© deloitte consulting, 2004 alternatives to credit scoring in insurance james guszcza, fcas, maaa...

© Deloitte Consulting, 2004

Alternatives to Credit Scoring in Insurance

James Guszcza, FCAS, MAAACheng-Sheng Peter Wu, FCAS, ASA, MAAA

CAS 2004 Ratemaking SeminarPhiladelphia

March 12-13, 2004

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Agenda

Introduction The credit scoring revolution From credit scoring to predictive modeling

The big idea: credit scoring is just one kind of insurance predictive (“data mining”) model… many other predictive models can be built

Conclusions What it means to actuaries


Introduction

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Introduction – Our Efforts

Is credit for real? “Does Credit Score Really Explain Insurance Losses?

Multivariate Analysis from a Data Mining Point of View”

Going beyond credit “Mining the Most from Credit and Non-Credit Data”

How do credit scoring models work? “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?

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Introduction

Progressive Insurance Company

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Progressive CombinedRatioProgerssive GrowthRate

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IntroductionProgressive vs. Industry

80%

85%

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110%

115%

1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

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Progressive Combined Ratio

Industry Combined Ratio

Progerssive Growth Rate

Industry Growth Rate

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Why?

Multiple Choice 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


The Credit Score Revolution

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Personal Lines Pricing and Class Plans – 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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Personal Lines Credit Scoring – History 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 Now everyone is using it:

Marketing and direct solicitation New business and renewal business pricing and underwriting How to stay competitive if everyone is using it?

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 types of credit information

More states want the “black box” filed and opened

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Some Facts About Credit Scores A composite score that usually contains 10 to

40 pieces of credit information Payment pattern information, bankruptcies/liens,

collections, inquiries, bad debt/defaults…

Loss ratio lift is significant – a powerful class plan factor or rate tiering factor

Benefits/ROI are measurable 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

82

66

58

62

7074

78

90

120

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Credit Score Decile

Loss Ratio

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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%


From Credit Scoring to Predictive Modeling

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From Credit Scores to Predictive Models What is a predictive model?

A multivariate scoring formula (linear or non-linear) that combines the values of several predictive variables to estimate the value of a target variable

What is a credit score? A multivariate scoring formula (linear or non-

linear) 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 A credit score is just one example of an

insurance predictive modelA 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 Credit scores are PMs that use only credit-

related variables to predict relative profitability.

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

Why would we want to build a purely non-credit PM?

Competitive advantages – e.g. matrix with creditState-specific regulatory constraintsExpense of ordering credit reportsThin files/no-hitsPublic relations

But from a purely actuarial POV, credit is predictive should be used as part of the PM!

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PMs: Considerations

The key is to use as much information as possible

in a multivariate way Choice of statistical techniques is important,

but the real key is the quality and breadth of predictive variables used.

GIGOActuarial/insurance knowledge is critical

Untapped riches reside in many companies’ transactional records.

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PMs: Data Sources

We classify possible data sources into two groups

Internal data sources: predictive information gleaned from the company’s own systems

Regardless of how or whether it is currently used

External data sources: predictive information available from 3rd parties.

Both credit and non-credit

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Internal Data Sources

Policy informationLimits, Deductibles, Measure of exposure (# cars,

#houses, #employees, $sales, premium size…

Line-Specific informationDriver, Vehicle, Business Class …

Policyholder informationAge, gender, marital status …

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Internal Data Sources

Customer-level information Transactional data

Coverage, premium and loss transactions

Billing informationCorrelation with credit

Agent information

A little creativity in using these data sources will go a long way!

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External Data Sources

CreditPredictive both for commercial and personal lines

MVR – CLUE Zipcode/geographic information

Rating territoryMany different sources available

The sky is the limit butConsider cost, hit rate, implementation, …etc

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Types of Variables Generated

Territory-levelDemographic, weather, crime, ...etc

Policy / policyholder-specificMany traditional rating variables fall into this category

BehavioralLess traditional – fits more neatly into data mining

paradigm than classification ratemakingCredit, billing, prior claims, cancel-reinstatements…

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How Many Variables?

It is possible to generate literally hundreds of predictive variables

Some will be redundantSome will not be very predictiveSome will be somewhat predictiveSome will be “killer”

A good model can contain as few as 15-20 or as many as 60-70 variables

Usually no single “ideal” model

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Which Variables to Use?

Choosing is a major part of the data mining process

Use variety of exploratory statistical techniquesUse prior modeling experience / actuarial knowledge

Several considerationsActuarial / underwriting knowledgeClient’s business needsLegal / regulatory considerationsData availability / costSystems implementation considerations

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In Our Experience….

Do non-credit PMs work? YES: non-credit predictive models are

Valuable alternative to credit scoresFlexibleTailored to individual companiesLeverage company’s untapped internal dataComparable predictive power to credit scores

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: IT resources constraints Project management Business process buy-in Success of system and business

implementation Training and organizational change


Conclusions

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Industry Trends How do companies try to stay competitive

regarding the use of credit? How do companies prepare for increasing

regulatory constraints? Industry trends

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

Fully utilize all sources of information Leverage company’s internal data sourcesEnriched with other external data sources

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 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 LTV, fraud, cross sell, retention, ..etc.

Next generation of pricing – more segmentation 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 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 excessiveNot inadequateNot 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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Implications for Ratemaking Principles 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?

© deloitte consulting, 2004 alternatives to credit scoring in insurance james guszcza, fcas, maaa...

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