Transcript Deloitte Consulting, 2004

© Deloitte Consulting, 2004
Alternatives to Credit Scoring
in Insurance
James Guszcza, FCAS, MAAA
Cheng-Sheng Peter Wu, FCAS, ASA, MAAA
CAS 2004 Ratemaking Seminar
Philadelphia
March 12-13, 2004
© Deloitte Consulting, 2004
Agenda
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Introduction
The credit scoring revolution
From credit scoring to predictive modeling
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The big idea: credit scoring is just one kind of insurance
predictive (“data mining”) model… many other predictive
models can be built
Conclusions
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What it means to actuaries
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Introduction
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Introduction – Our Efforts
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Is credit for real?
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Going beyond credit
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“Does Credit Score Really Explain Insurance Losses?
Multivariate Analysis from a Data Mining Point of View”
“Mining the Most from Credit and Non-Credit Data”
How do credit scoring models work?
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“A View Inside the “Black Box”: Review and Analysis of
Personal Lines Insurance Credit Scoring Models Filed in
the State of VA”
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Introduction
Which Company is this?
$10
Premium (in Billions)
$9
$8
$7
$6
$5
$4
$3
$2
$1
$0
1994
1995
1996
1997
1998
1999
2000
2001
2002
Year
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Introduction
110%
35%
105%
30%
100%
25%
95%
20%
90%
15%
85%
10%
80%
5%
75%
0%
1994
1995
1996
1997
1998
Year
1999
2000
2001
Progressive Combined
Ratio
Progerssive Growth
Rate
Premium Growth Rate
Combined Ratio
Progressive Insurance Company
2002
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Introduction
Progressive Combined Ratio
Progressive vs. Industry
Industry Combined Ratio
Progerssive Growth Rate
115%
35%
110%
30%
105%
25%
100%
20%
95%
15%
90%
10%
85%
5%
80%
0%
1994
1995
1996
1997
1998
Year
1999
2000
2001
2002
Growth Rate
Combined Ratio
Industry Growth Rate
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Why?
Multiple Choice
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Progressive provided foosball tables and free
snacks to their trendy, 20-something
workforce
Progressive built a compound GammaPoisson GLM model to design their class
plan
Progressive pioneered the use of credit in
pricing/underwriting
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The Credit Score
Revolution
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Personal Lines Pricing and Class
Plans – History
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Few rating factors before World War II
Explosion of class plan factors after the War
Auto class plans:
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Homeowners class plans:
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Territory, driver, vehicle, coverage, loss and violation,
others, tiers/company…
Territory, construction class, protection class, coverage,
prior loss, others, tiers/company...
Credit scoring introduced in late 80s and early
90s
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Personal Lines Credit Scoring –
History
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First important factor identified over the past 2
decades
Composite multivariate score vs. raw credit
information
Introduced in late 80s and early 90s
Viewed at first as a “secret weapon”
Quiet, confidential, controversial, black box, …etc
“Early believers and users have gained
significant competitive advantage!”
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The Current Environment
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Now everyone is using it:
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Marketing and direct solicitation
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New business and renewal business pricing and underwriting
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How to stay competitive if everyone is using it?
Regulatory constraints:
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Many states have conducted studies on the true correlation
with loss ratio and potential discrimination issues - WA study,
TX study, MO study
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Many states have/are considering restricting the use of credit
scores or certain types of credit information
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More states want the “black box” filed and opened
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Some Facts About Credit Scores
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A composite score that usually contains 10 to
40 pieces of credit information
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Loss ratio lift is significant – a powerful class
plan factor or rate tiering factor
Benefits/ROI are measurable
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Payment pattern information, bankruptcies/liens,
collections, inquiries, bad debt/defaults…
Lift curve can be translated into bottom-line benefit
Blind test and independent validation can be
done to verify the benefit
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Loss Ratio Lift Curve
120
90
82
78
Loss Ratio
74
66
70
62
58
50
Credit Score Decile
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Credit Score Revolution Segmentation Power
1997 NCCI/Tillinghast Study of 9 Companies' Data
Loss Ratio Relativity of the Best and Worst 20% of Credit Score
Co1
Co2
Co3
Co4
Co5
Co6
Co7
Co8
Co9
Avg
Best 20%
-38% -29% -19% -15% -14% -34% -22% -22% -36% -25%
Worst 20%
48%
20%
32%
30%
46%
59%
20%
22%
95%
41%
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From Credit Scoring
to Predictive Modeling
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From Credit Scores to Predictive
Models
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What is a predictive model?
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A multivariate scoring formula (linear or nonlinear) that combines the values of several
predictive variables to estimate the value of a
target variable
What is a credit score?
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A multivariate scoring formula (linear or nonlinear) that combines the values of several
credit variables to estimate the value of a
target variable
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From Credit Scores to Predictive
Models
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A credit score is just one example of an
insurance predictive model
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A credit scoring project is a first approximation to a
full insurance data mining project.
The same methods used to build credit
scores are used in data mining to build
insurance predictive models.
The primary difference is in the predictive
information used.
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From Credit Scores to Predictive
Models
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Credit scores are PMs that use only creditrelated variables to predict relative
profitability.
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Payment pattern information, bankruptcies/liens,
collections, inquiries, bad debt/defaults…
But PMs can also be built using
Both credit and non-credit information (preferred)
 Only non-credit information (perfectly feasible)
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Non-Credit PMs
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Why would we want to build a purely noncredit PM?
Competitive advantages – e.g. matrix with credit
 State-specific regulatory constraints
 Expense of ordering credit reports
 Thin files/no-hits
 Public relations
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But from a purely actuarial POV, credit is
predictive  should be used as part of the
PM!
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PMs: Considerations
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The key is to use as much information as
possible
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in a multivariate way
Choice of statistical techniques is important,
but the real key is the quality and breadth of
predictive variables used.
GIGO
 Actuarial/insurance knowledge is critical
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Untapped riches reside in many companies’
transactional records.
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PMs: Data Sources
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We classify possible data sources into two
groups
Internal data sources: predictive information
gleaned from the company’s own systems
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Regardless of how or whether it is currently used
External data sources: predictive information
available from 3rd parties.
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Both credit and non-credit
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Internal Data Sources
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Policy information
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Line-Specific information
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Limits, Deductibles, Measure of exposure (# cars,
#houses, #employees, $sales, premium size…
Driver, Vehicle, Business Class …
Policyholder information
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Age, gender, marital status …
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Internal Data Sources
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Customer-level information
Transactional data
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Coverage, premium and loss transactions
Billing information
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Correlation with credit
Agent information
A little creativity in using these data sources will
go a long way!
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External Data Sources
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Credit
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Predictive both for commercial and personal lines
MVR – CLUE
Zipcode/geographic information
Rating territory
 Many different sources available
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The sky is the limit but
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Consider cost, hit rate, implementation, …etc
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Types of Variables Generated
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Territory-level
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Policy / policyholder-specific
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Demographic, weather, crime, ...etc
Many traditional rating variables fall into this category
Behavioral
Less traditional – fits more neatly into data mining
paradigm than classification ratemaking
 Credit, billing, prior claims, cancel-reinstatements…
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How Many Variables?
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It is possible to generate literally hundreds of
predictive variables
Some will be redundant
 Some will not be very predictive
 Some will be somewhat predictive
 Some will be “killer”
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A good model can contain as few as 15-20 or
as many as 60-70 variables
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Usually no single “ideal” model
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Which Variables to Use?
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Choosing is a major part of the data mining
process
Use variety of exploratory statistical techniques
 Use prior modeling experience / actuarial knowledge
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Several considerations
Actuarial / underwriting knowledge
 Client’s business needs
 Legal / regulatory considerations
 Data availability / cost
 Systems implementation considerations
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In Our Experience….
Do non-credit PMs work?
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YES: non-credit predictive models are
Valuable alternative to credit scores
 Flexible
 Tailored to individual companies
 Leverage company’s untapped internal data
 Comparable predictive power to credit scores
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And mixed credit / non-credit PMs can be
even stronger
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…But It’s Not a Walk Through the
Park
Challenges for PMs:
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IT resources constraints
Project management
Business process buy-in
Success of system and business
implementation
Training and organizational change
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Conclusions
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Industry Trends
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How do companies try to stay competitive
regarding the use of credit?
How do companies prepare for increasing
regulatory constraints?
Industry trends
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Companies are developing modeling capabilities and
pursuing various applications
Companies are developing proprietary credit scoring
models rather than buying “off-the-shelf” credit scores.
Companies are also going beyond credit, to build
scoring models that don’t rely solely on credit
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Keys to Building PMs
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Fully utilize all sources of information
 Leverage
company’s internal data sources
 Enriched with other external data sources
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Use large amount of data
Employ systematic analytical process
Use state-of-the-art modeling tools
Apply multivariate methodology
Disciplined project management
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Implications for Actuarial and
Ratemaking Practice
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Opportunities for out-of-the-box thinking (who thought of credit a
decade ago?)
Increased multivariate analytic projects in the future
On-going search for new predictive data sources, new modeling
techniques, and new applications
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Next generation of pricing – more segmentation
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LTV, fraud, cross sell, retention, ..etc.
A price for every risk
New methodologies
 Statistical computing
 Lift curve concept
 Blind test / model validation methodology
 ROI benefit calculation
 …etc
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Implications for Ratemaking
Principles
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Actuarial Ratemaking Principle #1: “A rate is an
estimate of the expected value of future costs”
Actuarial Ratemaking Principle #4: ” A rate is
reasonable and
Not excessive
 Not inadequate
 Not unfairly discriminatory
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But is that really the way profit-seeking companies
price their products?
Are rates ultimately based on costs or on what the
market will bear?
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Implications for Ratemaking
Principles
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How do you measure the ROI of a traditional
ratemaking/class plan exercise?
Why do ratemaking principles not mention
blind tests of pricing algorithms?
“Unfairly discriminatory”:
If we develop a powerful new segmentation model, is
it discriminatory to certain risks?
 If we don’t introduce it, is it discriminatory to other
risks?
 How do we know if we don’t do the analysis?
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