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Cutting Edge Tools for Pricing and Underwriting SeminarIntegrating External Data into the Decision Making / Predictive Modeling Process
Casualty Actuarial Society
by Serhat Guven
© 2011 Towers Watson. All rights reserved.
by Serhat GuvenFall 2011
Agenda
Background
Data warehousing basics
External data sources
Case study: Elasticity Models
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Background
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Background
Goals of predictive modelingProduce a sensible model that explains recent historical experience and is likely to be
Response Systematic Unsystematic1. Separate the random
p p ypredictive of future experience
Response Variable
Systematic Component
Unsystematic Component= +
Signal: Function of the Rating Factors/Predictors
Noise: Reflects stochastic process
pcomponents from the systematic components of the estimator
Overall mean Best Models One parameter per observation
Factors/Predictors
2. Balance predictive d l t
Underfit: Overfit:
power and explanatory effects
Model Complexity (number of parameters)
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PredictivePoor explanatory power
Poor predictive powerExplains history
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(number of parameters)
Background
Goals of predictive modelingPredict a response variable using a series of explanatory variables
Explanatory variablesAge AccidentsLimit Convictions
p g p y
Predictive Model
t Co ct o sRegion Credit score
ParametersValidation St ti tiModel
Response variablesLossesClaims
Statistics
Retention
Larger data storage capabilities and greater access to external data h bilit t id tif th i ht t f th i k
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enhances ability to identify the right rate for the risk
Background
Many external factors have been found to be predictive of frequency and/or loss severity. Here are a few for auto…
Territory
Garaged
Major Convictions
Traffic Density
Age Minor ConvictionsGender
Method of Payment
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Method of Payment
Background
Modeling is an iterative process
How does the analyst decide How does the analyst decide which factors are most valuable? Parameters/standard errors
Review Model
Parameters/standard errors Consistency of patterns over time
or random data setsT III t ti ti l t t Type III statistical tests (e.g., chi-square tests)
Judgment (e.g., do the trends make sense)
ComplicateSimplify
make sense) Focus of the section is on gathering
data NOT analysis
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Background
Greater availability of data requires better understanding of warehousing principles to ensure proper analysiswarehousing principles to ensure proper analysis Identifying entity groups Merge strategies
External data sources for differing entity groups
Elasticity data set case study Data classes External sources
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Data Warehousing Basics
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Data Warehousing Basics
Basic definitionsPrimary Key uniquely identifies the granular construct to be modeled Primary Key – uniquely identifies the granular construct to be modeled— Policy Number
— Policy Number/Risk
— Policy Number/Risk/Endorsement
Fields – information stored for each granular construct— Explanatory factors/ independent variables/predictors
— Categorizing fields into entity groups
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Data Warehousing Basics
Basic definitionsIndexes important elements in the database Indexes - important elements in the database— Used for key sort and merge elements
— Primary key is the natural index of data
Metrics – measured attributes— Dependent variable/Response
The goal is to identify external data fields that will be merged into the warehouse either on a key or index to better predict the metric
External data are defined as fields that are not in any internal systems
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Data Warehousing Basics
Proper data management and warehousing store and maintain data in relational tablesrelational tables
Statistical models inputs require flattened versions of the data Statistical models inputs require flattened versions of the data
External data can be defined as additional fields that are merged to specific entity groups on the original data
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Data Warehousing Basics
Standard entity groups that can be used to integrate external dataPolicy number Policy number
Named Insured Risk VIN Geographic unit (e.g. census block, zip code, etc) SIC class code SIC class code Building ID ….
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Data Warehousing Basics
Two main strategies used to merge external data
Di t th d fi d tit Direct merge on the defined entity group Credit characteristics of named insured Geodemographic elements of the geographic unit Vehicle attributes of the VIN OSHA attributes of an SIC code
Competitor’s price points Competitor s price points
Indirect merge based on ‘proximity’ to the entity group Characteristics of the responding fire department Traffic conditions of neighboring zipcodes
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Data Warehousing Basics
Issues that will affect the statistical model outputMissing data Missing data— Dummy controls vectors in multivariate models tend to produce biases
Changing data— External data changes over time (e.g. population growth, credit score definition
changes, etc)
— Need to understand assumptions of data feeding the model and application of the modelmodel
— Concerned about additional prediction error
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External Data Sources
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External Data Sources
PersonalPersonal
FinancialCoverage FinancialCoverage
B h i lB i BehavioralBusiness
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Geographical
External Data Sources
Personal Lines
C i l Li
SourcesU.S. CensusNational Highway Traffic Safety AdministrationCommercial Lines National Highway Traffic Safety Administration
Experian Mosaic®
Applied Geographic Solutions
CarfaxHighway Loss Data Institute
FireSafe
PerilVisionPersonal credit
Commercial credit
Occupational Safety & Health AdministrationNCCI/ISO
Safety and Fitness Electronic Records
GPS tracking
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U.S. News
Insurequote/Quadrant
Personal Lines Data Sources - Auto
Personal LinesAuto
SourcesU.S. CensusNational Highway Traffic Safety AdministrationAuto
Homeowners
Commercial Lines
National Highway Traffic Safety Administration
Experian Mosaic®
Applied Geographic Solutions
CarfaxHighway Loss Data Institute
FireSafe
PerilVisionPersonal credit
Commercial credit
Occupational Safety & Health AdministrationNCCI/ISO
Safety and Fitness Electronic Records
GPS tracking
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U.S. News
Insurequote/Quadrant
Personal Lines Data Sources - Homeowners
Personal LinesAuto
SourcesU.S. CensusNational Highway Traffic Safety AdministrationAuto
Homeowners
Commercial Lines
National Highway Traffic Safety Administration
Experian Mosaic®
Applied Geographic Solutions
CarfaxHighway Loss Data Institute
FireSafe
PerilVisionPersonal credit
Commercial credit
Occupational Safety & Health AdministrationNCCI/ISO
Safety and Fitness Electronic Records
GPS tracking
towerswatson.com© 2011 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.
U.S. News
Insurequote/Quadrant
Commercial Lines Data Sources - Workers Comp
Personal Lines
C i l Li
SourcesU.S. CensusNational Highway Traffic Safety AdministrationCommercial Lines
Workers Compensation
Commercial Auto
National Highway Traffic Safety Administration
Experian Mosaic®
Applied Geographic Solutions
CarfaxOther Commercial Highway Loss Data Institute
FireSafe
PerilVisionPersonal credit
Commercial credit
Occupational Safety & Health AdministrationNCCI/ISO
Safety and Fitness Electronic Records
GPS tracking
towerswatson.com© 2011 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.
U.S. News
Insurequote/Quadrant
Commercial Lines Data Sources – Commercial Auto
Personal Lines
C i l Li
SourcesU.S. CensusNational Highway Traffic Safety AdministrationCommercial Lines
Workers Compensation
Commercial Auto
National Highway Traffic Safety Administration
Experian Mosaic®
Applied Geographic Solutions
CarfaxOther Commercial Highway Loss Data Institute
FireSafe
PerilVisionPersonal credit
Commercial credit
Occupational Safety & Health AdministrationNCCI/ISO
Safety and Fitness Electronic Records
GPS tracking
towerswatson.com© 2011 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.
U.S. News
Insurequote/Quadrant
Commercial Lines Data Sources – Other Commercial
Personal Lines
C i l Li
SourcesU.S. CensusNational Highway Traffic Safety AdministrationCommercial Lines
Workers Compensation
Commercial Auto
National Highway Traffic Safety Administration
Experian Mosaic®
Applied Geographic Solutions
CarfaxOther Commercial Highway Loss Data Institute
FireSafe
PerilVisionPersonal credit
Commercial credit
Occupational Safety & Health AdministrationNCCI/ISO
Safety and Fitness Electronic Records
GPS tracking
towerswatson.com© 2011 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.
U.S. News
Insurequote/Quadrant
Case Study: Elasticity Models
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Case Study: Elasticity Models
Data discovery and design is a process
Wh t ff t l ti it What affects elasticity General understanding of the process Classifying elements into groups
Identify external data sources and corresponding timelines
Construction and manipulation
Develop merge procedure
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What affects elasticity
Attributes &Attributes & Attitudes What is the customer like?
Influences What you have done to the customer?
Environmental What are the external influences?
Status Changes & Triggers What has changed and when?
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What affects elasticity?
Affluence Classification
What is the customer like?
Location
Marital Status
Scores
Policyholder Attitudes
Attributes &
Policyholder Characteristics
Age
Attitudes
Attitudes
Vehicle Age
Payment Method
Payment
Vehicle
PaymentsCoverage
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yFrequency Multi
LineLimit
What affects elasticity?
Group Memberships
What have you done to the customer?
Quoted Premium
Rate Change
Packaged ProductsAdditional
Services
Influences
PremiumPremium
Influences
Service Levels
Media Spend
Contact ChannelClaims
Awareness
Customer Relationship
Advertising
B d
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Communications ComplaintsBrand
What affects elasticity?
CampaignsShopping Effi i
What are the external influences?
Campaigns
Relative
Efficiency
Competitor Acquisition
Media Spend
Acquisition Initiatives
EnvironmentalPain Index
Market
Price
Market Competitiveness
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Product
What affects elasticity?
Type Ti
What has changed and when?
yp Time Since
Premium Change
When Purchased
Cross Sell Upgrade /
Downgrade Mid Term Adjustments
Status Ch &
Change
Products Held
AdjustmentsProduct
Holdings & Activity Marriage
Changes & Triggers
Change P t
Children
Death
Life Events
Initial Contact ChannelLapse or
Late
Payment Method
Customer Relationship
Payments
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CommunicationDuration
Late Payment
Identifying External Sources
Location Census Census EASI
Affluence and Classification Scores Third Party Credit Providers (Choicepoint, Experian, L2C, etc)
Vehicle Polk/Jato/HLDI etc Polk/Jato/HLDI etc
Media Spend
Market PricesMarket Prices Insurequote/Quadrant
Indices are constructed via local knowledge
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Timelines
Midt ll ti i hi t i l li iRO0 0 MTAi RO1 1
Historical Datax
C i i hi t i l t Midterm cancellations using historical policies
— Policy records as of t = 0
— PredictorsPolicy characteristics as of RO
Conversion using historical quotes
— Quote records issued from RO0 through RO1
— Predictors– Policy characteristics as of RO0
– Premium offered as of RO0
– Competitor premium as of RO0
— Response: whether or not the policy cancelled before RO1
– Policy characteristics at time of quote– Premium offered at time of quote– Competitor premium at time of quote
— Response: whether or not the quote was t d
Retention using historical policies
— Policy records as of RO1
— Predictors
accepted
– Policy characteristics at time RO1
– Premium change from RO0 (or MTAi) to RO1
– Competitor premium as of RO1
— Response: whether or not the policy accepted renewal as of t = 1
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accepted renewal as of t 1
Construction and Manipulation
Competitor Price Points
Policy level analysis Policy level analysis
Market Indices
Assessing market conditionsg
Recursive Modeling Builds
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Summary
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Summary
External data requires understanding of underlying process
Strong need to identify key drivers of modeling
Classification creates selection expertise
Data warehousing provides discipline
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Contact Details
Serhat GuvenTowers WatsonTowers WatsonSenior Consultant200 Concord Plaza Suite 420San Antonio, TX 78216
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