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CAS Predictive Modeling CAS Predictive Modeling SeminarSeminar
Predictive Modeling for a Commercial Insurance Company with Little or No
Data
Scott Bronstein October 5, 2004
©Experian Information Solutions, Inc. 2001 Confidential and proprietary - not for dissemination
Discussion pointsDiscussion points
Data available for predictive modelingData available for predictive modeling Models in useModels in use Sample resultsSample results SummarySummary
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Modeling guidelines with minimal dataModeling guidelines with minimal data Know your target populationKnow your target population Tap into business data - even minimal amount Tap into business data - even minimal amount
of business performance data can be predictiveof business performance data can be predictive Use consumer data as appropriateUse consumer data as appropriate Segment the population Segment the population Validate at logical intervals - can recalibrate if Validate at logical intervals - can recalibrate if
necessarynecessary
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Publicly held<1%
Privately held 7%Privately held 7%
PartnershipsPartnerships15%15%
Sole proprietorshipsSole proprietorships77%77%
190 million consumers
Source: Statistical Abstract of the United Source: Statistical Abstract of the United StatesStates
Twenty-two million businesses
Composition of U.S. businessesComposition of U.S. businesses
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Experian Business Information Experian Business Information SolutionsSolutions
Integrated Information SolutionsIntegrated Information Solutions
National Business National Business DatabaseDatabase
NationalBusiness Credit
Database
Business Public Business Public Record DatabaseRecord Database
National Consumer National Consumer Credit DatabaseCredit Database
No other company houses these assets under a single No other company houses these assets under a single roofroof
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FirmographicsExperianExperianBusiness Credit Business Credit
DatabaseDatabase
Standard & PoorsStandard & Poors
Public Public recordrecord
Banking, Banking, insurance,insurance,
leasingleasing
Trade Trade paymentpayment
Marketing databaseMarketing database
CollectionsCollections
Credit Database SourcesCredit Database Sources
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National Yellow Pages
NationalNationalBusiness DatabaseBusiness Database
Credit Database
Data VendorsData Vendors
Televerified Televerified DataData
Marketing DatabaseMarketing Database
DBA/FBNDBA/FBN(new (new
business)business)
Business White Business White PagesPages
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Scores Offer Solutions Scores Offer Solutions Across Customer LifecycleAcross Customer Lifecycle
Score portfolio to: Score portfolio to: Help process renewalHelp process renewal Identify cross-sell and Identify cross-sell and
up-sell opportunitiesup-sell opportunities Adjust credit limits Adjust credit limits
MaximizingMaximizing Score portfolio to:Score portfolio to: Monitor application Monitor application
policiespolicies Track ongoing Track ongoing
customer riskcustomer risk Prioritize / expedite Prioritize / expedite
collectionscollections Determine gross Determine gross
measures of portfolio measures of portfolio risk risk
ManagingManaging
Score portfolio for Score portfolio for targeted pre-targeted pre-qualified & cross-sell qualified & cross-sell effortsefforts
Select pre-qualified Select pre-qualified names from BIS names from BIS marketing listmarketing list
TargetingTargeting Process new applications Process new applications
using internet, online using internet, online software or CPU access.software or CPU access.
Bulk portfolio purchases Bulk portfolio purchases
AcquiringAcquiring
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Intelliscore overviewIntelliscore overview Used primarily in small business lending, commercial Used primarily in small business lending, commercial
card, leasing, telecommunications, and business card, leasing, telecommunications, and business servicesservices
Commercial IntelliscoreCommercial Intelliscore Utilizes Utilizes commercial credit, business demographics, credit, business demographics,
public record and legal informationpublic record and legal information Small Business IntelliscoreSmall Business Intelliscore
Utilizes Utilizes commercial and consumer credit, business credit, business demographics, public record and demographics, public record and legal informationlegal information
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Intelliscore models are Intelliscore models are segmented scoring systemssegmented scoring systems
Businesses within a modeling Businesses within a modeling sample are clustered or sample are clustered or segmented by common segmented by common characteristics such ascharacteristics such as
Size of businessSize of business Credit historyCredit history Data type availabilityData type availability
When predictors behave When predictors behave differently between clusters, the differently between clusters, the file should be segmented so the file should be segmented so the clusters can be modeled clusters can be modeled independently to capture subtle independently to capture subtle nuances in payment behaviornuances in payment behavior
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Model Model Segment 1Segment 1
scorescore
Model Model Segment 2Segment 2
scorescoreModel Model
Segment 3Segment 3scorescore
TransformationTransformation
Uniform 0 - 100 scoreUniform 0 - 100 score
Model segment integration Model segment integration Scoring system integrates model segments into one Scoring system integrates model segments into one uniform scoreuniform score
Each Intelliscore model solution consists of several Each Intelliscore model solution consists of several modeled segments each with its own set of variables modeled segments each with its own set of variables and raw scoreand raw score
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Commercial credit and business demographic information for up to fifteen elements
Credit score and percentile
Action
Score factors
Commercial Intelliscore ReportCommercial Intelliscore ReportKey featuresKey features
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Commercial IntelliscoreCommercial Intelliscore18% to 60% lift in predictiveness18% to 60% lift in predictiveness
60%
21%
34%
23%
19% 18% 19%
27%24%
0
5
10
15
20
25
30
35
40
45
50
KS
0%
10%
20%
30%
40%
50%
60%
70%
% L
ift
New CI CI %Lift
New CI 25.03 27.73 21.42 20.37 19.28 22.12 22.45 24.27 20.19 45.20
CI 20.64 20.72 17.48 12.77 16.24 18.69 18.84 19.14 16.31
%Lift 21% 34% 23% 60% 19% 18% 19% 27% 24%
Client A Client B Client C Client D Client E Client F Client G Client H Client I Development
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Small Business IntelliscoreSmall Business Intelliscore12% to 35% lift in predictiveness12% to 35% lift in predictiveness
12%
19%
31%
35%
0
5
10
15
20
25
30
35
40
45
50
KS
0%
5%
10%
15%
20%
25%
30%
35%
40%
% L
ift
New SBI SBI % Lift
New SBI 32.20 34.85 26.31 35.45 47.28
SBI 28.68 29.38 20.14 26.27
% Lift 12% 19% 31% 35%
Client A Client B Client C Client D Development
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SummarySummary Wide array of business data is availableWide array of business data is available Familiarity with how to use the data is Familiarity with how to use the data is
criticalcritical Small business is broadly defined and data Small business is broadly defined and data
availability will varyavailability will vary Segment the populationSegment the population Commercial risk scores are predictive Commercial risk scores are predictive
across several industries - and probably across several industries - and probably othersothers
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