Transcript Predictive Modeling Daniel Finnegan, PhD ISO Innovative Analytics Quality Planning Corporation

Predictive Modeling
Daniel Finnegan, PhD
ISO Innovative Analytics
Quality Planning Corporation
Insurance Rating Knowledge
Perfect
Knowledge
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Horoscope
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Physics
Insurance Rating Knowledge
Perfect
Knowledge
Random
Standard Rating Predicts 1.2% of Variance
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Physics
Insurance Rating Knowledge
Perfect
Knowledge
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Million Policy Grouping Predicts 86% of Variance
Individual Policy Predicts 1.2% of Variance
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Physics
The Law of Large Numbers:
The Las Vegas Advantage
Claim Cost by Premium
$1,600
$1,400
Claim Cost
$1,200
$1,000
$800
$600
$400
$200
$0
$100
$400
$700
$1,000
$1,300
Premium
$1,600
$1,900
$2,200
$2,500
Credit, Premium and Claim Costs
Claim Costs by Premium and Credit Rating
$2,400
Claim Costs
$2,000
$1,600
$1,200
Good Credit
Bad Credit
$800
$400
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6
$
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9
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$1
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Annual Premium
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Conditions for Additional Rating Data
Predictive of Loss
 Uniformly Available
 Legally and Socially Acceptable
 Low Error Rate
 Updateable
 Verifiable
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Price Differentiation: Progressive
Price Points for Each Carrier
Price Points
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20
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C
4
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26
G
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Source: InsurQuote and McKinsey & Company
High
Resolution
Underwriting
ZIP Code 94109
ZIP Code 94109: A Tour
Fishermen’s Warf
Robert Louis Stevenson
declared "Nob Hill, the
Hill of Palaces, must
certainly be counted the
best part of San
Francisco."
Japantown
The Tenderloin: "...the haunt of
the low and vile of every kind.
….Licentiousness, debauchery,
pollution, loathsome disease,
insanity from dissipation, misery,
poverty, blasphemy and death are
there. And Hell, yawning to receive
the putrid mass, is there also. “
High Resolution Underwriting: Address
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Street and Intersection Characteristics
Building Characteristics
Business and Community Environment
Weather
Crime
Demographics
Topology
Over 1,000 Other Variables
High Resolution Underwriting: Rating the
Risk
Rate the address not the ZIP
 Rate the vehicle not the symbol
 Rate the person not the group
 Rate the combination, not the
isolated elements
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Use of Predictive Models
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Marketing
Sales Compensation
Underwriting
Segment
Rating
Down Payment
Customer Relations
Renewal
Predictive Modeling:
A Lesson from the Credit Industry
Standards for Insurance Models
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Reflects proper use and
understanding of insurance data
Validated
Impact of Model on Book Understood
Reflects Proper Use and Understanding of
Insurance Data
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Modeler should understand the fine points
of insurance data collection
 Insurance accounting e.g. calendar/accident
year
 Claim adjusting and loss reserving process
Reflects Proper Use and Understanding of
Insurance Data
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Highly skewed nature of insurance loss
data
 Most insurance policies do not have losses
 Those that do have “heavy tails”
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Includes the Structure of Insurance
Policies such as Limits and Deductibles
Validated
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Validated on Independent Sample
Validated across Time
Validated across the Industry
Validated across Book Segments such as
 Type of Risk
 Region
 Size of Risk
Impact of Model on Book Understood
Region
 Business Segments
 Sales Force
 Prior Years
 Business Plans
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