Transcript Title of Presentation
Leveraging Alternative Credit Data to Make Better Risk Decisions
David Shellenberger
Senior Director, Scoring and Advanced Analytics FICO
Ankush Tewari
Director, Credit Risk Decisioning LexisNexis © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Joe Muchnick
Vice President, Enterprise Alliance Equifax
Agenda
► Alternative Data Opportunities ► Evaluating Data Sources ► Initial Research Findings 2 © 2014 Fair Isaac Corporation. Confidential.
US Credit Population by the numbers :
Over 28 million have traditional credit files but insufficient data for scoring
Approximately 170 million have scoreable traditional credit files.
More than 25 million have no traditional credit files but will have a traditional credit file in the next 24 months. The FICO ® score has three key minimum scoring criteria: • The consumer cannot be deceased.
• The credit file needs one trade line reported by a creditor within the last six months.
• The credit file needs one trade line that is at least six months old • 54 million will go on to open a credit account in the next 6 months • Of these, 2.8 million are unscoreable or have no traditional credit file at time of application.
© 2014 Fair Isaac Corporation. Confidential.
Segment Performance
► Observing payment behavior on newly opened trade lines over 24 months we see that unscoreable applicants as a whole are more risky than those with some credit information
Scoreable Segment
Thick and mature histories with no derogatory information Files with derogatory information 4 © 2014 Fair Isaac Corporation. Confidential.
Bad Rate
2.1% 17.3%
Unscoreable Segment
Inactive credit history without derogatory information Public record or collection account only
Bad Rate
6.4% 32.3%
Alternative Data Considerations
►
Regulatory compliance:
The data source must comply with all regulations governing consumer credit evaluation ►
Depth of information:
Data sources that are deeper and contain greater detail are often of greater value ►
Scope of coverage:
A database covering a broad percentage of consumers can be favorable ►
Accuracy:
How reliable is the data? How is it reported? Is it self-reported? Are there verification processes in place? ►
Predictiveness:
The data should predict future consumer repayment behavior ►
Orthogonality:
Useful data sources should be supplemental or complementary to what’s captured by other data sources 5 © 2014 Fair Isaac Corporation. Confidential.
Evaluating Data Sources
6 © 2014 Fair Isaac Corporation. Confidential.
Live Interview
7 © 2014 Fair Isaac Corporation. Confidential.
Consumer Services Database
TM
(CSD)
CSD data is a collection of consumer identity, contact and payment information in the Telco, Pay TV and Utilities industries
Telco Pay TV Utilities
© 2014 Fair Isaac Corporation. Confidential.
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Coverage of the Consumer Services Database
TM The Consumer Services Database TM includes 186 million unique consumers of which 25M are under or unbanked consumers
Account Types 60+ data contributors
Wireless, Landline, Cable, Satellite, Gas and Electric providers
Utility 3% Cable/Pay TV 24% Top National Providers
Top 2 Telco providers and top 3 Cable / Pay TV providers
Landline 30% Historical data beginning May 2009
including both closed and open accounts
Mobile 43% 186 million unique consumers
9 © 2014 Fair Isaac Corporation. Confidential.
Data and Attributes of the Consumer Services Database
TM
Data:
Personally identifiable account information
e.g., account number or service type
Account payment information
e.g., account status, balance, payment, past due and charge off amount
Attribute Examples:
► # of accounts connected in last 3, 6, 12 or 24 months ► # of involuntary disconnected accounts ► Average period since latest connection ► ►
Solutions
# of delinquent trades in 30+, 60+ 90+ or in charge off ► Time since most recent delinquency in CSD ► Percent of delinquent trades in CSD 10 © 2014 Fair Isaac Corporation. Confidential.
Example 1: # of months since opening a satisfactory account
The higher the number of months since the connection of a satisfactory account the lower the risk
Bad Rate 6% Segment Bad Rate 5% 4% 3% 2% Growth Opportunity 1%
Bad Rate Segment Bad Rate
0% No Accounts 0 to 6 Months 7 to 17 Months 18 to 31 Months # of months since opening a satisfactory account
Note: Example population reflects consumer with a prior bankruptcy
32 to 52 Months
© 2014 Fair Isaac Corporation. Confidential.
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53+ Months
Example 2: # of satisfactory telecom accounts
An increase in satisfactory telecomm accounts indicates lower risk
Bad Rate 10% 8% Segment Bad Rate 6% 4% 2%
Bad Rate Segment Bad Rate
0% None 1 to 1
© 2014 Fair Isaac Corporation. Confidential.
2 to 3 4 to 5
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6 to 8 9 to 10 # of satisfactory telecom accounts Note: Example population reflects consumer with Low Score, Thin File, Without Prior Bankruptcy Population 11 to 12 Growth Opportunity 13 to 22 23+
Case Study: The Future of Retail Banking
CSD data is helping increase profits for Demand Deposit Accounts Tight regulatory oversight is curtailing fee income Tough economic conditions mean fewer consumers are shopping their financial services Less profit potential for retail banks Traditional Score vs CSD Score
% Lift Over Credit: DDA Accounts 80% 60% 40% 20% 0%
Traditional Credit Score CSD Score Worst 5% CO Capture ($) Worst 10% CO Capture ($) Worst 15% CO Capture ($) Worst 20% CO Capture ($) 13 © 2014 Fair Isaac Corporation. Confidential.
1 Live Interview
RiskView data provides insight into creditworthiness using a mix of public record and non-traditional sources STABILITY ABILITY TO REPAY WILLINGNESS TO REPAY Address Changes Property Value Criminal Records Home Ownership Economic Stability Occupational Licenses Education History Bankruptcies, Liens, Judgments Evictions and Foreclosures
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LexisNexis Data Highly Correlated to Default Risk Address Changes Stable Addresses: 5X Less Risky Presence of Eviction: 3X more risky Evictions High Value: 7X Less Risky Value of Residence
Source: LexisNexis analysis of credit bureau extract 16
RiskView Case Study: Augmenting Bureau Scores in Auto Lending
RiskView Performance in the Auto Industry • • • LexisNexis periodically tests RiskView’s ability to augment bureau-based scores In 2013, we conducted research on a combined portfolio of auto loans from multiple lenders The portfolio contained a bureau-based score for each loan as well as the performance of the loan; bad defined as 90 DPD within 24 months of origination • RiskView added lift to the lending decisions in all segments ranging from superprime to deep subprime and also the bureau unscorables 18
Baseline Bureau-only Score Performance 16% 11% 9%
Bad Rate Distribution Based on Bureau Score Only
14,4% 13,3% 10,8% 6,8% 6% 2,4% 1% 0,3% -4% No Score E (300 - 500) D (500 - <550) C (550 - <600) B (600 - <650) AB (650 - <750) AA (750 - 850) Total Sample Size: 108,044 Total Bads: 5,035 19
RiskView Performance on High Risk Segment: Bureau Score between 500 and 550 30% 25% 20% 15% 10% 5% 0%
Bad Rate Distribution Based on RiskView Score
28,1% 21,1% 19,8% 18,2% Segment Bad Rate = 13.3% 17,2% 14,6% 10,6% 9,4% % of Segment 500 <520 28.1% 520 <540 21.1% 540 <560 560 <580 580 <600
RiskView Score Bands
600 <620 19.8% 18.2% 17.2% 14.6% 620 <640 10.6% 640+ 9.4% 20
RiskView Performance on High Risk Segment: Bureau Score between 550 and 600 25% 20% 15% 10%
Bad Rate Distribution Based on RiskView Score
23,0% 20,9% 16,9% 15,2% 15,1% Segment Bad Rate = 10.8% 11,9% 11,5% 9,9% 8,3% 6,0% 3,7% 5% 0% 500 <520 520 <540 540 <560 560 <580 580 <600 600 <620 620 <640
RiskView Score Bands
640 <660 660 <680 680 <700 700+ % of Segment 1.4% 1.9% 3.3% 5.2% 8.0% 12.2% 17.7% 18.6% 14.9% 9.0% 7.2% 21
RiskView Performance on Moderate Risk Segment: Bureau Score between 600 and 650
Bad Rate Distribution Based on RiskView Score
18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 15,6% 14,2% 14,1% 10,5% 10,1% 6,4% 5,7% Segment Bad Rate = 6.8% 4,0% % of Segment <560 1.4% 560 <580 1.7% 580 <600 3.1% 600 <620 620 <640 640 <660 660 <680
RiskView Score Bands
7.2% 15.2% 20.0% 20.8% 680 <700 16.0% 3,8% 700 <720 8.3% 2,2% 720+ 6.0% 22
Initial Research Findings
23 © 2014 Fair Isaac Corporation. Confidential.
FICO’s Alternative Data Score Research
► Focus on national sampling of consumers with little to no credit data ► Evaluated incremental value over credit bureau data for credit risk evaluation ► Core sample for the research was obtained from Equifax ► 15 million consumers at two points in time ► Observation date of May 2011, with performance being evaluated through May 2013 ► Performance measured on all credit trades opened no later than 6 months following observation date ► Alternative data provided as of observation date, May 2011 24 © 2014 Fair Isaac Corporation. Confidential.
Which Consumers Are Included in FICO ® Score Development?
BUREAU SNAPSHOT A BUREAU SNAPSHOT B
Scoring date Performance date 2 years
Only consumers who had measurable credit repayment history between Snapshots A and B – i.e., “classifiable performance” - are included in FICO ® Score model development
© 2014 Fair Isaac Corporation. Confidential.
Sizing the Unscorable Population ► The following segments are candidates for inclusion in the scoreable universe:
Segment “New-to-Credit” Files-
No tradeline opened at least 6 months
“Derogatory” Files-
Files with delinquent tradelines, collections or adverse public records
“Stale” Files –
No tradeline updated in last 6 months
“No Hits”-
No credit files
Total Non-Scoreable Files Population Size
3 MM 18 MM 7 MM 25 MM 53 MM © 2014 Fair Isaac Corporation. Confidential.
Minimum Scoring Analysis
Evaluation Criteria
► Classifiable performance rates ► % of non-scorables with classifiable Known and All Good/Bad performance ► Raw # of Good and Bad consumers ► Performance metrics ► Divergence, ROC area and KS ► Alignment plots ► Visual inspection of flattening of odds-to-score relationship ► Propensity Score-based assessment of common support ► Do the consumers included in model development profile similarly to the rest of the segment not included in model development?
© 2014 Fair Isaac Corporation. Confidential.
Min Scoring Analysis Template: Assessing Predictive Strength and Odds-to-Score Consistency
► Calculate performance stats and calculate odds-to-score fit to gauge degree of degradation across potentially “scorable” segments
Staleness (as measured by Months Since Most Recent Bureau Update) Divergence All Stales (7+ months) Baseline1: 0-6 months ROC Area KS Odds-to_Score Slope Odds-to-Score Intercept PDO Baseline2: 3-6 months 7-8 months 9-11 months 12-14 months 15-17 months 18-20 months 21-23 months …
© 2014 Fair Isaac Corporation. Confidential.
Extending the Scoreable Population
Stale files
• One trade line reported in last 24 months
FICO ® Score Minimum Scoring Criteria
• The credit file needs one trade line reported by a creditor within the last six months.
• The credit file needs one trade line that is at least six months old New minimum scoring criteria with the inclusion of alternative credit data
Derogatory files
• One trade line/collection/public record reported in last 24 months
New to credit files
• One trade line opened more than one month
or
• No tradelines and one inquiry within the last 6 months
No credit file
• Additional LexisNexis or CSD reported information © 2014 Fair Isaac Corporation. Confidential.
Some Relevant Numbers from the Research Dataset
Total number of unscoreable and no hit files………53 million Total number of unscoreable and no hit files with LexisNexis match……38 million Total number of unscoreable and no hit files meeting minimum scoring criteria …15 million © 2014 Fair Isaac Corporation. Confidential.
Extending the Scoreable Population
Stale files Derogatory files New to credit files No credit file
Unscoreable applicant population now scoreable Segment bad rate
43% 6.2% 47% 34.2% 76% 54% 18.4% 14.6%
© 2014 Fair Isaac Corporation. Confidential.
Aligning Score Segments to FICO 9
© 2014 Fair Isaac Corporation. Confidential.
Solid Rank Ordering of Good and Bad Accounts
Within Total Alt Data Scoreable population
Trade Off Curves- Good vs Bad accounts © 2014 Fair Isaac Corporation. Confidential.
Alternative Data Score Distribution Skews Lower
Although more than a third score above 620
© 2014 Fair Isaac Corporation. Confidential.
The Majority of Scores above 620 are in Stale and No Credit Segments
© 2014 Fair Isaac Corporation. Confidential.
Score Distribution Varies Greatly by Segment
Segment
Stales Derogs New-to-credit No Credit File All Alt Data Segments
Pop Odds
15.04
1.93
4.44
5.83
3.61
© 2014 Fair Isaac Corporation. Confidential.
Dropping Derogatory Files Shifts the Score Distribution
© 2014 Fair Isaac Corporation. Confidential.
Key Insights
► Alternative credit data can be very effective in extending the scoreable population ► Not all unscoreables are alike ► When selecting data partners know their stability, compliance and operational abilities in addition to the predictive power of their data ► Even with the use of alternative credit data, minimum scoring criteria should still be investigated © 2014 Fair Isaac Corporation. Confidential.
Thank You!
David Shellenberger
(415) 491-7064
Ankush Tewari
(678) 694-2140 39 © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Joe Muchnick
(404) 885-8210
Learn More at FICO World
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Blogs
► www.fico.com/blog 40 © 2014 Fair Isaac Corporation. Confidential.
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David Shellenberger
(415) 491-7064
Ankush Tewari
(678) 694-2140 41 © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Joe Muchnick
(404) 885-8210