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Utilizing Advanced Pricing Methodologies: Accurately Establishing Cost and Revenue Thresholds to ccu ate y stab s g Cost a d e e ue es o ds toEnable Competitive Price Setting
Mike Paczolt, FCAS, MAAAMilliman
August 3, 2011
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About MillimanAbout Milliman
Actuaries and other consultants
Independent – Not broker or insurance carrier
Over 2,100 Employees
Offices in most major cities globally
22
Why use advanced pricing methodology?Why use advanced pricing methodology?
Competition
Profitability
Risk
Underwriting Decisions
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Adverse Selection – Year 1Adverse Selection Year 1PRICE
Low Risk High RiskCompany A $25 $75Company B $50 $50
# of PoliciesLow Risk High Risk
Company A 1 000 1 000Company A 1,000 1,000Company B 1,000 1,000
Company B – Profit SummaryCompany B Profit SummaryLow Risk High Risk
Profit Per Policy +$25 -$25
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# Policies x 1,000 x 1,000Total Profit +$25,000 -$25,000
Econ 101Econ 101
Demand
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Price
Adverse Selection – Year 2Adverse Selection Year 2PRICE
Low Risk High RiskCompany A $25 $75Company B $50 $50
# of PoliciesLow Risk High Risk
Company A 1 500 500Company A 1,500 500Company B 500 1,500
Company B – Profit SummaryCompany B Profit SummaryLow Risk High Risk
Profit Per Policy +$25 -$25
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# Policies x 500 x 1,500Total Profit +$12,500 -$37,500
Adverse Selection – Year 3Adverse Selection Year 3PRICE
Low Risk High RiskCompany A $25 $75Company B $50 $50
# of PoliciesLow Risk High Risk
Company A 2 000 0Company A 2,000 0Company B 0 2,000
Company B – Profit SummaryCompany B Profit SummaryLow Risk High Risk
Profit Per Policy +$25 -$25
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# Policies x 0 x 2,000Total Profit $0 -$50,000
2 Classes of Pricing Analysis for Warranties2 Classes of Pricing Analysis for Warranties
Cost Per Exposure
• High level analysis
Predictive Analytics
• Identifies patterns in data
• Average historical cost per policy
• Captures relationship between claims and
li h t i ti• Often segmented by
product type
policy characteristics
• Accounts for correlation b libetween policy characteristics
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Probability is a function of…Probability is a function of…
Family History Age Lifestyle DiseaseHistory g y
On-base % ERA Slugging
%Baseball
Wins
Product Age Supplier Dealer
Extended Warranty Claims
Predictive modeling attempts to convert these tendencies into a
Claims
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g pmathematical formula
Predictive AnalyticsPredictive Analytics
One-Way Linear Regression
Multivariate Linear Regression
Market Segmentation
Other advanced techniques are becoming more popular (e.g. machine learning, price optimization, etc.)
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SegmentsSegments
Location – Zip Code
Brand/Product Type
Dealer/Salesman
Factory
Product Age
Manufacturer/SupplierManufacturer/Supplier
Parts/Components
Customer Demographics
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One-Way Linear Regression ExampleOne Way Linear Regression Example
$140
$160 Cost Per Unit by Product Age
$100
$120
$140
nit
$60
$80
$100
ost P
er U
$20
$40
$60
Co
$00 1 2 3 4 5 6 7 8
P d t A (Y )
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Product Age (Years)
Inter-DependenciesInter Dependencies
Supplier X55%
85%45%
Sold in ILCustomerCredit Score
125%
Sold in ILCredit Score <300 75%85%
90%
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Multivariate Linear Regression ExampleMultivariate Linear Regression ExampleCost Per Unit by
Product Age & Supplier
$150
$200
$100
$150$150-$200$100-$150$50-$100
CD$0
$50$ $$0-$50
AB0 1 2 3 4 5 6 7 8
Supplier
Product Age (Years)
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Market Segmentation – How does it work?Market Segmentation How does it work?Dealer 1Brand A
4 000 PoliciesBrand A
7,500 Policies
4,000 Policies
Dealer 2
Initial Population10,000 Policies
Brand A3,500 Policies
0,000 o c es
Brand B
Dealer 1Brand B
1,500 PoliciesBrand B
2,500 PoliciesDealer 2Brand B
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1,000 Policies
Market Segmentation – Example of ResultsMarket Segmentation Example of Results
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Building a Predictive ModelBuilding a Predictive Model
Implementation
Model
Implementation• Pricing• Underwriting Decisions
Data
• Create Model• Validate Model
Data• Gather Data• Prepare Data
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Data GatheringData Gathering
Sales / Policy Database– Location, supplier, product type, etc…
Claims Database– Number of claims by type, claim values amounts labor/parts, etc…
External Database– Credit score, customer purchase history, etc…
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Data PrepData Prep
Clean data is crucial
May exclude suspect data
Not uncommon to eliminate 10% to 25% of records
Data can be held back to validate modelData can be held back to validate model
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Create ModelCreate Model
Decide purpose of model– Claim Frequency
– Claim Severity
Loss Ratios– Loss Ratios
– CPU
Iterative process
Use one-way analysis to identify important variables
Group variables together
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Group variables together
Model ValidationModel Validation
Monitor “best fit” based on stats
Correlation vs. Causality
Back-testing on holdout sample
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Predictive Modeling ResultsPredictive Modeling Results
Sophisticated statistical model identifying key traits of claims that answers:– What segments of my policies am I making money?
– What is my price floor?What is my price floor?
– Are certain dealers/salesman underperforming peers?
– What is causing my warranty claims?
– Should I reduce or expand coverage?
– Who should I sell my extended warranties to?
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QuestionsQuestions
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