© Deloitte Consulting, 2005
Cheng-Sheng Peter Wu, FCAS, ASA, MAAA
CAS 2005 Spring Meeting
Scottsdale, AZ
May 16-19, 2005

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Agenda
What is Predictive Modeling
A Case Study of Successful Predictive
Modeling - Credit Scoring Revolution
From Credit Scoring to Predictive Modeling
What Does Predictive Modeling Mean for
Actuaries?
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What is Predictive Modeling
What is predictive modeling?
– Predictive modeling is an application of mathematical and statistical techniques and algorithms to produce a mathematical model that can effectively predict and segment future events
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Why is PM a Hot Topic?
Is it just a new actuarial fashion?
Is it just the “flavor of the month” that we all like to talk about at conferences but nobody really does?
No to both!
– It is a new addition to the actuary’s toolkit.
– It is here to stay.
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Why is PM a Hot Topic?
PM is a natural extension of what actuaries have done all along.
– Use data to make predictions and forecasts.
It allows us to add statistical rigor and additional info to traditional areas of actuarial practice.
– Large scale data mining and multivariate analysis
– GLM-based ratemaking
– Stochastic Loss reserving
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Why is PM a Hot Topic?
PM also allows us to broaden actuarial practice.
– Underwriting models
– Credit scoring
– Retention and cross-sell modeling
– Target marketing models
– Agency monitoring tools
– Other industries
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What is New About “Today’s”
Predictive Modeling?
Rapid advancement of cheap computing power
Moore’s Law
Year 1980 1985 1990 1995 2000 2004
Storage Cost per
Megabyte $190 $ 70 $ 10 $0.90 $0.05 $0.001
Microprocessor Speed,
MHz 5-8 16 33 75 200 400
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What is New About “Today’s”
Predictive Modeling?
Availability of wide range of data from internal and external sources.
“Data Mining”:
“ Data mining is a process that utilizes predictive modeling techniques to analyze large quantities of internal and external data, in order to unlock previously unknown and meaningful business relationships”
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What is New About “Today’s”
Predictive Modeling?
Development of new and powerful modeling and data exploration techniques
– Examples: regression, GLM, neural networks, decision trees, clustering analysis, MARS, ...
– Explore complicated patterns in data such as nonnormality, non-linearity, interactions, etc.
Statistical analysis is no longer restricted to what you can do with pencil and paper...
…or spreadsheets.
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What is New About “Today’s”
Predictive Modeling?
Multivariate analysis with large amount of data and many variables
– Analyze multiple variables “simultaneously” instead of one or two at a time.
– Use large amounts of data
No need to use summarized data for actuarial analyses.
– Create and analyze novel predictive variables.
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12
10
8
6
4
2
0
1994 1995 1996
Which Company is This?
1997 1998
Year
1999 2000 2001 2002 2003
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Progressive vs Industry
0.35
0.3
0.25
0.2
0.15
0.1
0.05
90%
85%
0
1994 1995 1996 1997 1998 1999
Year
2000 2001 2002 2003
80%
115%
110%
105%
100%
95%
Industry Growth Rate
Progressive Growth Rate
Industry Combined Ratio
Progressive Combined Ratio
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Why?
Progressive provided foosball tables and free snacks to their trendy, 20-something workforce
Progressive built a compound Gamma-
Poisson GLM model to design their class plan
Progressive pioneered the use of credit in pricing/underwriting
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Credit Score Revolution
Personal line rating history:
– Few rating factors before World War II
– Explosion of class plan factors after the War
– Auto class plans:
Territory, driver, vehicle, coverage, loss and violation, others, tiers/company…
– Homeowners class plans:
Territory, construction class, protection class, coverage, prior loss, others, tiers/company...
– Credit scoring introduced in late 80s and early 90s
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Credit Score Revolution
About credit score:
– 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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Credit Score Revolution
Current environment of credit score:
– Now everyone is using it:
Marketing and direct solicitation
New business and renewal business pricing and underwriting
– Regulatory constraints:
Many states have conducted studies on the true correlation with loss ratio and potential discrimination issues - WA study, TX study, MO study
Many states have/are considering restricting the use of credit scores or certain type of information, MI.
More states want the “black box” filed and opened
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Credit Score Revolutions
What is “credit score”?
– A composite score that usually contains 10 to
40 pieces of credit information
Payment pattern information, account history, bankruptcies/liens, collections, inquiries, bad debt/defaults…
Formula scoring or rule-based scoring
Industry scores and proprietary scores
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Credit Score Revolutions
Why “credit score” is so successful?
– “Large scale” “ multivariate ” scoring using
“ external data source”
– Loss ratio lift is significant, a powerful class plan factor or rate tiering factor
– “Brilliant” marketing approach for credit score:
Benefits/ROI are measurable and 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
Loss Ratio
50
58
62
66
70
74
78
82
90
120
Credit Score Decile
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Credit Score Revolution
1997 NAIC/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 Scores to
Predictive Modeling
A credit score is just “ one example ” of an insurance predictive model
The same methods used to build credit scores are used in data mining to build insurance predictive models:
– Fully utilize all sources of internal and external data sources
– Fully utilize all available data
Not just credit
– Other lines of business?
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What Does PM Mean for
Actuaries?
– New data sources
– New technologies
– New analytical tools
– True Multivariate analysis
No longer one or two variables at a time
– Analysis of risk-, policy-, or HH-level data, rather than aggregated data.
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What Does PM Mean for
Actuaries?
New emphasis on the “business” side of the analytical work and out-of-box thinking
Who thought of credit a decade ago?
How to stay competitive if everyone is using credit and
GLM?
What is the “next” big thing out there?
Are you using the same “lift curve” and ROI concept in your analytical work?
How do you tie in your model/analytical work to business benefit?
Can you demonstrate the business benefits of your analytical work through a blind test?
…etc
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What Does PM Mean for
Actuaries?
New challenges to “actuarial” methodologies and principles
– Actuarial Ratemaking Principle #1: “A rate is an estimate of the expected value of future costs”
– Actuarial Ratemaking Principle #4: ” A rate is reasonable, not excessive, not inadequate, and not unfairly discriminatory
– 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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What Does PM Mean for
Actuaries?
New challenges to actuarial principles and methodology:
– “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?
Actuaries’ “Static/Equalibrium” Principles vs.
Business’ “Ever Changing/Dynamic” Principles
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Placebo or Panacea?
So which is it?
Not a placebo
– PM is here to stay
– A permanent addition to the actuary’s toolkit
– Has the power to both deepen and expand actuarial practice.
Not a panacea
– PM complements, doesn’t replace fundamental actuarial principles
– PM does nothing without sound business strategy and implementation.
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