Predictive Analytics in Life Insurance

Advances in Predictive Analytics Conference, University of Waterloo

December 1, 2017

Format of this session

Speakers:Jean-Yves Rioux - DeloitteKevin Pledge – Claim AnalyticsIan Bancroft – Sun LifeEugene Wen – Manulife

Discussion and Questions

PredictiveAnalyticsinlifeinsurance

Jean-YvesRiouxAdvancesinPredictiveAnalyticsConference,UniversityofWaterloo

December1,2017

Beyonddescriptivestatistics

Evolutionoftheanalyticsprocess

• Predictiveanalyticsistheuseofdatatogeneratepredictiveinsightsinordertomakesmarterdecisionsthatimproveperformanceofbusinessesanddrivestrategytooutlastthecompetition.Analyticsshouldgobeyonddescriptionofthepastandshouldprovideactionableinsightsaboutthefuture.Thisdiscussionwitheducateyouonthefullpotentialoftheapplicationoftechniques.

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Hindsight

Insight

Foresight

Simulationandmodelling

Quantitativeanalyses

Advancedforecasting

Exceptionsandalerts

Sliceanddicequeriesanddrill-downs

Managementreporting

Optimizationalgorithms

Predictiveandprescriptive

Role-basedperformancemetrics

Descriptive

Enterprisedatamanagement

…andwhyresearching/understandinganalyticsiscomplex

Overallmethodology

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Phase UnderstandingBusiness Problem

Data Collection DataCleansing AnalysisofData ModelSelection

VariableSelection

ParameterizationandOptimization

ResultsPresentationandImplementation

Description Identify thekeyquestionstoanswer,problemstosolve,andbusinessconstraints

Collectandunderstanddataandvariablerepresentations

Validate,standardize,andtransformdata

Analyzevariables,distributions,correlations,andclustering

Identifyappropriatemodellingoptionsbasedonbusinessproblemanddataavailable

Identifypreliminaryvariablesthatappeartobesignificantpredictors

Traindifferentmodelsandvariablecombinationstodeterminetheoptimalmodelandparameters

Presentresultsinamannerthatcanbeunderstoodbyseniormanagementofvarioustechnicalbackgrounds.Embedthepredictivemodelintobusinessprocesses

Predictiveanalyticsapplications

Otherapplications• ProvideinsightsusingHR/workforceAnalytics

SalesandMarketing• Buildmoreeffective,targetedmarketingcampaigns• Identifycustomerslikelytopurchaseproducts• Provideappropriateproductrecommendations

AgencyManagement• Recruitadvisorsmostlikelytobecomesuccessful• Optimizeagentretentionefforts• Monitorperformanceofagents

Underwriting• Identifybestrisksandstrategicallyprioritizeunderwritingefforts• Improvesimplifiedunderwritingandstreamlineunderwritingprocesses• Expandunderwritinginformationusingnewsources

InforceManagement• Profileandsegmentcustomers• Identifyandretainpolicyholderslikelytosurrender• Designretentionstrategiesandofferadditionalproductstocurrentcustomers• Analyzecustomersatisfaction

Pricing,Reserving,andExperienceStudies• Developaccurate,competitivepricingandpricingstructure• Identifynewexperiencedriversusingaugmenteddata• Improvereservingaccuracy• Improveunderstandingofexperiencedrivers

ClaimsManagementandFraudDetection• Analyzeclaimfrequencyandseverity• Prioritizeclaimsresources• Identifylikelyfraudulentorrescindedclaims

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Applications1. Cross-selling/ Up-selling2. Risk-Based Segment Targeting3. Distribution Partner/ Client Matching4. New Business Application Triage/ Simplified UW5. Underwriting Process Improvement6. Underwriting Application Simplification7. Customer Retention/ Lifetime Value8. Pricing Risk Score9. Experience Studies Policyholder Behavior10. Experience Studies Mortality11. Fraud Detection12. Workforce Analytics

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Applications1. Agency Management2. Claims Management3. Product Design4. Direct to Consumer Targeted Marketing5. Predictive Underwriting

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• CIAPredictiveModelingCommittee’smissionistopromotetheapplicationoftheactuarialskillsettopredictivemodelingandtomarketactuariesasskilledexpertsinthisfield.Morespecificallythecommitteewill:

• a)Promotetheroleofactuariesinthepredictivemodelingfieldbothwithinandoutsidetheprofession;

• b)Identifyneedsfornewresearchinthefieldofpredictivemodelingthatwilldrawattentiontotheworkofactuaries;and

• c)Communicateexistingresearchandcasestudiesofworkinthefieldthroughvariousmedia

• d)InfluencetheEducationCurriculumandCPDofferingtoincluderelevantpredictivemodelingtechniques

• CIAPredictiveModelingCommittee’sinitiativesincluded:

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Promotionwithin/outside Needsforresearch Communication/Dissemination Influencecurriculum Collaboration/RelationshipProfileSeriesine-bulletinofPredictiveModelers(storiesfromactuariesandnon-actuariesandtheirworkinPM,startwithcommitteemembersandthenexpand),target3withinfirstyear

Organizeawebcast GetonCPDsessions (Ongoing)Connectwithotherorganizationstocoordinate/collaborate

Bookletaboutwhyandapplications MonitorCPDopportunitiesandinformtheCIAforinclusionintheCPDopportunitiese-mail

ProvideinputintotheCIAcurriculuminsupportofthealignmentwiththecurriculumadoptedbyotherorganizations(CASandSOA)

OrganizeaCIAeventWeb-basedindexofstrongreferencedocumentsrelatingtoTechniquesandApplications[completed]

ExpandtheWeb-basedindextoincludelinkstotools[completed]

Theeffortsoftheprofession– CanadianInstituteofActuariesinitiatives

Predictive Analytics in Life Insurance

Kevin PledgeClaim Analytics

Advances in Predictive Analytics Conference University of WaterlooDecember 1, 2017

Predictive analytics success seen in P&C and other industries – life and health slow to adopt

Why was that?

Long term nature of the business

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Claim Analytics c.2000 saw an opportunity in claim management for group disability, based on expected outcome:

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Claim Scores

Highest potential for return to work (a good claim manager will have biggest impact)

PermanentResolve Quickly

Don’t spend expensive resources

Expense management

In Between

Return to Health

Highest potential for success with investment of proactive and solid CM strategies. Likely to have a longer duration and require more specialized resources and experienced CMs.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Prob

abilityofR

TW

ClaimScore

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9

10

5

6

7

2

3

4

Low Touch

Require solid but generally straightforward CM strategies –often less complex claims with early RTW focused interventions.

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Expense Mgmt

Alternate resolution strategies such as SSDI offsets and settlements should be considered as the likelihood of a solid outcome with investment of other resources is less likely.

Claim Scoring6 Months

• Claim Assignment• Early

opportunities

12 months

• Claim Assignment• Resource

allocation

18 months

• Resource allocation

• Transition

24 months• Settlements

Can this be applied to Short Term Disability?

Duration Management

Transition Management

Resource Allocation

0

20

40

60

80

100

0-7 8-14 15-21 22-28 29-35 36-42 43-49 50-56 57-63 64-70 71-77 78-84 85-91

Cum

ulat

ive

Term

inat

ions

Use predictive models to compare companies

Benchmarking

RehabClaim Management

Claim Performance

Related models?

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1. Need to show value (initially quickly)2. Start simple and build from there3. Revisit & Refresh – always check in with the business

Summary & Final Thought

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New businessUnderwriting

Don’t limit yourself

Fraud

PredictiveModelingIANBANCROFT,SUNLIFEFINANCIAL,DECEMBER1ST, 2017

DescriptionofSLFModelingTeamsTheQuestion….ShouldwecentralizeordecentralizethemodelingfunctionintotheBusinessUnits(BU’s)?ie developoneareaofexpertmodelers….butwithoutbusinessexpertise,orshouldwemovethemodelersclosertothebusiness?Similarquestiontohowtoorganizetheactuarialfunction

DescriptionofSLFModelingTeamsTheAnswer….We’veplacedmostofourmodelersintoteamsintheBU’s,andalsohaveasmallcentralizedteam….similartoouractuarialorganizationalstructureThemakeupofeachmodellingteamdependsontheneedsoftheirwork…..eg avaluationpredictivemodelingteamisverydifferentthanafraudteamWespendalotoftimeconnectingacrossthedifferentmodelingteamstopromotesynergiesandbestpractices

DiversityofSkillsRequiredforModelingWorkNoonepersoncanbeanexpertinallmodelingtechniques,dataengineering,businessknowledge,changemanagement ….thereisno“IvoryTower”formodelersRatherateamofpeopleisneededGoodcommunication,collaborationskillsareveryimportant

GrowingandBuildingModelingCommunitiesAsmodelingbecomesmore“mainstream”,creatingamodelingcommunityacrossyourorganizationbecomesincreasinglyimportantThereisnooneuniquesolution……thingswearedoinginclude◦ Annualanalytics1dayconferences◦Monthlydeepdiveswithrotatingspeakers◦ Biweeklystatusmeetings◦ Aninternalcompetition(similartoKaggle)◦ InternaltrainingcoursesWefrequentlyexperimentwithandadjustourprocessestoimproveoursolutions

PredictiveAnalyticsinLifeInsurance

EugeneWen,MD.DrPH.VP- GroupAdvancedAnalyticsManulife

AdvancesinPredictiveAnalyticsConferenceDecember1,2017UniversityofWaterloo

Let’slookbacktohistoryofanalyticsininsurance…

Beforetheterm“DataScientist”wascoined,ActuaryhadservedasTHE“datascientist”ofinsuranceindustryformorethantwohundredyears.

…since the first Actuary

• Society for Equitable Assurances on Lives and Survivorship in London in 1762

• The first actuary: Edward Rowe Mores• Role of actuary

• Business Analysts: underwriting, claim management, marketing, investment, finance, risk, strategy, IT, BI & reporting etc.

• Data Scientists

Advanced Analytics in Manulife/John Hancock (1/2)

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CindyForbes,EVP,ChiefAnalyticsOfficer,

formerGlobalChiefActuary,

ChairofGovernors,UniversityofWaterloo

Advanced Analytics in Manulife/John Hancock (2/2)

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§ Over 60 FTEs globally

§ Approximately 25 in Asia

§ Advanced Analytics teams consist of highly educated and specialized talent across the globe

§ 15 PhD’s among the team members

§ Programs include: Pattern Recognition and Machine Intelligence, Math & Statistics, Computational Physics, Machine Learning, Computational Neuroscience, Computer Science, Economics, Medicine

§ 20 Masters degrees among team members

§ Programs include: Systems & Engineering, Statistics, Management Analytics, Computer Science, Electrical Engineering, Finance and Information Systems

§ 7 MBAs

DATA SCIENCE

Modernized Underwriting

Text Analytics

Computer Vision

Buying a life insurance product canbe a lengthy and time-consumingprocess. How can predictiveanalytics improve the underwritingapproval process?

MODERNIZED UNDERWRITING

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We have billions of customerservice transcripts. How can weuse this data to improve thecustomer experience?

TEXT ANALYTICS03

Our life insurance applicants mustprovide a lot of information to beunderwritten for a policy. Can weimprove this process by extractingfeatures from a photo?

COMPUTER VISION

04 Fraud Detection

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Data Science Projects (1/2)

We have billions of transactionsannually. The challenge for the datascience team is to identify fraudulenttransactions in all of the data.

FRAUD DETECTION

Data Science Projects (2/2)

How can we identify those that are likely to be dishonest on their application form?

Predicting Smoker Status

Life experiences don’t occur often. How do we ensure our model is performing as expected going forward?

Monitoring Experiences

Can we develop a model that significantly reduces the

time to underwrite a policy for qualified applicants?

Predictive Underwriting

How can we improve the life insurance

purchasing process for our applicants?

Purchase Process

*MortalityStudy/Liability/Nextyearpayout*LTClapseafterpremiumincrease*Segfund lapse

ThankYou

Jean-YvesRioux - DeloitteKevinPledge– ClaimAnalyticsIanBancroft– SunLifeEugeneWen- Manulife

AnyQuestions?