© 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. 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 Joe Muchnick Vice President, Enterprise Alliance Equifax

Agenda © 2014 Fair Isaac Corporation. Confidential. ► 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 More than 25 million have no traditional credit files but will have a traditional credit file in the next 24 months. 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. Approximately 170 million have scoreable traditional credit files. 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

© 2014 Fair Isaac Corporation. Confidential. ► 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 Segment Performance Scoreable SegmentBad Rate Thick and mature histories with no derogatory information 2.1% Files with derogatory information17.3% Unscoreable SegmentBad Rate Inactive credit history without derogatory information 6.4% Public record or collection account only 32.3% 4

© 2014 Fair Isaac Corporation. Confidential. ► 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 Alternative Data Considerations 5

© 2014 Fair Isaac Corporation. Confidential. Evaluating Data Sources 6

© 2014 Fair Isaac Corporation. Confidential. Live Interview 7

© 2014 Fair Isaac Corporation. Confidential. CSD data is a collection of consumer identity, contact and payment information in the Telco, Pay TV and Utilities industries Consumer Services Database TM (CSD) 8 TelcoPay TVUtilities

© 2014 Fair Isaac Corporation. Confidential. Coverage of the Consumer Services Database TM 9 The Consumer Services Database TM includes 186 million unique consumers of which 25M are under or unbanked consumers Landline 30% Utility 3% Account Types Cable/Pay TV 24% Mobile 43% 60+ data contributors Wireless, Landline, Cable, Satellite, Gas and Electric providers Top National Providers Top 2 Telco providers and top 3 Cable / Pay TV providers Historical data beginning May 2009 including both closed and open accounts 186 million unique consumers

© 2014 Fair Isaac Corporation. Confidential. Products and Solutions 10 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 ► Balance reported in last 3, 6 or 12 months ► # of delinquent trades in 30+, or in charge off ► Time since most recent delinquency in CSD ► Percent of delinquent trades in CSD Data and Attributes of the Consumer Services Database TM

© 2014 Fair Isaac Corporation. Confidential. The higher the number of months since the connection of a satisfactory account the lower the risk Example 1: # of months since opening a satisfactory account 11 Segment Bad Rate Bad Rate # of months since opening a satisfactory account Note: Example population reflects consumer with a prior bankruptcy Growth Opportunity

© 2014 Fair Isaac Corporation. Confidential. An increase in satisfactory telecomm accounts indicates lower risk Example 2: # of satisfactory telecom accounts 12 Segment Bad Rate Bad Rate # of satisfactory telecom accounts Note: Example population reflects consumer with Low Score, Thin File, Without Prior Bankruptcy Population Growth Opportunity

© 2014 Fair Isaac Corporation. Confidential. CSD data is helping increase profits for Demand Deposit Accounts Case Study: The Future of Retail Banking 13 Tight regulatory oversight is curtailing fee income Tough economic conditions mean fewer consumers are shopping their financial services Less profit potential for retail banks Worst 5% CO Capture ($)Worst 10% CO Capture ($)Worst 15% CO Capture ($)Worst 20% CO Capture ($) 80% 60% 40% 20% 0% % Lift Over Credit: DDA Accounts Traditional Score vs CSD Score Traditional Credit Score CSD Score

Live Interview 14

RiskView data provides insight into creditworthiness using a mix of public record and non-traditional sources WILLINGNESS TO REPAYABILITY TO REPAYSTABILITY Address Changes Home Ownership Economic Stability Property Value Occupational Licenses Education History Criminal Records Bankruptcies, Liens, Judgments Evictions and Foreclosures 15

LexisNexis Data Highly Correlated to Default Risk Source: LexisNexis analysis of credit bureau extract 16 Address Changes Evictions Stable Addresses: 5X Less Risky Presence of Eviction: 3X more risky Value of Residence High Value: 7X Less Risky

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 Total Sample Size: 108,044 Total Bads: 5,035 19

RiskView Performance on High Risk Segment: Bureau Score between 500 and 550 Segment Bad Rate = 13.3% % of Segment28.1%21.1%19.8%18.2%17.2%14.6%10.6%9.4% 20

RiskView Performance on High Risk Segment: Bureau Score between 550 and 600 Segment Bad Rate = 10.8% % of Segment1.4%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 Segment Bad Rate = 6.8% % of Segment1.4%1.7%3.1%7.2%15.2%20.0%20.8%16.0%8.3%6.0% 22

© 2014 Fair Isaac Corporation. Confidential. Initial Research Findings 23

© 2014 Fair Isaac Corporation. Confidential. ► 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 FICO’s Alternative Data Score Research 24

© 2014 Fair Isaac Corporation. Confidential. BUREAU SNAPSHOT A Scoring date BUREAU SNAPSHOT B Performance date Which Consumers Are Included in FICO ® Score Development? 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: SegmentPopulation Size “New-to-Credit” Files- No tradeline opened at least 6 months 3 MM “Derogatory” Files- Files with delinquent tradelines, collections or adverse public records 18 MM “Stale” Files – No tradeline updated in last 6 months7 MM “No Hits”- No credit files25 MM Total Non-Scoreable Files53 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) DivergenceROC AreaKSOdds-to_Score SlopeOdds-to-Score InterceptPDO All Stales (7+ months) Baseline1: 0-6 months Baseline2: 3-6 months 7-8 months 9-11 months months months months months …

© 2014 Fair Isaac Corporation. Confidential. Extending the Scoreable Population 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 Stale files One trade line reported in last 24 months 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 43% 47% 76% 54% Segment bad rate 6.2% 34.2% 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

© 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. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. Thank You! 39 David Shellenberger (415) Joe Muchnick (404) Ankush Tewari (678)

© 2014 Fair Isaac Corporation. Confidential. Learn More at FICO World Related Sessions ► The FICO ® Score: 25 Years of Democratizing Access to Credit ► Separating Fact from Fiction in the Marketing of Credit Scores Products in Solution Center ► FICO ® Score 9 Experts at FICO World ► Dave Shellenberger ► Brian Cooper ► Freddie Huynh White Papers Online ► Insights #40: To Score or Not to Score Blogs ► 40

© 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. David Shellenberger (415) Please rate this session in the FICO World App! 41 Joe Muchnick (404) Ankush Tewari (678)