F f A Discussion on The Use of Credit Information and Scoring for Insurance Underwriting Eddy Lo...

A Discussion on The Use of Credit A Discussion on The Use of Credit Information and Scoring for Information and Scoring for Insurance UnderwritingInsurance UnderwritingEddy LoEddy LoMarch 12, 2001March 12, 2001CAS Ratemaking Seminar, Las VegasCAS Ratemaking Seminar, Las Vegas

2© 1999 Fair, Isaac and Co., Inc.© 1999 Fair, Isaac and Company, Inc.

TopicsTopics

1.1. Introduction and ObjectivesIntroduction and Objectives2.2. Fair, Isaac and Company, Inc.Fair, Isaac and Company, Inc.33.. Fair Credit Reporting ActFair Credit Reporting Act44 PredictivenessPredictiveness55.. FairnessFairness66.. AccuracyAccuracy77.. InquiriesInquiries88.. Current OperationsCurrent Operations

2

3© 1999 Fair, Isaac and Co., Inc.© 1999 Fair, Isaac and Company, Inc.

TopicsTopics

9.9. Statistical CorrelationStatistical Correlation10.10. Scoring DefinitionsScoring Definitions11.11. Scorecard ExamplesScorecard Examples12.12. ResultsResults1313.Usage of Insurance Bureau Scores.Usage of Insurance Bureau Scores1414.Summary.Summary1515.Questions & Answers.Questions & Answers

3

4© 1999 Fair, Isaac and Co., Inc.

Introduction and ObjectivesIntroduction and Objectives

Provide facts on the use of insurance bureau Provide facts on the use of insurance bureau scoresscores

Answer questions on insurance bureau Answer questions on insurance bureau scoresscores


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. Founded in 1956, byFounded in 1956, by

William R. FairWilliam R. Fair Earl J. IsaacEarl J. Isaac

Starting outStarting out Better credit decisions by statistics than Better credit decisions by statistics than

traditional judgmental methodstraditional judgmental methods NowNow

Better Decisions Through DataBetter Decisions Through Data


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. ValuesValues

Absolute integrityAbsolute integrity Very high standards of excellence of Very high standards of excellence of

product and serviceproduct and service Personal commitment via championingPersonal commitment via championing Collegial atmosphereCollegial atmosphere Strive for constant innovationStrive for constant innovation


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. Developed unique modeling processes Developed unique modeling processes

based on documented models and based on documented models and proprietary algorithmproprietary algorithm

National award examplesNational award examples Forbes Top 200 Small Companies list, Forbes Top 200 Small Companies list,

Honor Rolls in 1999, 1998, and other Honor Rolls in 1999, 1998, and other years years

Future Banker “1999 Top 25 Technology Future Banker “1999 Top 25 Technology Deals” for alliance with eCredit.com, and Deals” for alliance with eCredit.com, and other yearsother years


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. National award examples (cont’d)National award examples (cont’d)

Credit Risk Management Report award Credit Risk Management Report award for Best Scoring Model, 1998 & 1995for Best Scoring Model, 1998 & 1995

ABA Bank Card Distinguished Service ABA Bank Card Distinguished Service Award, September 1997Award, September 1997

Financial World Magazine “One of the Financial World Magazine “One of the 100 Best Growth Companies” 1997100 Best Growth Companies” 1997

President Corporate Award; Society of President Corporate Award; Society of Insurance Research 1995Insurance Research 1995


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. Industries servedIndustries served

Insurance, Finance Services, Insurance, Finance Services, Government, Healthcare, E-Business, Government, Healthcare, E-Business, TelecommunicationsTelecommunications

Global experienceGlobal experience Offices and representations on 6 Offices and representations on 6

continentscontinents


OFFICESOFFICESRepresentationRepresentation

Fair Isaac WorldwideFair Isaac Worldwide

TOKYO,TOKYO,JAPANJAPAN

Melbourne, Melbourne, AustraliaAustralia

Sydney, Sydney, AustraliaAustralia

JOHANNESBURG,JOHANNESBURG,SOUTH AFRICASOUTH AFRICA

Kuala Lumpur,Kuala Lumpur,MalaysiaMalaysia

Istanbul,Istanbul,TurkeyTurkey

BIRMINGHAM, UNITED KINGDOMBIRMINGHAM, UNITED KINGDOM

Helsinki, FinlandHelsinki, FinlandAntwerp, Antwerp, BelgiumBelgium

WIESBADEN,WIESBADEN,GERMANYGERMANY

PARIS, PARIS, FRANCEFRANCE

Madrid,Madrid,SpainSpain

TORONTO, CANADATORONTO, CANADAFair, IsaacFair, IsaacST. PAULST. PAUL

MINNESOTAMINNESOTA

NEW CASTLE,NEW CASTLE,DELAWAREDELAWARE

ATLANTA,ATLANTA,GEORGIAGEORGIA

* SAN RAFAEL,* SAN RAFAEL,CALIFORNIACALIFORNIA

HEADQUARTERSHEADQUARTERS

Santiago, Santiago, ChileChile

Phoenix,Phoenix,ArizonaArizona

MEXICO CITY,MEXICO CITY,MEXICOMEXICO


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. Participation in NAIC White PaperParticipation in NAIC White Paper

““Credit Reports and Insurance Credit Reports and Insurance Underwriting”Underwriting”

NAIC Subgroup visited Fair, Isaac in NAIC Subgroup visited Fair, Isaac in August 1995August 1995

Participated in an industry working groupParticipated in an industry working group Issued acceptable principles in Issued acceptable principles in

October 1995October 1995 NAIC Subgroup Chairman joined Fair, NAIC Subgroup Chairman joined Fair,

Isaac InterACT ‘96 educational Isaac InterACT ‘96 educational conferenceconference


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. Tillinghast study ofTillinghast study of

Insurance Bureau Scores Vs. Loss Ratio Insurance Bureau Scores Vs. Loss Ratio RelativitiesRelativities

Presented to NAIC Subgroup in Presented to NAIC Subgroup in December 1996December 1996

Included in White Paper AppendixIncluded in White Paper Appendix White Paper adopted by NAIC in December White Paper adopted by NAIC in December

19961996


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. PresentationsPresentations

American Agents AllianceAmerican Agents Alliance American Insurance AssociationAmerican Insurance Association Alliance of American InsurersAlliance of American Insurers Association of Insurance and Financial Association of Insurance and Financial

AnalystsAnalysts Casualty Actuarial SocietyCasualty Actuarial Society Chartered Property and Casualty Chartered Property and Casualty

UnderwritersUnderwriters National Association of Independent National Association of Independent

InsurersInsurers


Fair, Isaac & Company, Inc.Fair, Isaac & Company, Inc. Presentations (cont’d)Presentations (cont’d)

National Association of Insurance National Association of Insurance CommissionersCommissioners

Neighborhood Reinvestment CorporationNeighborhood Reinvestment Corporation Professional Insurance AgentsProfessional Insurance Agents Independent Insurance Agent AssociationIndependent Insurance Agent Association Insurance departments and legislatorsInsurance departments and legislators Reinsurance Association of AmericaReinsurance Association of America OthersOthers


Fair Credit Reporting Act (FCRA)Fair Credit Reporting Act (FCRA) Original statute in 1970Original statute in 1970

Major amendments in 1996; effective Major amendments in 1996; effective September 30, 1997September 30, 1997

Requires “consumer reporting agencies” to Requires “consumer reporting agencies” to adopt procedures governing accuracy, adopt procedures governing accuracy, relevancy, access to and utilization of relevancy, access to and utilization of “consumer reports”“consumer reports”

Allows consumers access to their files and a Allows consumers access to their files and a complaint procedurecomplaint procedure


Fair Credit Reporting Act (cont’d)Fair Credit Reporting Act (cont’d) Requires users of consumer reports to certify Requires users of consumer reports to certify

the permissible purpose(s) and use only for the permissible purpose(s) and use only for certified (permissible) purpose(s); and to give certified (permissible) purpose(s); and to give FCRA adverse action noticesFCRA adverse action notices

Imposes accuracy-oriented obligations on Imposes accuracy-oriented obligations on furnishers of informationfurnishers of information


Fair Credit Reporting Act (cont’)Fair Credit Reporting Act (cont’) Permissible purposesPermissible purposes

Court order or written instructions of Court order or written instructions of consumerconsumer

Use in connection with a credit Use in connection with a credit transaction involving the consumer; credit transaction involving the consumer; credit extensions/review of accounts/collectionsextensions/review of accounts/collections

Use for underwriting insuranceUse for underwriting insurance EmploymentEmployment


Fair Credit Reporting ActFair Credit Reporting Act Permissible purposes (cont’d)Permissible purposes (cont’d)

Use by person with other legitimate Use by person with other legitimate business need for information in business need for information in connection with a business transaction connection with a business transaction initiated by the consumer, or to review an initiated by the consumer, or to review an account to determine whether the account to determine whether the consumer continues to meet the terms of consumer continues to meet the terms of the accountthe account


Fair Credit Reporting ActFair Credit Reporting Act Permissible purposes (cont’d)Permissible purposes (cont’d)

Prescreening: Use for “transaction not Prescreening: Use for “transaction not initiated by consumer” for “firm offer of initiated by consumer” for “firm offer of credit or insurance”, permit conditioning credit or insurance”, permit conditioning the offer on verification of information in the offer on verification of information in credit report or application to ensure that credit report or application to ensure that consumer still meets the prescreen consumer still meets the prescreen criteria at time of acceptance; may also criteria at time of acceptance; may also condition offer on information in condition offer on information in application meeting pre-established application meeting pre-established criteria, or on the furnishing of required criteria, or on the furnishing of required collateral as disclosed in the offercollateral as disclosed in the offer


Homeowner

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

low high

Score Range

Lo

ss R

atio

Rel

ativ

ity

PredictivenessPredictivenessRank OrderingRank Ordering


Personal Auto

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

low high

Score Range

Lo

ss R

atio

Re

lati

vit

y

PredictivenessPredictivenessRank OrderingRank Ordering


PredictivenessPredictiveness Low scores correlate with high loss ratio Low scores correlate with high loss ratio

relativitiesrelativities

High scores correlate with low loss ratio High scores correlate with low loss ratio relativitiesrelativities

Validated byValidated by InsurersInsurers TillinghastTillinghast


FairnessFairness Data Elements UsedData Elements Used

BalancesBalances CollectionsCollections DelinquenciesDelinquencies InquiriesInquiries LimitsLimits Payment DatesPayment Dates Payment Due DatesPayment Due Dates Public RecordsPublic Records Trade Line Open and Close DatesTrade Line Open and Close Dates Trade Line TypesTrade Line Types

33 44 55 66

1010 1111 1212 1313

1717 1818 1919 2020

2424 2525 2626 2727

SEPTEMBERSEPTEMBER


Fairness (cont’d)Fairness (cont’d) Data Elements Data Elements NoNott Used Used

AgeAge DisabilityDisability GenderGender Health StatusHealth Status IncomeIncome LocationLocation Marital StatusMarital Status33 44 55 66

1010 1111 1212 1313

1717 1818 1919 2020

2424 2525 2626 2727

SEPTEMBERSEPTEMBER


FairnessFairness Data Elements Data Elements NotNot Used (cont’d) Used (cont’d)

NationalityNationality Net WorthNet Worth OccupationOccupation RaceRace ReligionReligion Sexual OrientationSexual Orientation Zip CodesZip Codes33 44 55 66

1010 1111 1212 1313

1717 1818 1919 2020

2424 2525 2626 2727

SEPTEMBERSEPTEMBER


Fairness (cont’d)Fairness (cont’d) Income Study by an InsurerIncome Study by an Insurer

Flat relationship between income levels and Flat relationship between income levels and scoresscores

Virginia Bureau of InsuranceVirginia Bureau of Insurance ‘‘Use of Credit Reports in Underwriting’, 1999 Use of Credit Reports in Underwriting’, 1999

reportreport To the Senate Commerce and Labor To the Senate Commerce and Labor

Committee of the the General Assembly of Committee of the the General Assembly of VirginiaVirginia

“… “… Nothing in this analysis leads the Bureau Nothing in this analysis leads the Bureau to the conclusion that income or race alone to the conclusion that income or race alone is a reliable predictor of credit scores thus is a reliable predictor of credit scores thus making the use of credit scoring an making the use of credit scoring an ineffectiveineffective tool for tool for redliningredlining. …”. …”

33 44 55 66

1010 1111 1212 1313

1717 1818 1919 2020

2424 2525 2626 2727

SEPTEMBERSEPTEMBER


AccuracyAccuracy

FCRA mandate correction processFCRA mandate correction process

1992 Arthur Anderson Study1992 Arthur Anderson Study Commissioned by Associated Credit Commissioned by Associated Credit

BureausBureaus Based on 15,202 declinesBased on 15,202 declines 2% dispute on declines2% dispute on declines

MVR accepted by most regulatorsMVR accepted by most regulators higher error rateshigher error rates


InquiriesInquiries

Homeowner

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 1 2 3 4 5+

Inquiries

Lo

ss R

atio

Rel

ativ

ity


InquiriesInquiries

Personal Auto

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 1 2 3 4 5+

Inquiries

Lo

ss R

ati

o R

ela

tivit

y


InquiriesInquiries Predictive of loss ratio relativitiesPredictive of loss ratio relativities

Fair, Isaac includes consumer-initiated Fair, Isaac includes consumer-initiated inquiriesinquiries

Fair, Isaac excludes inquiries forFair, Isaac excludes inquiries for Marketing / prescreeningMarketing / prescreening Account reviewsAccount reviews InsuranceInsurance


Current OperationsCurrent Operations

Fair, IsaacFair, IsaacScorecardsScorecards

Credit Credit DatabaseDatabase

ScorecardsScorecardsUnderwritingUnderwriting

Subscriber RequestsSubscriber Requests

Scores and ServicesScores and Services

RoyaltyRoyalty

MaintenanceMaintenance

InsurerInsurer Credit BureauCredit Bureau


Statistical CorrelationStatistical Correlation Personal propertyPersonal property

230,000 policies with claims230,000 policies with claims 1,000,000 policies without claims1,000,000 policies without claims 11 archives11 archives


Statistical Correlation (cont’d)Statistical Correlation (cont’d) Homeowner univariate analysesHomeowner univariate analyses

Number of adverse public recordsNumber of adverse public records Months since most recent adverse public recordMonths since most recent adverse public record Number of trade lines 60+ days delinquent in last 24 monthsNumber of trade lines 60+ days delinquent in last 24 months Number of collectionsNumber of collections Number of trade lines opened in the last 12 monthsNumber of trade lines opened in the last 12 months


Statistical CorrelationStatistical CorrelationHomeowner HO - 3Homeowner HO - 3

Number of Adverse Public Records

1000

1540

0

200

400

600

800

1000

1200

1400

1600

zero one or more

96%

Loss

Rat

io R

elat

ivity


Months Since Most Recent Adverse Public Record

1000

1678

1226

0

200

400

600

800

1000

1200

1400

1600

1800

no public record less than 48 48 or more

96%

Lo

ss R

atio

Rel

ativ

ityStatistical CorrelationStatistical CorrelationHomeowner HO - 3Homeowner HO - 3


Number of Trade Lines 60+ Days Delinquentin Last 24 Months

1000

1293

1804

0

200

400

600

800

1000

1200

1400

1600

1800

2000

zero one two or more

89%

Lo

ss

Ra

tio

Re

lati

vit

yStatistical CorrelationStatistical CorrelationHomeowner HO - 3Homeowner HO - 3


Number of Collections

1000

1686

0

200

400

600

800

1000

1200

1400

1600

1800

zero one or more

97%

Lo

ss R

atio

Rel

ativ

ity



Number of Trade Lines Opened in the Last 12 Months

1000

11471220

1503

1658

0

200

400

600

800

1000

1200

1400

1600

1800

zero one two three four or more

60%

Lo

ss R

ati

o R

ela

tivi

ty



Statistical Correlation (cont’d)Statistical Correlation (cont’d) Personal autoPersonal auto

350,000 policies with claims350,000 policies with claims 1,000,000 policies without claims1,000,000 policies without claims 6 archives6 archives


Statistical Correlation (cont’d)Statistical Correlation (cont’d) Personal auto univariate analysesPersonal auto univariate analyses

Number of adverse public recordsNumber of adverse public records Months since most recent adverse public recordMonths since most recent adverse public record Number of trade lines 60+ days delinquent in last 24 monthsNumber of trade lines 60+ days delinquent in last 24 months Number of collectionsNumber of collections Number of trade lines opened in the last 12 monthsNumber of trade lines opened in the last 12 months


Number of Adverse Public Records

1000

1225

0

200

400

600

800

1000

1200

1400

zero one or more

97%

Lo

ss R

ati

o R

ela

tivi

ty

Statistical CorrelationStatistical CorrelationPersonal AutomobilePersonal Automobile


Months Since Most Recent Adverse Public Record

1000

1339

1182

0

200

400

600

800

1000

1200

1400

1600

no public record less than 18 18 or more97%

Lo

ss R

ati

o R

ela

tivi

tyStatistical CorrelationStatistical CorrelationPersonal AutomobilePersonal Automobile


Number of Trade Lines 60+ Days Delinquentin Last 24 Months

1000

1238

1444

0

200

400

600

800

1000

1200

1400

1600

zero one two or more

86%

Lo

ss R

ati

o R

ela

tivit

yStatistical CorrelationStatistical CorrelationPersonal AutomobilePersonal Automobile


Number of Collections

1000

1494

0

200

400

600

800

1000

1200

1400

1600

zero one or more

96%

Lo

ss R

ati

o R

ela

tivit

yStatistical CorrelationStatistical CorrelationPersonal AutomobilePersonal Automobile


Number of Trade Lines Opened in the Last 12 Months

10001083

1270

0

200

400

600

800

1000

1200

1400

zero or one two or three four or more

82%

Lo

ss R

ati

o R

ela

tivit

y

Statistical CorrelationStatistical CorrelationPersonal AutomobilePersonal Automobile


A score for an insurance riskA score for an insurance risk Is a numeric summaryIs a numeric summary Of the impact on loss ratio relativityOf the impact on loss ratio relativity Based on a certain set of predictive Based on a certain set of predictive

characteristics of the riskcharacteristics of the risk A model/scorecard is an algorithm, a table, or A model/scorecard is an algorithm, a table, or

a piece of computer softwarea piece of computer software That will calculate a scoreThat will calculate a score Based on a certain set of characteristicsBased on a certain set of characteristics Provided for a riskProvided for a risk

Scoring DefinitionsScoring Definitions


The 4 reason codes for a score are the 4 The 4 reason codes for a score are the 4 reasons that contributed most significantly, reasons that contributed most significantly, positively or negatively, to the calculation of a positively or negatively, to the calculation of a scorescore

Scoring Definitions (cont’d)Scoring Definitions (cont’d)


Scorecard ExamplesScorecard Examples

Simple homeowner scorecardSimple homeowner scorecard

Overlapping characteristicsOverlapping characteristics weights adjustedweights adjusted


Scorecard ExamplesScorecard ExamplesSimple Homeowner ScorecardSimple Homeowner Scorecard

Number Adverse Public RecordsNumber Adverse Public Recordszerozero one or moreone or more 3030 0 0

Months Since Most Recent Adverse PublicMonths Since Most Recent Adverse PublicRecordRecord

no public recordno public record less than 48less than 48 48 or more48 or more

3030 0 0 10 10


Number of Trade Lines 60+ Days Delinquent inNumber of Trade Lines 60+ Days Delinquent inLast 24 MonthsLast 24 Months

zerozero oneone two or moretwo or more

2525 10 10 0 0

Number of CollectionsNumber of Collectionszerozero one or moreone or more 2020 0 0



Number of Trade Lines Opened on the Last 12Number of Trade Lines Opened on the Last 12MonthsMonths

zerozero oneone two two three three four or more four or more

2020 10 10 5 5 3 3 00



ResultsResults Insurance Bureau Scores Vs. Loss Ratio Insurance Bureau Scores Vs. Loss Ratio

RelativitiesRelativities Multivariate analysis - homeowner (HO1, Multivariate analysis - homeowner (HO1,

HO2, HO3, HO4, HO6, dwelling fire)HO2, HO3, HO4, HO6, dwelling fire) Multivariate analysis - personal auto (non-Multivariate analysis - personal auto (non-

standard, standard minimum limits, standard, standard minimum limits, standard above minimum limits, preferred standard above minimum limits, preferred minimum limits, preferred above minimum minimum limits, preferred above minimum limits)limits)

Risk rankingRisk ranking


ResultsResults Insurance Bureau Score Vs. Loss Ratio Insurance Bureau Score Vs. Loss Ratio

Relativities (cont’d)Relativities (cont’d) Development vs. validation datasetsDevelopment vs. validation datasets Validation (handouts)Validation (handouts) Tillinghast study; Conclusion, page 5; Tillinghast study; Conclusion, page 5; “…“…The data for all companies included in this The data for all companies included in this study except Company 2 indicates at least 99% study except Company 2 indicates at least 99% probability that a relationship exists. The data for probability that a relationship exists. The data for Company 2 indicates a 92% probability that there Company 2 indicates a 92% probability that there is a relationship. A layman’s interpretation of this is a relationship. A layman’s interpretation of this result could be that it is very likely there is a result could be that it is very likely there is a correlation between Insurance Bureau Scores and correlation between Insurance Bureau Scores and loss ratio relativities.”loss ratio relativities.”


Usage of Insurance Bureau ScoresUsage of Insurance Bureau Scores FacilitateFacilitate

underwriting applicationsunderwriting applications underwriting investigationunderwriting investigation tier placementtier placement


0.4

0.9

1.4

650-674 675-699 700-724 725-749 750-774

Score Range

Loss

Rat

io R

elat

iviti

es

640-660 referral

Illustration of Underwriting Acceptance


0.40.60.81.01.21.41.61.8

<650 650-674 675-699 700-724 725-749 750-774 775-799 800+

Score Range

Loss

Rati

o Re

lativi

ties Tier 1 Tier 2 Tier 3

665-685

r ef er r al

740-760

r ef er r al

Illustration of Underwriting Tier Placement


SummarySummary FCRA Makes Insurance Bureau Scores FCRA Makes Insurance Bureau Scores

Usable for Insurance Underwriting and Usable for Insurance Underwriting and MarketingMarketing

Poll says Insurance Bureau Scores are Poll says Insurance Bureau Scores are favoredfavored

Tillinghast Study confirms loss ratio Tillinghast Study confirms loss ratio relativities and Insurance Bureau Scores relativities and Insurance Bureau Scores relationshiprelationship

Credit reports more accurate than Motor Credit reports more accurate than Motor Vehicle ReportsVehicle Reports Credit report accuracy further enhanced Credit report accuracy further enhanced

by correctionsby corrections


SummarySummary Insurance Bureau Scores summarize credit Insurance Bureau Scores summarize credit

history succinctly and nothing elsehistory succinctly and nothing else The relationship between how people The relationship between how people

maintain their credit and property is simply maintain their credit and property is simply common sensecommon sense Good credit managers are good risk Good credit managers are good risk

managersmanagers Credit management reflected in Insurance Credit management reflected in Insurance

Bureau ScoresBureau Scores


SummarySummary Insurance Bureau Scores deliver a fair shakeInsurance Bureau Scores deliver a fair shake

Insurance Bureau Scores do not look at Insurance Bureau Scores do not look at race, creed, gender, marital status, race, creed, gender, marital status, income, age, etc.income, age, etc.

Insurance Bureau Scores do not worsen Insurance Bureau Scores do not worsen discrimination nor add to itdiscrimination nor add to it

Scoring remedies discriminationScoring remedies discrimination Insurance Bureau Scores can control Insurance Bureau Scores can control

discriminationdiscrimination Insurance Bureau Scores do not unfairly Insurance Bureau Scores do not unfairly

discriminatediscriminate


SummarySummary Insurance Bureau Scores help to open up Insurance Bureau Scores help to open up

marketsmarkets Scoring leads to precision underwritingScoring leads to precision underwriting

Insurance Bureau Scores facilitate Insurance Bureau Scores facilitate consistent underwritingconsistent underwriting

Insurance Bureau Scores don’t make Insurance Bureau Scores don’t make decisions, people dodecisions, people do

Insurance Bureau Scores provide input to Insurance Bureau Scores provide input to refine decisionsrefine decisions

Insurance Bureau Scores provide more Insurance Bureau Scores provide more objectivity and accuracyobjectivity and accuracy


SummarySummary Insurance Bureau Scores help underwriters Insurance Bureau Scores help underwriters

focus on risks needing attention mostfocus on risks needing attention most Insurance Bureau Scores help to reduce Insurance Bureau Scores help to reduce

premium subsidies/inequitypremium subsidies/inequity Insurance Bureau Scores strengthen insurer Insurance Bureau Scores strengthen insurer

solvencysolvency Fair, Isaac expertise to shareFair, Isaac expertise to share


MaterialsMaterials

1.1. Reasons and Codes Reasons and Codes2.2. Answers to Your Questions about Answers to Your Questions about

Insurance Bureau ScoresInsurance Bureau Scores

QuestionsQuestions AnswersAnswers

&&

F f A Discussion on The Use of Credit Information and Scoring for Insurance Underwriting Eddy Lo...

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