WCM 2012 - Predictive Modeling: Pricing Service Contracts in a Competitive Environment
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Transcript of WCM 2012 - Predictive Modeling: Pricing Service Contracts in a Competitive Environment
2012®
®
“Predictive Modeling: Pricing Service Contracts in a Competitive
Environment”
Mike Paczolt, FCAS, MAAA
Consulting ActuaryMilliman
®About Milliman• Actuaries and other consultants
• Independent – Not broker or insurance carrier
• Over 2,100 Employees
• Offices in most major cities globally
®
Adverse Selection – Year 1
PRICE
Low Risk High Risk
Company A $25 $75
Company B $50 $50
# of Policies
Low Risk High Risk
Company A 1,000 1,000
Company B 1,000 1,000
Company B – Profit Summary
Low Risk High Risk
Profit Per Policy +$25 -$25
# Policies x 1,000 x 1,000
Total Profit +$25,000 -$25,000
®Econ 101
Price
Demand
®
Adverse Selection – Year 2
PRICE
Low Risk High Risk
Company A $25 $75
Company B $50 $50
# of Policies
Low Risk High Risk
Company A 1,500 500
Company B 500 1,500
Company B – Profit Summary
Low Risk High Risk
Profit Per Policy +$25 -$25
# Policies x 500 x 1,500
Total Profit +$12,500 -$37,500
®
Adverse Selection – Year 3
PRICE
Low Risk High Risk
Company A $25 $75
Company B $50 $50
# of Policies
Low Risk High Risk
Company A 2,000 0
Company B 0 2,000
Company B – Profit Summary
Low Risk High Risk
Profit Per Policy +$25 -$25
# Policies x 0 x 2,000
Total Profit $0 -$50,000
®
2 Types of Pricing Analysis
Cost Per Exposure
• High level analysis
• Average historical cost per policy
• Often segmented by product type
Predictive Modeling
• Identifies patterns in data
• Captures relationship between claims and policy characteristics
• Accounts for correlation between policy characteristics
®
Why use Predictive Modeling?
Pricing Develop accurate rates to maintain profitability and competitiveness
Underwriting Prioritize business for underwriting scrutinySales Target profitable customer base for new
and renewal businessLoss Control Identify root causes of product failures for
quality controlClaims Management
Set thresholds for determining acceptable claim severities
Customer Management
Target highly profitable business for renewals based on lapse rates
®
Probability is a function of…
• Predictive modeling attempts to convert these tendencies into a mathematical formula
Family History Age Lifestyle Disease
On-base % ERA Slugging
%Baseball
Wins
Product Age Supplier Dealer
Extended Warranty Claims
®Predictive Models• One-Way Linear Regression
• Multivariate Linear Regression
• Market Segmentation
• Other advanced techniques are becoming more popular (e.g. machine learning, price optimization, etc.)
®Variables
• Location – Zip Code• Brand/Product Type• Dealer/Salesman• Factory• Product Age/Usage• Manufacturer/Supplier• Parts/Components• Customer Demographics• Service Level
®
One-Way Regression Example
0 1 2 3 4 5 6 7 8$0
$20
$40
$60
$80
$100
$120
$140
$160
Cost Per Unit by Product Age
Product Age (Years)
Cost
Per
Un
it
®Inter-Dependencies
Supplier X
Sold in ILCustomerCredit Score <300
75%85%
55%
85%45%
125%
90%
®
Multivariate Linear RegressionExample
0 1 2 3 4 5 6 7 8
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
A
C
Cost Per Unit byProduct Age & Supplier
ABCD
Product Age (Years)
Supplier
®
Market SegmentationHow does it work?
Initial Population10,000 Policies
Brand A7,500 Policies
Dealer 1Brand A
4,000 Policies
Dealer 2Brand A
3,500 Policies
Brand B2,500 Policies
Dealer 1Brand B
1,500 Policies
Dealer 2Brand B
1,000 Policies
®
Market SegmentationExample of Results
®
Building a Predictive Model
Data• Gather Data• Prepare Data
Model• Create Model• Validate Model
Implementation• Pricing• Underwriting
Decisions
®Data 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…
®Data 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 model
®Create Model• Decide purpose of model
• Claim Frequency• Claim Severity• Loss Ratios• CPU
• Iterative process
• Use one-way analysis to identify important variables
• Group variables together
®Model Validation• Monitor “best fit” based on stats
• Correlation vs. Causality
• Back-testing on holdout sample
®
Predictive Modeling Results• Sophisticated statistical model identifying key traits of
claims that answers:• What segments of my portfolio am I making
money?• What is my price floor?• Are certain dealers/salesman underperforming
peers?• What is causing my warranty claims?• Should I reduce or expand coverage?• Which customers should my sales team target?
®
Sample ResultsUnderwriting Purpose
1 2 3 4 5 6 7 8 9 1025%
50%
75%
100%
125%
150%Loss Ratio by Segment
Segment
Loss R
ati
o
®
Sample ResultsRating Plan
Base RatingProduct Wholesale Price Rate Policy Length Factor
<$1,000 $200 1 Year 1.00$1,001 to $10,000 $300 2 Year 1.90
>$10,000 $1,000
Rating RatingProduct Age Factor Renewal Factor
< 1 Year 1.00 No 1.001 Year to 5 Years 1.20 Yes 0.95
> 5 Years 2.00
®
Sample ResultsSales Purpose
-10% -8% -6% -4% -2% 0% 2% 4% 6% 8% 10%25%
50%
75%
100%
125%
150%
Loss Ratio vs.Relative SC Revenue Growth
Relative Service Contract Revenue Growth
Loss R
ati
o
Questions
