7% 3% Nominal 7% 4% 0% Actuarial ATM ITM 5-50% ITM 50+% 8

50 GLWB moneyness basis matters Surrender Rate 25% Spike - nominal Spike - actuarial 0% OTM 25%+ OTM 5-25% ATM ITM 5-25% ITM 25-50% ITM % ITM 100%+ 9

51 Surrender Rate GLWB income utilization affects surrenders 25% Excess None Full or Less Than 0% 7 or or more more Years Remaining in Surrender Charge Period 10

52 Income utilization varies by age and tax status GLWB Partial Withdrawal Frequency 100% Qualified Nonqualified 0% < Attained Age 11

53 Income commencement is the key question GLWB Partial Withdrawal Frequency 100% Qualified Continuation Nonqualified Qualified Commencement Nonqualified 0% Attained Age 12

54 GMIB annuitizations are low Actuarial basis 20% % 13 ITM 100+% ITM % ITM 25-50% ITM 5-25% ATM OTM 5-25% OTM 25+%

55 Guarantees can affect mortality too 150% % of Ruark Mortality Table GMDB only GLWB 0% Duration

56 Mortality effects are amplified by policy size 150% % of Ruark Mortality Table GLWB GMDB only 0% 15 <$50k $50-100k $ k $ k $500k-1mil $1mil+ Account Value

57 Overview of FIA Industry Experience

58 FIA Industry Data 16 participating companies 2007 to present 17 million contract years of exposure $215 billion account value 17

59 FIA surrenders vary based on interest credited Surrender Rate 35% 0-2% All others 18 0% 10 or more or more Years Remaining in Surrender Charge Period

60 Behavioral Analytics Framework

61 Industry Data Traditional Analysis Statistical Techniques Expert Judgment 20 20

62 Model Development Start with maximum data set (industry) Extract relevant subset for a company Develop a model on this basis Do likewise using only company s data Customize model to reflect both, so that most important factors are included, with stable coefficients, balancing goodness-of-fit and predictive power You can go far with Generalized Linear Models (GLM) 21

63 Logistic Regression Model ln μμ 1 μμ = ββ 0 + ββ ii xx ii Log of odds is a linear function of key factors Binary values, such as surrenders or deaths 22

64 Goodness of Fit Predictive Power 23 23

65 Bayesian Information Criterion Rewards goodness-of-fit to historical data, but penalizes for additional factors used in your model One of many metrics to help guide your model selection process 24

66 Actual-to-Expected Ratios Predictive Power in the new vernacular Develop E using train data, compare to A from test data Out-of-sample, out-of-time, and k-fold cross-validations Examine in aggregate, by cohorts, and over time Look at range of outcomes and tails 25

67 Expert Judgment is Vital Business context, sensibility, materiality, parsimony Let the data speak More data usually beats more complex models Build simple models for complex data, and complex models for simple data 26

68 Sample Model

69 VA GLWB / GMIB Income Utilization Attained Age Tax Status Historical Income Utilization Contract Size Interactions 28

70 Using industry data For each factor coefficient, standard error terms typically very small ~ 1/300 to 1/100. σσ μμ are Then testing predictive power using 5-fold cross-validation, average A/E errors are also very small ~ 1/

71 Using company-only data In some cases, company-only data is insufficient to even identify the key factors observed in the industry data, or it demonstrates factor coefficient estimates that are not sensible. Even if they do, the coefficient standard error terms be 20x larger. σσ μμ can Similarly, the average cross-validation A/E errors can be 10x larger. 30

72 Combining industry and company-only data A customized combination of industry and company-only data can produce a vastly superior model with much better fit and predictive power. Such a model should identify and quantify the effects of each additional factor in the presence of the others, and the interactions between them. Confidence increases with additional data. 31

73 The power of more data For GWLB / GMIB income utilization, need to address complexities of frequency and severity relative to guarantee amounts. Customized model using industry data can reduce error by half where it matters most, for Full income utilization. 32

74 Discussion