Sriteja Chavva, Gabby Ignacio, Jazz Laosirichon, Eva …actuary/caseCompetition/2019...Sriteja...

SritejaChavva,GabbyIgnacio,JazzLaosirichon,EvaMars

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AGENDA

Part1 Part2 Part3 Part4IntroducBon Analyzinga

NewRaBngSystem

TacklingKeyBusinessIssues

Conclusion

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GOALS

RecommendRaBngSystem

AMractYoungerCustomers

ImproveRetenBon

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NEWRATERS

Vehicle RateV1 $150V2 $260V3 $370

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Driver FactorMom 0.90Dad 0.95Teenager 2.10

Driver VehicleMom V1Dad V2Teenager V3

DriverAssignmentDriverAveraging

$150×0.90$260×0.95$370×2.10

} $1159Averagedriverrate:(0.90+0.95+2.10)/3=1.32

($150+$260+$370)×1.32=$1030

WINNERS&LOSERS

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Profile DriverAveraging

DriverAssignment

1 $3.1k $2.9k

2 $3.9k $3.6k

3 $1.1k $1.1k

NoCOMP/COLLforriskiestdriver

Riskiestdriver≠primarydriver

Only1vehicle&driver

CONSIDERATIONSFORRATERSYSTEM

Policyholdermix

ManipulaBonfromagents/customers

PricefluctuaBonsfromswitch

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GeneralizedLinearModel

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Response

COLLISONCOVERAGE

Method

GLM

Data

VARIABLES

CONSIDERATIONSFORGLMs

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Countrywide

• Moredata• ProtecBonfromsuddenfluctuaBons

• Simpler

Statewide

• Statelaws• Pointsystem• DrivingcondiBons

• Marketshare

GLMOUTPUTISSUES

Driverpoints

•  Indicatedfactor(1point>2points)

Driverexperience

• Minimaldifference

Goodstudent

• Needstoberecategorized

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ADJUSTMENTS

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Modelyear

Combinecategories

Drivingexperience

ADDITIONALVARIABLESTOCONSIDER

Lowstandarderror

Vehicleuse MulBpolicy

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RETENTION

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Accidentforgiveness FullpaydiscountPersistencywithcompany

YOUNGCUSTOMERS

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ReferralprogramMobileapp

GoodStudent

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SHORTCOMINGS

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ì  Notgivenpolicyholderdatatomakeconcretebusinessdecisions

ì Morevariables

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