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Credit Scoring:
1
Going
Beyond the
Usual
PERC Presentation: March 12th
, 2015
Urban Institute—Washington, DC
Select PERC Supporters Include…
Foundations
& Nonprofits
Government &
Multilaterals
Trade
Associations
Private
Organizatio...
Our Footprint
Africa
Cameroon
Kenya
South Africa
Tanzania
North America/
Caribbean
Canada
Mexico
Trinidad & Tobago
United ...
PERC’s
Alternative
Data
Initiative
(ADI)
PERC advocates the inclusion of alternative data for use in
credit granting
alter...
Q: Who benefits from ADI?
A: The credit-underserved population
The credit-underserved population is estimated to include t...
PERC’s ADI Research
Select ADI Publications
2004 Giving Underserved Consumers Better Access to Credit Systems
2006 Give Cr...
Research has shown that using alternative data for
credit granting results in:
Increased, Safer, Sounder,
Fairer and Broad...
A New Pathway to Financial Inclusion:
8
ALTERNATIVE DATA,
CREDIT BUILDING, AND
RESPONSIBLE LENDING
IN THE WAKE OF THE
GREA...
9
Consistent credit score impacts over time…
VantageScore Change with Alt Data, All Consumers
10
Much more ‘positive’ impact for thin-file
VantageScore Change with Alt Data, Thin-file
11
VantageScore Tier Change with Alt Data
Uses the ‘ABC’ Tiers:
900-990 is an A
800-899 is a B
700-799 is a C
600-699 is a...
12
Change in Acceptance by Household Income
(at 3% portfolio target default rate)
13
Score Change with Alt Data: Lowest Income
14
Change in Acceptance by Age
(at 3% portfolio target default rate)
15
VantageScore Score Change with Alt Data,
Helps those with damaged credit (PR & 90+ dpd)
55.8% see score increases, 30.2...
Research Consensus Confirms
Benefits of Alternative Data
16
March 2015!
Research(Consensus(Confirms(
Benefits(of(Alternati...
Many Organizations Examined Alternative Data
Types of Data Examined: Utility payments, Rent
Payments, Telecom Payments, Pa...
Broad Findings…A Consensus
How Big of an Issue is Credit Invisibility?
Who are the Credit Invisible?
At least tens of mill...
Predicting Financial Account Delinquencies with
Utility and Telecom Payment Data
19
March / April 2015
Alt Data is Predictive of Financial Accounts
30+ DPD Delinquency Rate or Public Record
(July 2009- July 2010)
On time and ...
30+ DPD Delinquency Rate on Mortgage Accounts
(July 2009- July 2010)*
Alt Data is Predictive of Mortgages
*Only includes t...
30+ DPD Delinquency Rate on a previously Clean Mortgage
Accounts (July 2009-July 2010)*
Alt Data is Predictive of Clean Mo...
30+ DPD Delinquency Rate on previously Clean Mortgage Accounts
(July 2009- July 2010) by VantageScore Credit Score*
*Only ...
Shares of Previously Clean Mortgage Sample with / without Previous
90+ DPDs
Previously Clean Mortgage Delinquency Rates wi...
25
‘Consumer Friendly’ Reporting
For instance:
• Use restriction (not for employment
screening or insurance underwriting)
...
26
Other Alternative Data
Being Used
Rental data
 United States (certain locations)
 Colombia (in Bogota area)
 South A...
27
Digital Data Being Tested/Used
Promise of improving credit access for urban and rural
poor in emerging economies:
Mobil...
Hurdles to Reporting (US)
28
Technological barriers to reporting:
 Complex billing cycles (footprint dependent)
 Legacy ...
29
How Should We Approach Alt Data
For traditional providers, Incentives are different.
Banks are users of the data, so th...
30
Big Data and Data Fiefdoms
Some observations from the field:
 McKinsey effect
› Growing belief that every firm is sitt...
302 East Pettigrew Street
Suite 130
Durham, NC 27701
www.perc.net
(919) 338-2798 x803
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PERC_031215_UI Briefing_Final_1(1)

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PERC_031215_UI Briefing_Final_1(1)

  1. 1. Credit Scoring: 1 Going Beyond the Usual PERC Presentation: March 12th , 2015 Urban Institute—Washington, DC
  2. 2. Select PERC Supporters Include… Foundations & Nonprofits Government & Multilaterals Trade Associations Private Organizations 2
  3. 3. Our Footprint Africa Cameroon Kenya South Africa Tanzania North America/ Caribbean Canada Mexico Trinidad & Tobago United States of America Asia Brunei China Hong Kong India Indonesia Japan Malaysia Philippines Singapore Sri Lanka Thailand Australia/Oceani a Australia New Zealand Europe France Central/South America Bolivia Brazil Chile Colombia Guatemala Honduras 3
  4. 4. 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
  5. 5. 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 5
  6. 6. 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 6
  7. 7. Research has shown that using alternative data for credit granting results in: Increased, Safer, Sounder, Fairer and Broader Lending What have we found? 7
  8. 8. A New Pathway to Financial Inclusion: 8 ALTERNATIVE DATA, CREDIT BUILDING, AND RESPONSIBLE LENDING IN THE WAKE OF THE GREAT RECESSION June 2012
  9. 9. 9 Consistent credit score impacts over time… VantageScore Change with Alt Data, All Consumers
  10. 10. 10 Much more ‘positive’ impact for thin-file VantageScore Change with Alt Data, Thin-file
  11. 11. 11 VantageScore Tier Change with Alt Data Uses the ‘ABC’ Tiers: 900-990 is an A 800-899 is a B 700-799 is a C 600-699 is a D 501-599 is an F Unscoreable defined as lowest tier More tier rises than falls
  12. 12. 12 Change in Acceptance by Household Income (at 3% portfolio target default rate)
  13. 13. 13 Score Change with Alt Data: Lowest Income
  14. 14. 14 Change in Acceptance by Age (at 3% portfolio target default rate)
  15. 15. 15 VantageScore Score Change with Alt Data, Helps those with damaged credit (PR & 90+ dpd) 55.8% see score increases, 30.2% see decreases
  16. 16. Research Consensus Confirms Benefits of Alternative Data 16 March 2015! Research(Consensus(Confirms( Benefits(of(Alternative(Data( ! March(2015( Authors:( Michael(A.(Turner,(Ph.D.( Robin(Varghese,(Ph.D.( Patrick(Walker,(M.A.(
  17. 17. Many Organizations Examined Alternative Data Types of Data Examined: Utility payments, Rent Payments, Telecom Payments, Pay TV, Cable, and Underutilized Public Records
  18. 18. Broad Findings…A Consensus How Big of an Issue is Credit Invisibility? Who are the Credit Invisible? At least tens of millions 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
  19. 19. Predicting Financial Account Delinquencies with Utility and Telecom Payment Data 19 March / April 2015
  20. 20. Alt Data is Predictive of Financial Accounts 30+ DPD Delinquency Rate or Public Record (July 2009- July 2010) On time and severely delinquent Alt Data Payers (Utility + Telecom) measured prior to July 2009
  21. 21. 30+ DPD Delinquency Rate on Mortgage Accounts (July 2009- July 2010)* Alt Data is Predictive of Mortgages *Only includes those with an active mortgage
  22. 22. 30+ DPD Delinquency Rate on a previously Clean Mortgage Accounts (July 2009-July 2010)* Alt Data is Predictive of Clean Mortgages *Only includes those with an active mortgage, Clean Mortgage defined as no delinquencies reported for mortgages for the 24 months prior to July 2009
  23. 23. 30+ DPD Delinquency Rate on previously Clean Mortgage Accounts (July 2009- July 2010) by VantageScore Credit Score* *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 Alt Data is Predictive of Clean Mortgages after Accounting for Traditional Data
  24. 24. Shares of Previously Clean Mortgage Sample with / without Previous 90+ DPDs Previously Clean Mortgage Delinquency Rates with / without Previous 90+ DPDs Alt Data Contains New, Useful Information That may not be found in Traditional Accounts Consumers with Past Alt Data Delinquencies but no Past Financial Acct Delinquencies are not seen by lenders but are higher risk…
  25. 25. 25 ‘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)
  26. 26. 26 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
  27. 27. 27 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)
  28. 28. Hurdles to Reporting (US) 28 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?
  29. 29. 29 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 post- paid 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.
  30. 30. 30 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.
  31. 31. 302 East Pettigrew Street Suite 130 Durham, NC 27701 www.perc.net (919) 338-2798 x803

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