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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

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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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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


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


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


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


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


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


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

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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

[email protected]

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http://www.hlramerica.com/

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


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


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



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Build predictive model to simulate risk selection

preferred

standardAssign cost to misclassification


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.


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Quantify mortality estimate


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


• New 3rd party tools, such as• Lexis Nexis Risk Classifier• Transunion TrueRisk Life• Milliman RxScore

Retrospective studies -- great where possible

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Statistical techniques isolate the impact of the scores and provides confidence bands around estimates


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



Product Development in the InsurTech WorldThank you!

Patrick SullivanSVP Integrated Analytics

[email protected]

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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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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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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https://www.soa.org/Library/Newsletters/Small-Talk/2017/september/stn-2017-iss48.pdf

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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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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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


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


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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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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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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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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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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https://www.actuary.org/files/publications/Acad%20to%20LATF%20AUW%20Update%20080417.pdf

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

http://www.contingenciesonline.com/contingenciesonline/march_april_2017?pg=40#pg40

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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http://www.actuarialstandardsboard.org/wp-content/uploads/2014/07/asop012_101.pdf

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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http://www.actuarialstandardsboard.org/wp-content/uploads/2014/07/asop012_101.pdf

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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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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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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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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http://www.dfs.ny.gov/insurance/ogco2008/rg080106.htm

https://www.sec.gov/about/offices/ocie/amlsourcetool.htm#1

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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https://www.ftc.gov/tips-advice/business-center/guidance/consumer-reports-what-insurers-need-know

http://files.consumerfinance.gov/f/201604_cfpb_list-of-consumer-reporting-companies.pdf

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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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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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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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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http://repository.law.umich.edu/cgi/viewcontent.cgi?article=2733&context=articles

https://www.aclu.org/other/summary-laws-regarding-genetic-discrimination

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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http://www.naic.org/store/free/MDL-670.pdf

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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http://www.naic.org/store/free/MDL-670.pdf

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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Contact Information

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Leonard Mangini, FSA, FRM, CLU, FALU, MAAAPresident , Mangini Actuarial and Risk Advisory LLC

E-mail: [email protected]: www.manginiactuarial.comMobile: (516) 418-2549

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