Housing Finance Policy Center Lunchtime Data Talk Credit Scoring: Going Beyond the Usual

Transcription

1 Housing Finance Policy Center Lunchtime Data Talk Credit Scoring: Going Beyond the Usual Sarah Davies, VantageScore Solutions Michael Turner, PERC Kenneth Brevoort, Consumer Financial Protection Bureau Laurie Goodman, Urban Institute March 12, 2015

2 Alternative Data & Credit Scoring Sarah F. Davies Senior Vice President, Analytics & Product Management VantageScore Solutions, LLC

3 Topics. Who can be scored using traditional credit file data? The scoreable universe Criteria for scoring three gating factors How good is the score? Leveraging alternative data Rent and utility data Full-file or positive-only data VantageScore Solutions, LLC

4 The Scoreable Universe { Age < 18 (23% of US population) No hit/no files Illegal status Approx U.S. Population 2010 Census All estimates millions * May vary by Credit Bureau Credit Eligible Universe* Scored by conventional scoring models Typically un-scoreable* by conventional models VantageScore Solutions, LLC

5 Three gating factors to obtain a credit score 1. Presence of a credit file at one or more of the credit bureaus with evidence of credit management behaviors 2. Sufficient credit management behavior data Sufficient is uniquely determined by each score developer. 3. Model design to specifically leverage the data VantageScore Solutions, LLC

6 Gating Factor #1: Presence of a credit file? CREDIT FILE COMPOSITION Number of accounts Frequency of update Volumes (millions) Mainstream - High (within 6 High (=>3) Thick File months) 160 Mainstream - 1 or 2 High Thin File 20 Moderate (6-24 Infrequent Any months) 13 New Entrant < 6 months old Any 1 Rare User Any Low (> 24 months) 13 Only collections or No Trades Any public records 13 Exclusions Inquiry only/deceased 7 No File No Hit/No Files 10 No File Less than 18 years (ineligible) 71 Total: 308 VantageScore Solutions, LLC

7 Gating Factor #2: Scoring Model Inclusion Criteria. Many credit scoring models models require at least the following data: At least one trade is at least 6 months old The credit file has been updated within the last 6 months In other words, mainstream thick or thin files, 180 million consumers Consumers that fail these criteria may be excluded from receiving certain credit scores despite the availability of predictive credit file data VantageScore Solutions, LLC

8 Gating Factor #3: Using traditional data with effective segmentation Total population Previous bankruptcy No previous bankruptcy (13) No recent activity/no trades (1) Highest risk (2) Lowest risk Thin file (3) Highest risk (4) Lowest risk Full file Highest risk (5) Bankruptcy profile (6) Bad profile Higher risk Assigning consumers with similar behaviors into a single segment creates more predictive models (7) Bankruptcy profile (8) Bad profile Lower risk (9) Bankruptcy profile (10) Bad profile Lower risk (11) Bankruptcy profile (12) Bad profile VantageScore Solutions, LLC

9 Using traditional data and modeling more effectively No magic bullet or mystery Scorecard designed specifically for consumers with sparse credit files Segment 13: Consumers with. No Recent Activity No Open Trades Segment 3 & 4: Thin file consumers... New Entrants: Less than 6 months history on credit file Infrequent: Credit file updated within a 6 to 24 month window 40% 66.4% 100% 5.6% 14% 15.8% Segment ID % Of New Scoring Population % of Scorecard VantageScore Solutions, LLC

10 Segment 13 Strongest predictive variable Number of unpaid external collections with balances greater than $250 Provides meaningful predictive insight when included in the appropriate segments 70.0% 60.0% 50.0% Default Rate 40.0% 30.0% 20.0% 10.0% 0.0% VantageScore Solutions, LLC

11 Presence of a file and sufficient data? CREDIT FILE COMPOSITION Number of accounts Mainstream - High (=>3) Thick File Mainstream - Thin File Infrequent Frequency of update Volumes (millions) Conventional Models SCORED BY VantageScor e 3.0 High (within 6 months) or 2 High 20 Any Moderate (6-24 months) 13 New Entrant < 6 months old Any 1 Rare User Any Low (> 24 months) 13 Only collections or No Trades public records Exclusions Inquiry only/deceased 7 Any 13 No File No Hit/No Files 10 No File Less than 18 years (ineligible) 71 Total: 308 Insufficient Data VantageScore Solutions, LLC

12 Roughly 20% of protected class populations have insufficient credit file data for conventional scoring models but can be scored by newer models New Scoring % Of Population (Protected Class) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Black Hispanic Asian Native Am All else Conventional New Scoring Populations and distributions approximated using 2010 US Census data VantageScore Solutions, LLC

13 New Scoring Distribution Approximately 35-40* million additional consumers can be scored 12.0% 10.0% New Scoring Consumer Volumes : 21 million 580+ : 13 million : 6 million 45.0% 40.0% 35.0% % Of Population 8.0% 6.0% 4.0% 30.0% 25.0% 20.0% 15.0% Default Rate 2.0% 10.0% 5.0% 0.0% 0.0% Mainstream No Trade Rare New Entrant Infrequent Mainstream PD New Scoring PD VantageScore Solutions, LLC

14 Up to 93% (~220 million consumers) of the credit eligible population can be scored using traditional credit data Leveraging alternative data to score the remainder VantageScore Solutions, LLC

15 Scoring everyone else. leveraging alternative data Appro x227 No hit/no files { Illegal status Approximately 15 to 55* million consumers remain unscoreable depending on the credit scoring model used. Best Case No hit/no file Inquiry only Worst Case Above plus conventional model exclusions U.S. Population Scored by conventional scoring models Typically un-scoreable by conventional models * ~15 million with newer models, eg. VS3.0 ~55 million with conventional models VantageScore Solutions, LLC

16 Scoring everyone. leveraging alternative data Experian RentBureau study demonstrates the value of incorporating paid-asagreed rent payment trades Study: Simulated impact of 20,000 leases on credit file thickness and credit scores using Vantagescore 60% File thickness migration 57% 50% 40% 41% 43% 48% 30% 20% 10% 0% 11% 0% No-hit Thin File Thick File Before trade added After trade added Source: Experian RentBureau Credit for Renting, 2014 VantageScore Solutions, LLC

17 Scoring everyone. leveraging alternative data Substantial improvement in credit quality expanding access to credit at better terms 70% 60% 50% Risk segment migration 65% 53% 40% 30% 20% 10% 0% 23% 21% 17% 12% 6% 3% Score Exclusion Subprime Nonprime Prime Before trade added After trade added Source: Experian RentBureau Credit for Renting, 2014 VantageScore Solutions, LLC

18 Scoring everyone. leveraging alternative data Similar results are observed when incorporating positive energyutility data (Experian Let There Be Light, 2015) 20% of thin file consumers migrated to thick file Subprime population reduced by 47% Several challenges remain with these data Data quality and accuracy Universal reporting Impact of consumer utility laws However, it s a positive sign that major credit scores now incorporate rental payments when available on the consumer s primary credit file VantageScore Solutions, LLC

19 Positive or Full-file Data? Consumers with both Utility and Non-utility trades have slightly higher delinquency rates on their non-utility trades 88.8% 71.3% 72.7% 28.8% 27.3% Current Delq % Performance Performance on Utility Trade Performance on Non-Utility Trade Consumers with only Utility Trades Consumers with Utility and Non-Utility Trades VantageScore Solutions, LLC

20 Credit Scoring: Going Beyond the Usual PERC Presentation: March 12 th, 2015 Urban 19 Institute Washington, DC

21 Select PERC Supporters Include Foundations & Nonprofits Government & Multilaterals Private Organizations Trade Associations 20

22 Our Footprint Asia Brunei China Hong Kong India Indonesia Japan Malaysia Philippines Singapore Sri Lanka Thailand North America/ Caribbean Canada Mexico Trinidad & Tobago United States of America Central/South America Bolivia Brazil Chile Colombia Guatemala Honduras Africa Cameroon Kenya South Africa Tanzania Australia/Oceania Australia New Zealand Europe France 21

23 PERC s Alternative Data Initiative (ADI) PERC advocates the inclusion of alternative data for use in credit granting alternative = regular bill payment data from telecoms, energy utilities, rental payments and other such non-financial services that are valuable inputs for credit decisions

24 Q: Who benefits from ADI? A: The credit-underserved population The credit-underserved population is estimated to include the estimated 54 to 70 million Credit Invisible: Immigrants Students and young adults Elderly Americans Consumers operating on a cash basis Minorities Consumers trying to establish a good credit rating without new debt 23

25 PERC s ADI Research Select ADI Publications 2004 Giving Underserved Consumers Better Access to Credit Systems 2006 Give Credit where Credit is Due (w/brookings Institution) 2008 You Score You Win 2009 New to Credit from Alternative Data 2009 Credit Reporting Customer Payment Data 2012 A New Pathway to Financial Inclusion 2012 The Credit Impacts on Low-Income Americans from Reporting Moderately Late Payment Data 24

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27 A New Pathway to Financial Inclusion: ALTERNATIVE DATA, CREDIT BUILDING, AND RESPONSIBLE LENDING IN THE WAKE OF THE GREAT RECESSION June

28 Consistent credit score impacts over time Remain a no score 2% 2% Can now be scored 7% 11% Increase >= 50 2% 2% Increase between 25 and 49 3% 3% Increase between 10 and 24 4% 5% Increase less than 10 19% 19% No change 44% 48% Decline less than 10 6% 4% Decline between 10 and 24 4% 3% Decline between 25 and 49 3% 3% Decline >= 50 2% 2% 0% 10% 20% 30% 40% 50% 2005 'Utility Sample' VantageScore Change with Alt Data, All Consumers

29 Much more positive impact for thin-file Remain a no score 4% 9% Can now be scored 60% 74% Increase >= 50 4% 2% Increase between 25 and 49 7% 5% Increase between 10 and 24 6% 5% Increase less than 10 3% 1% No change 3% 1% Decline less than 10 3% 1% Decline between 10 and 24 3% 0% Decline between 25 and 49 4% 1% Decline >= 50 3% 1% 0% 10% 20% 30% 40% 50% 60% 70% 80% Utility 2009 VantageScore Change with Alt Data, Thin-file

30 VantageScore Tier Change with Alt Data Uses the ABC Tiers: is an A is a B is a C is a D is an F Unscoreable defined as lowest 29 tier More tier rises than falls

31 Change in Acceptance by Household Income (at 3% portfolio target default rate) 30% 25% 20% 15% 10% 5% 0% < $20K $20-$29K $30-$49 $50-$99 $100K+ 2009/ /

32 Score Change with Alt Data: Lowest Income Remain a no score 3% 2% Can now be scored 7% 15% Increase >= 50 4% 2% Increase between 25 and 49 5% 3% Increase between 10 and 24 7% 5% Increase less than 10 20% 19% No change 29% 48% Decline less than 10 5% 4% Decline between 10 and 24 4% 3% Decline between 25 and 49 4% 3% Decline >= 50 3% 2% 0% 10% 20% 30% 40% 50% <20K All 31

33 Change in Acceptance by Age (at 3% portfolio target default rate) 20% 15% 10% 5% 0% 18-25yr 26-35yr 36-45yr 46-55yr 56-65yr 2009/ / yr+ 32

34 VantageScore Score Change with Alt Data, Helps those with damaged credit (PR & 90+ dpd) 40% 35% 30% 25% 20% 15% 10% 5% 0% 50 pt pt pt < 10 pt No Change Can Now be Scored Decrease Increase Remain a "No Score" 55.8% see score increases, 30.2% see decreases 33

35 Research Consensus Confirms Benefits of Alternative Data March

36 Many Organizations Examined Alternative Data PERC CFSI Brookings Institution Boston Fed World Bank IFC PBOC CRC Privacy Commission (AUS, NZ, EU) Equifax Experian VantageScore FICO Lexis-Nexis MicroBilt SAS Institute Types of Data Examined: Utility payments, Rent Payments, Telecom Payments, Pay TV, Cable, and Underutilized Public Records

37 Broad Findings A Consensus How Big of an Issue is Credit Invisibility? At least tens of millions Who are the Credit Invisible? Disproportionately low income, young, elderly, ethnic minority What is the Risk Profile of the Credit Invisible? Somewhat riskier than average, has a smaller superprime group, but contains a large number of moderate to low risk consumers. The group is NOT monolithically high risk. How Can Alternative Data Help Eliminate Credit Invisibility? Alternative data is found to be predictive of future performance of financial accounts alternative data can be used to underwrite credit majority of Credit Invisible can become scoreable with alternative data

38 Predicting Financial Account Delinquencies with Utility and Telecom Payment Data March / April

39 Alt Data is Predictive of Financial Accounts 30+ DPD Delinquency Rate or Public Record (July July 2010) On time and severely delinquent Alt Data Payers (Utility + Telecom) measured prior to July 2009

40 Alt Data is Predictive of Mortgages 30+ DPD Delinquency Rate on Mortgage Accounts (July July 2010)* 80% 70% 60% 59.80% 70.00% 50% 40% 30% 20% 10% 7.50% 10.20% 13.40% 0% Never 30+ DPD on Alt Tradeline No 90+ DPD ever on Alt Tradeline All DPD on an Alt tradeline previous 12 months >1 90+ DPD on Alt tradelines previous 12 months *Only includes those with an active mortgage

41 Alt Data is Predictive of Clean Mortgages 30+ DPD Delinquency Rate on a previously Clean Mortgage Accounts (July 2009-July 2010)* 30% 25% 20% 22.30% 26.20% 15% 10% 5% 4.10% 4.90% 5.40% 0% Never 30+ DPD on Alt Tradeline No 90+ DPD ever on Alt Tradeline All DPD on an Alt tradeline previous 12 months >1 90+ DPD on Alt tradelines previous 12 months *Only includes those with an active mortgage, Clean Mortgage defined as no delinquencies reported for mortgages for the 24 months prior to July 2009

42 Alt Data is Predictive of Clean Mortgages after Accounting for Traditional Data 30+ DPD Delinquency Rate on previously Clean Mortgage Accounts (July July 2010) by VantageScore Credit Score* 40% 35% 36.60% 30% 25% 28.30% 27.10% 20% 18.70% 16.90% 15% 10% 11.30% 8.40% 5% 0% 4.70% 3.00% 1.10% Never 30+ DPD on Alt Data DPD on Alt Data in Past 12 Months *Only includes those with an active mortgage, Clean Mortgage defined as no delinquencies reported for mortgages for the 24 months prior to July 2009, VantageScore used here only includes Traditional Data

43 Alt Data Contains New, Useful Information That may not be found in Traditional Accounts Shares of Previously Clean Mortgage Sample with / without Previous 90+ DPDs Previously Clean Mortgage Delinquency Rates with / without Previous 90+ DPDs Consumers with Past Alt Data Delinquencies but no Past Financial Acct Delinquencies are not seen by lenders but are higher risk

44 Consumer Friendly Reporting For instance: Use restriction (not for employment screening or insurance underwriting) Exclude all negatives less than 90 days Report assistance as paid as agreed or exclude (e.g. LIHEAP) Exclude unpaid balances on closed accounts (e.g. <$100) 43

45 Other Alternative Data Being Used Rental data United States (certain locations) Colombia (in Bogota area) South Africa (Johannesburg area) Trade supply (not trade credit) for FMCG Agricultural supply data (for rural lending) Some fit into credit bureau model, others do not 44

46 Digital Data Being Tested/Used Promise of improving credit access for urban and rural poor in emerging economies: Mobile microfinance Development of mobile based interface for financial services offers new opportunities for risk assessment Unified platform for application and distribution Data o Payment and prepayment patterns o Social collateral from call log data Smart (Philippines), M-Shwari (Kenya), Cignifi (Brazil) Mobile data in bank lending First Access (Tanzania) 45

47 Hurdles to Reporting (US) Technological barriers to reporting: Complex billing cycles (footprint dependent) Legacy IT systems Regulatory barriers: Some states have statutory prohibitions Regulatory uncertainty Jurisdictional issues FCC, state PUCs/PSCs, CFPB Economic barriers: Compliance costs FCRA data furnisher obligations Customer service costs from lenders scaring customers substantial Incentives, what do you get for sharing data? 46

48 How Should We Approach Alt Data For traditional providers, Incentives are different. Banks are users of the data, so they get something for what they give. Confidentiality concerns are different banks are backed by regulation, by safety and soundness concerns, and by a postpaid relationship. Not so with alt data furnishers. Fairness: why should these sources give a bureau data for free, so that a bureau can make money off of it? Here s where regulators can help, in pushing financial inclusion mission, and in helping the system develop trust. 47

49 Big Data and Data Fiefdoms Some observations from the field: McKinsey effect Growing belief that every firm is sitting on a gold mine. Seeking to monetize data assets. Data Fiefdoms Data becoming more fragmented (MNOs, banks on SME credit, banks) All want to be CRA/info service provider Muddy Waters Traditional alternative data vs. Fringe alternative data (Robinson+Yu) Sensing increased uncertainty among regulators/policymakers Here s where regulators can help in pushing financial inclusion mission, and in helping the system develop trust. 48

50 302 East Pettigrew Street Suite 130 Durham, NC (919) x803

51 Credit Scoring: Going Beyond the Usual Housing Finance Policy Center Lunchtime Data Talk Ken Brevoort Section Chief, Credit Information & Policy Office of Research Consumer Financial Protection Bureau March 12, 2015 The opinions in this presentation are those of the author and do not necessarily reflect the views of the Consumer Financial Protection Bureau or the United States.

52 CFPB Report on Remittance Histories Released July 2014 Remittance: Electronic transfers of funds to recipients abroad Found: Remittance histories add very little to the predictiveness of a credit scoring model. The opinions in this presentation are those of the author and do not necessarily reflect the views of the Consumer Financial Protection Bureau or the United States. 51

53 My Office The opinions in this presentation are those of the author and do not necessarily reflect the views of the Consumer Financial Protection Bureau or the United States. 52

54 Why Are Some Records Unscorable? Model builders are unable to predict which consumers will repay their loans Reasons why: A lack of information about the consumer Alternative data can help here, but How many thin files have this information? Is alternative data really predictive? Building a model requires both left- and right-hand-side variables, so we need observable performance Alternative data unlikely to help here The opinions in this presentation are those of the author and do not necessarily reflect the views of the Consumer Financial Protection Bureau or the United States. 53

55 Why is this Important? An Example Utility payment information for random sample of 1 million consumers with unscorable records Credit Record data from end of 2012 and end of 2014 Credit Characteristics from 2012 Credit Performance in 2013 and 2014 from 2014 data Thin files are less likely to have performance that is observable in the data If only 10 percent have observable performance, the model Will be estimated using only 100,000 observations May prove unreliable when extrapolated to the other 90 percent of consumers with unscorable records The opinions in this presentation are those of the author and do not necessarily reflect the views of the Consumer Financial Protection Bureau or the United States. 54

56 Conclusions Sarah Davies and Michael Turner are doing important and interesting work! There are a lot of reasons to be enthusiastic about alternative data s potential But until the predictive power of these data are reliably demonstrated, we should be cautious in advocating the use of such data The opinions in this presentation are those of the author and do not necessarily reflect the views of the Consumer Financial Protection Bureau or the United States. 55