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Session 050 PD - Product Development in the InsurTech World
Moderator:
Brock E. Robbins, FSA, FCIA, MAAA
Presenters: Anthony C. Laudato, FSA, MAAA
Leonard Mangini, FSA, MAAA Patrick Sullivan
SOA Antitrust Compliance Guidelines SOA Presentation Disclaimer
Session 50
Product Development in an InsurTech World
SOA Annual MeetingOctober 2017
Moderator: Brock Robbins, SCOR Presenters: Tony Laudato, Hannover Re
Patrick Sullivan, Munich Re Leonard Mangini, Mangini Actuarial & Risk Advisory
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Product Development in an InsurTech World
Marketing trends driving InsurTech – Tony Laudato, FSA
Challenges: data, predictive analytics and limitations –Patrick Sullivan, FSA
Regulatory and professional limitations –Leonard Mangini, FSA
Audience Polling
We’ll be asking you some questions using the polling devicesThere are 10 choices on the keypads 1/A, 2/B, 3/C, and so on
through 9/I, 0/J so we’ll label our available replies that way to help you pick your answersSome of our questions will be “pick as many as apply” so we’ll
show you how to select 2 or more responses for theseWe’ll jump in now with a warm-up question to test the
devices!
2017 SOA Annual Meeting: Session 50:Product Development in an InsurTech World Oct 16, 2017
Audience Response System Keypad
Enter your response when you see the answer now button
A light on the keypad will indicate your response was recorded
You may change your answer while polling is open
No need to hit go
Answer Now
Product Development in an InsurTech World
5A. B. C. D. E. F.
28%
16%
30%
19%16%
9%
Which of the following adjectives would you use to describe actuaries? (Please choose as many as apply)
TEST Polling Question #0
A. BrilliantB. Extraordinarily WittyC. Less Socially Adept than
AccountantsD. More Socially Adept than
AccountantsE. I’m a consultant, how would
you like me to describe you…?F. Don’t quit your day job…let’s
get to the real poll and the presentation
Product Development in an InsurTech World
6A. B. C.
21%
48%
31%
How would you describe your company’s investment in InsurTech?
Polling Question #1
A. Heavily investedB. Moderately investedC.Lightly invested
Product Development in an InsurTech World
7A. B. C. D. E.
17%
8%
37%34%
5%
Where are your company’s InsurTech dollars being spent?Polling Question #2
A. Distribution supportB. Targeting new marketsC.Product
design/developmentD.Underwriting without fluidsE. All the above
Product Development in an InsurTech World
8A. B. C.
28%23%
49%
Customer engagement is critically important to a life insurer’s long term success. Where does Insurtech offer the most promise?
Polling Question #3
A. Reach / understand new target markets
B. Faster / less invasive underwriting
C.Efficiencies / lower business development costs
Product Development in an InsurTech World
9A. B. C. D. E.
50%
4%
27%19%
1%
Are you using, or do you plan to use 3rd party or “purchased data” for use in your InsureTech underwriting?
Polling Question #4
A. YesB. No, we are concerned
about Fair Credit Reporting Issues
C.No, we feel that data is hard to draw conclusions from
D.Haven’t thought about itE. Organization still weighing
pros/cons
Product Development in an InsurTech World
10A. B. C.
16%
58%
26%
• Have you considered making your InsureTech product “Principles-Based Reserves” Friendly?
Polling Question #5
A. Yes, and its worth doing soB. Yes, considered it, but
decided to worry about it laterC.No, didn’t occur to us.
Product Development in the InsurTech World
SOA Annual MeetingOctober 2017Boston, MA
Market Trends Driving InsurTech
The Life Insurance Challenge
US Population and Number of Life Policies
Closing the Coverage Gap
100
150
200
250
300
350
1945 1980 2013
US Population (in millions) Number of Life Insurance Policies
LIMRA 2015
13
More and More Households UninsuredAnd increasingly turning online for information
55%
50%
28%
25%
28%
40%
20%
22%
32%
2016
1998
1960
Household Individual Life Coverage
No Members Covered All Members Covered Some Members Covered
14
Focus for Disruption
15
Customers and Buying Preferences
Distribution
New Technology New Data
InsurTech is Here
32
InsurTech Characteristics
LifestyleEngagementValue over PriceCustomer First
SimplicityExperiencePersonalizationConvenience
InsurTechCharacteristics
What is Possible in the New World?
Enabling a Complete Digital Insurer
BUY NOW
Insurance Policy
Web and mobilesales enablement
Mobile experiencesfor policyholders
Insurer engagement center
1 2 3
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Social IntegrationStart integrating with policyholders in real-time
Cross-selling CapabilitiesIncreases lifetime value of customers by selling relevant products
GeolocationLeverage geolocation data to better service your customers
Rewards & ChallengesKeeps customers actively engaged
Device-connectedEnables gamificationpossibilities with health devices
Policy ManagementView, update, and engagewith policy information
Apply & BuyOnline policy purchase with automated underwriting
Platforms can Enable Multiple New Ways to Engage Clients
20
20
End Customer Matters
Product design is consumer focused rather than distribution focused
Products are simple to understand for consumers and regulators
Purposeful engagement is key to building long term value through increased sales and retention
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New Product Development
New Product Development
Consumer Driven Product Development and Design
Speed to Market
Chassis Flexibility
Forward Thinking Regulator Discussions
New and Different Available Data Sources
Movement from “Rate Classes” to “Cost”
Building New Monitoring Procedures
What do Actuaries Need to Focus On?
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Conclusions
Given the trends in the life and annuity markets, new strategies are required to spur top and bottom line growth
The future is about Product. Customer engagement and experience are crucial to success going forward
More customers are comfortable engaging digitally on their own schedule with a shorter attention span than in the past (3 second rule)
While new underwriting techniques and paradigms are important and “hot”, we need to make sure that we are continually monitoring, analyzing and adjusting to properly be assessing risks
No one company can “do it all” and Partnerships will rule the day
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Questions?
Tony Laudato, FSA, MAAAHannover Re
Vice PresidentPartnership Solutions
200 South Orange Avenue, Suite 1900 Orlando, Florida 32801
Phone: (407) 996-2450Mobile: (413) 695-2386
tony.laudato@hlramerica.comwww.hlramerica.com
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Product Development in the InsurTech World
October 16th, 2017Patrick Sullivan
The challenge
1. Insuretech is driving…
new distribution models new data sources and analytic technique new expectations
2. Leading to situations where we're making risk assessment decisions…
with less information (no fluids) with new information instantaneously on new populations
3. Demanding we change estimations of…
mortality (morbidity) lapse
27Integrated Analytics
© 2017 Munich American Reassurance Company. All Rights Reserved.
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PrescriptionsInsurance History
DrivingRecord
Application& Tele-
Interview
Credit
available risk assessment tools / data:
Medical Lab
Results
Attending Physician Statement
Income & financial
infotraditional data:
Predictive models- Pre-screen- Risk class- Behavior
Decision
Rules-based Automated UW
Manual UW
Electronic health
records
Accelerated underwriting landscape and shift in data sources
Integrated Analytics
© 2017 Munich American Reassurance Company. All Rights Reserved.
WearablesLifestyle / Social
How predictive analytics support the shift
Make underwriting decisions instantaneously
Setting assumptions for mortality / morbidity impact
Monitor underwriting decisions
Competitive advantage
Integrated Analytics 29
© 2017 Munich American Reassurance Company. All Rights Reserved.
Setting assumptions: start with data
Applications•Both accepted and declined
Quantity•Approximately 1-3 years•Better quality data in terms of predictors and number of observations results in more robust models
Format•One observation per row is preferred but not required (columns are attributes/variables and the target is the outcome we are trying to predict, i.e., UW risk class)
•Data must be structured (i.e., no pdf or word documents)
Typical Variables •Application•Demographics•Tele-interviews•Third party data•Medical•Claims
30Integrated Analytics
© 2017 Munich American Reassurance Company. All Rights Reserved.
31
Build predictive model to simulate risk selection
preferred
standardAssign cost to misclassification
© 2017 Munich American Reassurance Company. All Rights Reserved.
Quantify misclassification
Model Predicted Probabilities
Case #Actual
UW Class Class 1 Class 2 Class 3 Class 4Predicted UW Class
Case 1 Class 1 96% 3% 1% 0% Class 1Case 2 Declined 2% 47% 29% 23% Class 2Case 3 Class 2 11% 68% 16% 5% Class 2Case 4 Class 1 73% 16% 8% 3% Class 1Case 5 Class 3 63% 6% 29% 2% Class 1
• Raw model output : Probabilities of being in the each of the classes
• The predictive model assigns a probability that the case belongs to each of the available classes.
© 2017 Munich American Reassurance Company. All Rights Reserved.
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Quantify mortality estimate
© 2017 Munich American Reassurance Company. All Rights Reserved.
Change in mortality cost
1) Initial estimate based on the actual vs predicted class misclassification matrix and the relative mortality impact
2) Refine with a Present Value of Death Benefit (PVDB) approach using pricing assumptions
• Calculate the PVDB per thousand for each combination of age, gender, face amount band, risk class and level term period, etc
• Calculate (A): PVDB based on the predicted (automated) risk class (A)
• Calculate (B): PVDB based on the actual FUW risk class
• Excess Mortality ($) = B – A, floored at zero
• Sensitivity testing to better understand the range of outcomes
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Leverage commercial mortality scores
Integrated Analytics
• New 3rd party tools, such as• Lexis Nexis Risk Classifier• Transunion TrueRisk Life• Milliman RxScore
Retrospective studies -- great where possible
35
© 2017 Munich American Reassurance Company. All Rights Reserved.
Statistical techniques isolate the impact of the scores and provides confidence bands around estimates
Integrated Analytics
WorseMortality
Score
BetterMortality
Score
WorseMortality
Score
BetterMortality
Score
Take-aways
1. Data is key
Support assumption setting Tracking results
2. Predictive analytics / machine learning
For instantaneous decisions For simulating underwriting Both custom / commercial
3. Recognize limitations of analytics
Anti-selection Hold-outs / audit
36Integrated Analytics
© 2017 Munich American Reassurance Company. All Rights Reserved.
Product Development in the InsurTech WorldThank you!
Patrick SullivanSVP Integrated Analytics
Psullivan@munichre.com
Leonard Mangini, FSA, FRM, FALU, CLU, MAAAPresident and Managing MemberMangini Actuarial and Risk Advisory LLC
2017 SOA Annual MeetingSession 50: Product Development in an Insure Tech World
Regulatory and Professionalism ConsiderationsMonday, October 16, 2017
Presenter BiographyLeonard Mangini, FSA, FRM, FALU, CLU, MAAAPresident and Managing Member, Mangini Actuarial and Risk Advisory LLC
Mr. Mangini brings clients over 27 years of industry expertise, holding senior Product, Reinsurance, Financial,and Risk Management-related industry roles at Manulife, ACE, AXA, and USLIFE and assisting clients withFinancial Reporting, Risk Management, Underwriting, Product Development, Reinsurance, M&A, and Litigationissues as a consultant with E&Y, Milliman, and now his own 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 served on the Global Product Risk Committee.
In prior reinsurance roles, Leonard served as an internal Board member, President, Chief Actuary, Chief PricingOfficer, and Chief Risk Officer, co-founding a US life reinsurer. He’s one of the few US actuaries also credentialedby exam as a medical underwriter, and has priced mortality, morbidity, longevity, and policy behavior forproducts issued in fully underwritten and alternative distribution channels.
Mr. Mangini serves on the SOA Joint Risk Management Section Council, previously Chaired the FinancialReporting Section Council, and served on the Marketing and Distribution and Reinsurance Section Councils.He’s a Member of the Academy’s PBR Life Reserve Work Group (LRWG), the ASOP 11- Credit for Reinsurance-Update Committee, and the Committee on Professional Responsibility (COPR). Leonard is a Fellow of theSociety of Actuaries (FSA), a Certified Financial Risk Manager (FRM), Fellow of the Academy of LifeUnderwriting (FALU), and Member of the Academy of Actuaries (MAAA).
2017 SOA AM: Session 50: Product Development in an Insure Tech World Oct 16, 2017
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Liability Disclaimer, Copyright, Use of Slides
Although I’ve attempted to faithfully capture the letter and spirit of regulatory and Actuarial Standard ofPractice constraints, you have a personal professional duty to familiarize yourself with the original sourcematerial and apply professional judgment as to its specific application to your own work and those workingunder your direction as you perform covered Actuarial Services. The nature of your work, and otherprofessional designations you hold, may require you to be bound by additional professional requirementsfrom other organizations as well.
This material has been prepared for general informational purposes only and is not intended to be reliedupon as accounting, legal, tax, or other professional advice, nor is it an Actuarial Opinion by Leonard Mangini,Arnold Dicke, Tim Cardinal, Steve Stockman or their respective firms, Mangini Actuarial and Risk Advisory LLC,AADicke LLC, or Actuarial Compass LLC. Please refer to your advisors for specific professional advice. Theviews expressed by the presenter are not necessarily those of Mangini Actuarial and Risk Advisory LLC,AADicke LLC or Actuarial Compass LLC.
Much of the original source material on VM-20/PBR and Professionalism is copyrighted material of theAmerican Academy of Actuaries, Society of Actuaries, or National Association of Insurance Commissioners.This presentation paraphrases these for educational purposes to capture the intent of the regulations andstandards of practice or results of SOA research, and every attempt has been made to identify and citeoriginal sources.
These slides may NOT be copied, redistributed, or otherwise furnished to any party without prior writtenconsent of Mangini Actuarial and Risk Advisory LLC, other than as required to comply with an audit of theattendee’s annual CPE compliance.
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2017 SOA AM: Session 50: Product Development In an Insure Tech World Oct 16, 2017
Insure Tech Regulatory Considerations
PBR: Delay or Act Now?, SOA/Academy/NAIC Developments, ASOPs• VM-20 §9.A: Prudent Estimate Assumptions• VM-20 §9.C Mortality Assumptions and Segments• ASOP 12 on Risk Selection and many others as we will briefly highlight• Academy and NAIC Developments
Federal Regulations Impacting Underwriting and Risk Selection• Genetic Info under US (GINA Act of 2008), and Canadian (GNDA of 2017) Federal Law• Anti-Money Laundering (AML), Patriot Act, Foreign Asset Control (OFAC) • Fair Credit Reporting Act (FCRA)• McCarron-Ferguson vs. State Law
State-Level Regulations Impacting Underwriting and Risk Selection• Sources Summarizing Existing and Emerging State laws• NAIC Model Insurance Information & Privacy Protection Act• NAIC Model Unfair Trade Practices Act• Delayed Risks Inherent in File & Use Product Jurisdictions
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Insure Tech and PBR- Delay or Start Considering Now?
PBR and Valuation Manual apply in almost** every state…should I care for Insure Tech?
• 3-year phase-in permits adopting on “policy-group” basis…I can put this off…right?• Small Co and Single-State exclusions may exempt Company from PBR outright
“Its difficult enough to get Insure Tech right…why are you even talking to me aboutmaking it ‘PBR-friendly’ at the same time?…that makes it even more complicated!”
**All but NY, MA, and AK, and territories (DC, Puerto Rico, Guam etc)- but these are in progress
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Insure Tech and PBR- Why Consider This Now?
PBR IS complicated!...BUT
Avoiding tough decisions NOW won’t make it any easier to deal with them LATERAND you WILL need time to get it right!
• Anecdotal reports from switching to 2017 CSO shows perils of underestimating effort of a change-over• Will bump-up against resource constraints of “mainstream product” PBR Implementation• Insure Tech could possibly alter the Company’s overall PBR implementation, especially if these
products begin to take large shelf-space in the overall product portfolio of the 2020s• Under PBR Governance (VM-G) one or more “Qualified Actuaries” have new legal duties involving
documentation and reporting to the Board and Senior Management, oversight over assumptions, modelsand processes, and controls over these new processes
Model Audit Rule/SOX environment- so NOT easier to navigate later when there is less timeDon’t discover this when it’s too late and have to pull Insure Tech products while re-tooling!
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
VM-20 Mortality Grading
Split 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 or Grading Period Applicable Industry Data
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2017 SOA AM: Session 50 ©2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Valuation-Side Consequences of Delaying PBR for Insure TechMight have to redesign Insure Tech paradigm from scratch to make PBR compliant…Why?
• NO “Deterministic Exclusion Test” for Term, must calculate NPR and Modeled Reserve to see which “wins” Requires process to develop “Prudent Estimates” for material risk drivers for BOTH Term and ULSG
Many “levers” at Company’s discretion that legitimately lower PBR reserves
• Valuation Mortality: Company Period uses experience within Company-Chosen “Mortality Segments”• “Locked In” Credibility Basis Choice: Limited Fluctuation vs. Bühlmann with Commissioner approval to switch
If develop Insure Tech without considering PBR “levers” could have rude surprise later:• If Insure Tech risk selection eventually developed under PBR is “too different” from other products could force the
creation of new “mortality segment” at 1/1/2020 with little to no statistical credibility, AND under the VM-
IF Credibility < 20%, MUST use industry mortality for entire valuation projection with up to 20.4% credibility margin PBR Reserves for Insure Tech products that ignore VM CHOICES when designed might be greater than under XXX**
** “A VM-20 Mortality and Credibility Factor Observation”, by Tim Cardinal, Principal, Actuarial Compass LLCSOA “Small Talk” newsletter: https://www.soa.org/Library/Newsletters/Small-Talk/2017/september/stn-2017-iss48.pdf
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Valuation-Side Consequences of Delaying PBR for Insure Tech
BUT …if design Insure Tech products NOW using a process that strategically explores the use of available, defensible PBR discretion and then model, document, and defend these choices to show that they are statistically credible…
• May be able to include Insure Tech products in same Mortality Segment as existing products• Might be able to use “Company Experience” for many durations• Might be able to use assumption margins based on ‘higher credibility”
Which could significantly lower reserves that would apply once you move over to PBRPositive side-effect of forcing you to dive deep and understand your Insure Tech models
Current subject of ACTIVE work by SOA/Academy working with NAIC to develop guidance
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Insure Tech and the Valuation Manual
PBR and Professionalism Considerations• VM-20 §9.A: Prudent Estimate Assumptions• VM-20 §9.C: Mortality Segments and Assumptions• SOA and Academy Joint Task Force Activity and NAIC Guidance• ASOPs applicable to PBR van be view through functional activity lens• ASOP 12 on Risk Selection
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
VM-20 §9.A: Prudent Estimate Assumptions
Material risks over which Company has influence and not prescribed or stochastically modeled:• Mortality, Morbidity, Lapses, Partial Withdrawals• Premium Persistency, Loans, Rider/Option Election• Non-Guaranteed Elements (NGE) such as Interest Crediting, Expenses
VM-20 §9.A.c:• “For risk factors that have limited or no experience” sound actuarial judgment, most relevant available data
VM-20 §9.A.d:• “For such assumptions (under VM-20 §9.A.c)… the Qualified Actuary… shall use sensitivity testing and
disclose analysis performed to ensure the assumption is at conservative end of the plausible range”• ASOP 1: “shall” means NOT doing so is a violation of actuarial standards of practice! Good guard-rail to ensure that your Insure Tech underwriting does not stray too far into “new territory” and provides opportunity to demonstrate and document that experience is NOT limited
PBR ASOP:• Reflect PH view of policy value/embedded options- customers are self-interested rational actors• Reflect expected Management Actions based on ERM in practice- CANNOT reflect “hypothetical” future NGE
actions, hedging or other responses to policyholder behavior/markets- only documented/implemented ERM
Ought to have well-documented, management-approved ERM: reflect potential management actions andmonitoring, indicators, risk escalation, roles/responsibilities applicable that might trigger these actions
2017 SOA AM: Session 50 ©2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
VM-20 §9.A: Setting Prudent Estimate Assumptions
Prudent Estimate Assumption= Anticipated Experience + Margin
Anticipated Experience: Company’s 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 (with ASOP 1 interpretation of shall)
Margins:• Adverse Deviation/Estimation Error implies understanding complex predictive models well enough to
determine inherent estimation error, (perhaps) favoring models with low errors for lower reserves. In past could always try to price for uncertainty in data/methods but PBR MUST reserve for uncertainty too!
• Margin magnitude reflects uncertainty implies careful choice of Insure Tech variables/data sets• Must include element of conservatism consider error ranges in supporting studies and publications• Must include margin on each material risk driver assumption and must tend to increase reserves implies understanding complex predictive models well enough to consider interactions/correlations and robust
testing in order to understand directional impacts• e.g. lapse-supported products where uncertainty in persistency spills over into mortality uncertainty
Exposed ASOP on Models has VERY detailed requirements on assumption/model governance
2017 SOA AM: Session 50 ©2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
VM-20: Mortality SegmentsVM-20 §9.C.1.a:
“Company shall determine mortality segments for purpose of determining separate prudentmortality assumptions for groups of policies that the Company expects will have different experiencethan other groups of polices”
Implies Company can group together policies with similar expected experience
Considerations for Segmentation: • VM explicitly mentions: Gender, Smoker Status, Underwriting Risk Class, but then says “etc.”• Actuarial Standard of Practice 12 (discussed below) requires more
• Mortality used for the “Industry Period” assumption for a mortality segment must, per the Guidance Note to VM-20 §9.C.3.d take into account adherence to stated underwriting rules and exceptions
--> Insure Tech automated underwriting might result in stricter adherence to stated rules but also create “knockouts” for human underwriting susceptible to more exceptions impacts the “Industry Period” table
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2017 SOA AM: Session 50 ©2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
VM-20: Sources of Mortality Data and JustificationVM-20 §9.C.2.b:
“Company experience data shall be base on experience from the following sources:i) Actual Company experience for books of business within the mortality segmentii) Experience from other books of business within the Company with similar underwritingiii) Experience data from other sources, if available and appropriate…if the source has underwriting and
expected mortality experience characteristics that are similar to policies in the mortality segment”
VM-20 §9.C.2.c:
“The company experience mortality rates shall not be lower than the mortality rates the company expects to emerge which the company can justify and which are disclosed in the PBR Actuarial Report.
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2017 SOA AM: Session 50 ©2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
VM-20: Adjusting Mortality Segment Assumptions
VM-20 §9.C.2.f:
“The Company may adjust the company experience rates for each mortality segment to reflect the expectedincremental change due to the adoption of risk selection and underwriting practices different from thoseunderlying the company experience data identified above, provided that:
• The adjustments are supported by published medical or clinical studies or other published studies thatcorrelate a specific risk selection criteria to mortality or longevity experience (for example, criterion andcorrelations determined through predictive analytics); and
• The rationale and support for use of the study and for the adjustments are disclosed in PBR Actuarial Report.
Guidance Note: It is anticipated that the adjustment described in 9.C.2.f to experience will rarely be made. Sincethese adjustments are expected to be rare, and since it is difficult to anticipate the nature of these adjustments, thecommissioner may wish to determine the level of documentation or analysis that is required to allow suchadjustments. The NAIC may want to consider whether approval by a centralized examination office would be anacceptable alternative to approval by the commissioner.”
CAUTION: The meaning and substance of the terms “Incremental Change”, “Rationale and Support”, “Disclosure”,“level of documentation” are currently unclear and are being addressed by Joint SOA/Academy Group working withthe Academy’s Life Reserve Work Group (LRWG) to support the NAIC to develop temporary official guidance
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
VM-20 and Margins (VM-20 §9.C.5)
Separate Margins for Company and Industry Mortality Experience rates
A percentage Increase applied to each mortality rate• Company margin varies by attained age/credibility via table lookup• Bühlmann or Limited Fluctuation method “locked-in” at issue• Must request and receive approval from Commissioner to switch prudent to investigate BOTH credibility
measures to choose the best one implies testing BOTH when building models for Insure Tech paradigm
Size of margin Increased for uncertainty, including:• Imprecise methodology or “staleness” implications for “quality” of predictive analytics methods and testing• Underwriting or risk selection criteria have changed materially since experience study implies demonstrating
that Insure Tech underwriting variables not materially different or “worse” than existing and quantifyinguncertainty
• Lack of homogeneity implications for data used in “mining”, hold-back, back-testing, A/B testing etc. • Changes in marketing/admin creating anti-selection- obvious concern if sold online, w/o agent, paramedic, field UW• Ineffectiveness of underwriting compared to expectations- implies need for “control cycle” or feedback
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
SOA/Academy Activity and NAIC GuidanceAcademy/SOA Joint Committee on Simplified Issue (SI) and Accelerated Underwriting (AUW)
Attempting to “draw boundary” between “fully underwritten”, Accelerated, and Simplified Issue
• SI/AUW Work Group: GI/SI/AUW Definitions Subgroup, PBR Valuation Considerations/Recommendations• AUW POG: Experience Studies Considerations and VM-51 Experience Reporting Recommendations
PBR Valuation Considerations/Recommendations (VM-20 Reserving Subgroup)
• Identify current valuation practices for different underwriting protocols and where guidance is needed• NOT determining appropriateness of emerging UW techniques• Focus is on Modeled Reserves• Contract w/3rd Party to draft questions, get volunteers, conduct Delphi Study on Emerging UW and Mortality• Long-Term Goal: Categorizing UW Practices and Adjusting Base Mortality Tables for these practices• Short-Term Goal: NAIC Guidance for 12/31/17 and 12/31/2018 valuation and future Changes to VM itself• Academy Life Reserve Work Group (LRWG): actively working with sub-group to make recommendations to LATF
Among Topics to be Addressed:
• Can different mortality segments be combined for determining credibility?• What are appropriate Margins for groups of policies using Accelerated Underwriting?• Can existing Company experience be adjusted for Accelerated Underwriting under VM-20 §9.C.2.f by considering it an “incremental
change”? What type of support and documentation are adequate to convince regulators?
Source: August 4, 2017 SOA/Academy Presentation by Mary Bahna-Nolan to NAIC Life Actuarial Task Force• https://www.actuary.org/files/publications/Acad%20to%20LATF%20AUW%20Update%20080417.pdf
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2017 SOA AM: Session 50: Product Development In an Insure Tech World Oct 16, 2017
Insure Tech: PBR and “Function-Driven” ASOPs• ASOP 1- Introductory- defines “should”, “may” and deviations• ASOP 2- NGE- which are critical in modeled reserves, and part of reflecting management actions• ASOP 7- CF Models- since VM permits these to be leveraged for PBR Modeled Reserves• ASOP 10- US GAAP- since it has important considerations for margins• ASOP 12- Risk Classification- since drives VM-20 Mortality, choice of Tables• ASOP 21- Assisting Auditors/Examiners- since QA has Model Audit Rule/SOX Role• ASOP 22- Asset Adequacy Opinions- AA reliance in AOMR are changing• ASOP 23-Data Quality- since use of own experience and models is crucial• ASOP 25- Credibility- since involved in mortality, other modeled assumptions• ASOP 41- Communication- due to need to document data, decisions, methods• ASOP 45- Risk Evaluation in ERM- since PBR reflects actual risk underwriting/results• ASOP 46- Risk Treatment in ERM- since PBR reflects actual risk practices• ASOP on Models and ASOP on Pricing- covered in other Sessions at this Meeting
March 2017 Contingencies “PBR: Who, What, and How”- Dicke and Mangini• Discusses role of Company, Appointed Actuary, Qualified Actuary and the above ASOPs• http://www.contingenciesonline.com/contingenciesonline/march_april_2017?pg=40#pg40
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2017 SOA AM: Session 50 ©2016-17 AADicke LLC and Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
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 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
or the expected outcome of a risk classification system
Actuary should, in the ASOP 1 sense, satisfy applicable law AND this standard if applicable law conflicts with standard, compliance with law NOT a deviation, provided actuary
discloses that assignment or work product was produced in accordance with law ASOP 41
Source: ASB Board Website-• http://www.actuarialstandardsboard.org/wp-content/uploads/2014/07/asop012_101.pdf
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2017 SOA AM: Session 50: Product Development In an Insure Tech World Oct 16, 2017
Actuarial Standard of Practice 12§§ 3.2/3.3: Actuary SHOULD consider the following in Selecting Risk Characteristics:
• Expected outcomes- demonstrate variation in actual/reasonable expected anticipated experience correlates with risk drivers §3.2.2 causation is 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 equitable if differences reflect material differences in expected cost
• Interdependence- Should consider interdependence to extent expect to have material impact on system shouldmake appropriate adjustments
• Inferences without Demonstration- sometimes appropriate to infer without a specific demonstration e.g. serious impairments are obviously higher risks- don’t need to prove the obvious
• Objectivity- should select risk characteristics capable of objective determination (measurable) based on readily observable facts not easily manipulated. e.g. define conditions with test results
• Law and Business Practices: Should consider constraints of applicable law and business/industry practices
• Homogeneity (§3.32): variation in outcomes within a risk class too great subdivide. If too granular to be credible consider combining proposed risk classes to balance predictability and homogeneity
Source: ASB Board Website-• http://www.actuarialstandardsboard.org/wp-content/uploads/2014/07/asop012_101.pdf
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2017 SOA AM: Session 50: Product Development In an Insure Tech World Oct 16, 2017
Insure Tech and Federal Regulations
• US Genetic Information Non-Discrimination Act (GINA) of 2008• Canadian Bill S-201 of 2017• AML, OFAC and Implications for Direct-to-Consumer and Field Underwriting• Fair Credit Reporting Act (FCRA) and HIPAA• McCarron-Ferguson vs. State Law
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
US Federal GINA Act of 2008
US Genetic Information Non-Discrimination Act (GINA) of 2008
Prohibits discrimination on basis of genetic info with respect to health insurance and employment
• Does NOT apply to life insurance, LTD or LTC insurance• Prohibits group health plans and health insurers from denying coverage to a healthy individual or charging
higher premiums based solely on genetic predisposition to developing future disease• Bars employers from using individual’s genetic info in hiring, firing, job placement, or promotions• Does NOT have a “disparate impact theory” provision which refers to adversely impacting one protected
group of people versus another class- i.e. race, color, religion, national origin, disability status or gender.Under disparate impact, a “facially neutral” employment policy that has an adverse impact would still beconsidered discriminatory without having to prove an intent to discriminate.
Rationale- foster genetic research and prevent “chilling effect” against patients seeking diagnosis
• In practice a few insurers ASK if you’ve had a genetic test in Part 2 of their application, but I don’t personally know of any that REQUIRE one to undergo a genetic test as part of underwriting
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Canadian Bill S-201 GNDA Act of 2017
Canada, Bill S-201, Genetic Non-Discrimination Act passed into law on May 4, 2017• Criminalizes requiring individual to undergo genetic test for provision of goods and services or as condition
to enter into or continue a contract. ALSO forbids refusing services agreement if refuse to disclose results ofprior genetic test- subject to $C 1 Million fine and/or 5 years in prison.
• Effectively bars requiring disclosure of prior genetic tests in underwriting Canadian insurance applicants
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Federal Regulations: AML, OFAC and “Covered Products”Anti-Money-Laundering (AML)*: • 31 USCS § 5318(h)(1): requires all financial institutions to have Anti-Money Laundering Program• 31 USCS § 5312(a)(2): defines “financial institution” to include insurance companies• Treasury regulation 31 CFR §103.137: prescribes written AML program for “covered products”• Covered Products: individual permanent cash-value and individual annuity productsMust include:• internal policies, procedures, and controls; designation of a compliance officer; ongoing employee training
program; and an independent audit function to test AML programs
Office of Foreign Asset Control (OFAC)**:• Enforces trade sanctions; national security actions on targeted countries, terrorist organizations, narcotics
traffickers, and WMD proliferation. Applies to all US entities and thus life insurers
If Insure Tech risk classification system is direct-to-consumer for “covered products” need to consider how to collect identifying information, report suspicious activity/violations so as to be in compliance
• NOT a concern for Term insurance unless it builds cash values (such as ROP or Older Issue Ages)
Useful Sources: * NYS Department of Financial Services: http://www.dfs.ny.gov/insurance/ogco2008/rg080106.htm** SEC Website for Broker Dealers: https://www.sec.gov/about/offices/ocie/amlsourcetool.htm#1
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Federal Regulations: Fair Credit Reporting Act (FCRA)
If “Consumer Reports” used in underwriting or risk classification must comply with FCRA• “Traditional UW” uses Consumer Reports: MIB, MVR, Credit Reports, Criminal Records, Inspection Reports, Lexis/Nexis• Emerging practices ALSO considered Consumer Reports: Prescription (Rx) Databases, Electronic Health Records (EHR)
Providers of Such Data: must register as Consumer Reporting Agencies (CRA), satisfy compliance rules• Credit Rating Agencies. MIB, Milliman IntelliScript®, Lexis/Nexis all appear in CRA list (link to 33-page PDF below)**
FCRA Requirements:• Must have “permissable purpose” for obtaining such data: insurance underwriting is in scope• Must have oral, electronic, or written consent to obtain such data, per FCRA §604(g)• HIPAA release for Medical Info per FCRA §604(g)(1)(A) for CRA to release report• “Adverse Action”: denied, rate increase, coverage terminated- based partly/completely on Consumer Report• Must securely dispose of Consumer Report when finished using implications for historical underwriting files
FCRA §615(a) requires written notice of adverse action, contact info for CRA, inform right to dispute report content
Resources-FTC- What Insurers Need to Know: https://www.ftc.gov/tips-advice/business-center/guidance/consumer-reports-what-insurers-need-know
**CFPB (Consumer Financial Protection Board) 2016 list of Consumer Reporting Agencies:http://files.consumerfinance.gov/f/201604_cfpb_list-of-consumer-reporting-companies.pdf
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
FCRA: Collected versus Purchased Data for Insure Tech
Insure Tech paradigms may involve collecting data directly from prospects or policyholders• Insurer might “mine” existing policyholder underwriting data files as part of search for risk variables or testing
expected effectiveness of proposed alternative underwriting protocols, retention/reward programs etc• Insurer might collect existing policyholder data from “wearable” devices
Must be cognizant of whether party performing services might be considered a CRA, or whether original datasources for historical data being mined was obtained from CRA could have FCRA Adverse Notice Implicationsbased on actions taken and also FCRA Consumer report data destruction implications…check with legal!
Insure Tech paradigms may involve purchasing data about prospects/policyholders from 3rd parties
• Is 3rd party provider registered as a CRA?- check with Legal to ensure use of data complies with FCRA• Does your Alternative Underwriting program use “purchased data” as part of its algorithm or predictive model? Have you factored in FCRA Adverse Notices into processes, controls, and its consumerist implications?
ASOP 12 and Valuation Manual only require “correlation” in risk classification and mortality segmentation, but insured or agent might feel “causation” is required to receive an adverse underwriting action Could lead to market conduct complaints or lawsuits and suggests that from an ERM perspective your
assumptions, models, documentation, and rationale for Insure Tech risk classification might need to meeta higher “public scrutiny” test rather than mere compliance with Actuarial Standards or Valuation Manual
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Disparity between Federal/State Law and McCarran-Ferguson
Supremacy Clause of US Constitution (Article VI, Clause 2):
“This Constitution, and the Laws of the United States which shall be made in Pursuance thereof; and all Treaties made, or whichshall be made, under the Authority of the United States, shall be the supreme Law of the Land; and the Judges in every Stateshall be bound thereby, any Thing in the Constitution or Laws of any State to the Contrary notwithstanding.”
McCarran-Ferguson Act (15 USC §§1011-1015) exempts business of insurance from MOST Federal Regulation (particularly anti-trust) does NOT shield states from Federal Law regarding unfair discrimination
If Federal Law is MORE restrictive than state law, then Federal Law “wins” If State Law is MORE restrictive than Federal Law then the State Law is NOT pre-empted
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
State Regulations Impacting Underwriting and Risk Selection
Topics• Sources Summarizing Existing State Law• NAIC Model Insurance Information & Privacy Protection Act• NAIC Model Unfair Trade Practices Act• Delayed Risks Inherent in “File & Use” Product Filing Jurisdictions
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
State-Level Regulations on Underwriting- Existing Research
“Understanding Insurance Anti-Discrimination Laws”. Avraham, Logue, Schwarcz. (March 2013)*• Open-source Working-Paper from University of Michigan Law School that goes into great detail.• Of course, should check with your legal department for current information.
First in-depth scholarly article examining State Insurance Discrimination Laws. Section 2 tries to predict where regulations might emerge and Section 3 provides a compendium of laws that authors could find
• 9 categories: race, religion, national origin, gender, age, genetics, credit score, sexual orientation, geography
ACLU has summary of existing and pending state-level genetic testing regulations **• AZ, , MD, MT, NJ- have “limited” (in their opinion) legislative protections for life insurance
Anecdotal:• NY- prohibits debits for breast cancer survivors after 3 years• Some states- prohibit debits for victims of domestic violence, intellectual impairment, blindness
Source(s):
(*): http://repository.law.umich.edu/cgi/viewcontent.cgi?article=2733&context=articles
(**): https://www.aclu.org/other/summary-laws-regarding-genetic-discrimination
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
NAIC Model Insurance Info & Privacy Act (MDL-670)- 16 states
Scope: Insurers, agents, “support organizations” collect, receive, maintain Info on transactionsProtects: Subject of info collected; applicants, individuals, policy-owners seeking transactions“Adverse UW Decision”: Decline, Terminate, Failure Agent to Apply, Issue Higher Standard“Consumer Report”: credit, character, reputation, personal characteristics, mode of living Practices:• Bans Pretext Interview (unless claim, not “privileged”, suspect crime, fraud/material misrep)• Requires Notice of Info Practices at time of delivery if data from applicant, and at the time collection of
information is initiated if NOT from applicant or public sources• Identify questions used for marketing or research versus underwriting transaction• Need Permission for Investigative Consumer Reports, Consumer Can Request Copy• Consumer can request access to Recorded Personal Info, must supply within 30 days• Consumer request corrections- must be made or refused in writing in 30 days with reason• Subject can dispute, insurer must include dispute statement when disclosing info in future• Must supply reason for adverse decision unless crime, fraud, material misrep/non-disclose
MDL 670 and MDL-672 to be combined and replaced by new NAIC “Cyber Model Law”
Source: http://www.naic.org/store/free/MDL-670.pdf
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
NAIC Model Unfair Trade Practices Act (MDL-880)
Purpose: Regulates Trade Practices in “business of insurance”: McCarron-Ferguson and GLBProtects: “Customer” purchases, applies to purchase, solicited to purchase insuranceUnfair: Anything in Section 4 if flagrant, conscious disregard, frequency = general practice• Section 4.G Unfair Discrimination
1- “Making or permitting any unfair discrimination between individuals of the same class and equal expectation of life in the rates charged for any life insurance policy…benefits payable…or in any other terms and conditions of the policy”5- “Refusing to insure…continue to insure, or limiting the amount of coverage available to an individual based on sex, marital status, race, religion, or national origin…”7- “Refusing to insure solely because another insurer has refused to write a policy
• Section 4.H Rebates1- “…knowingly permitting or offering to make or making any life insurance policy…any rebate of premiums…special favor or advantage in…benefits…or any valuable consideration or inducement whatever not specified in the policy…”2- “Nothing in Subsection G or Paragraph 1 of H shall be construed as…discrimination or rebates in the case of life insurance…paying bonuses to policyholders or otherwise abating their premiums in whole or in part out of surplus…from non-participating insurance, provided that…fair and equitable to policyholders and for the best interests of the company and its policyholders”
Source: http://www.naic.org/store/free/MDL-880.pdf
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Delayed Risks Inherent in “File & Use” Product Jurisdictions
• Many jurisdictions are “file & use” with respect to premium rates
• Market conduct issues might only be discovered at time of state examination
• Creates risk that Insure Tech “black-box” inadvertently discriminates against a protected class but not “uncovered” by regulator until product has been on market for several years
Suggests that accelerated underwriting and predictive analytics “engines” be robustly tested to ensure they’re not a complicated “proxy” for discriminating against protected classes.
Consult with legal over use of new variables, while you are developing and testing new underwriting protocols, i.e. BEFORE you ever get to the stage of issuing new business!
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2017 SOA AM: Session 50 © 2017 Mangini Actuarial and Risk Advisory LLC Oct 16, 2017
Contact Information
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Leonard Mangini, FSA, FRM, CLU, FALU, MAAAPresident , Mangini Actuarial and Risk Advisory LLC
E-mail: leonard@manginiactuarial.comWeb: www.manginiactuarial.comMobile: (516) 418-2549
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