Sarah Davies , VantageScore Solutions
Michael Turner , PERC
Kenneth Brevoort , Consumer Financial Protection Bureau
Laurie Goodman , Urban Institute
March 12, 2015

Sarah F. Davies
Senior Vice President, Analytics &
Product Management
203-363-2162 VantageScore Solutions, LLC

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 © 2014
 2

The Scoreable Universe….
308
71
10
{
Age < 18 (23% of US population)
No hit/No files
Illegal status
47
Approx.
227
180
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 © 2014
 3

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 © 2014
 4

Gating Factor #1: Presence of a credit file?
CREDIT FILE COMPOSITION
Number of accounts
Frequency of update
Volumes
(millions)
Mainstream -
Thick File
High (=>3)
Mainstream -
Thin File
1 or 2
Infrequent Any
Rare User Any
High (within 6 months)
No Trades
Only collections or public records
Any
Exclusions Inquiry only/Deceased
No File No Hit/No Files
High
Moderate (6-24 months)
New Entrant < 6 months old Any
Low (> 24 months)
No File Less than 18 years (ineligible)
160
20
13
1
13
13
7
10
71
Total: 308
VantageScore Solutions, LLC © 2014
 5

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 © 2014
 6

Gating Factor #3: Using traditional data with effective segmentation
Total population
Previous bankruptcy
(1) Highest risk
(2) Lowest risk
No previous bankruptcy
Thin file
(3) Highest risk
(4) Lowest risk
• Assigning consumers with similar behaviors into a single segment creates more predictive models
(13) No recent activity/no trades
Full file
Highest risk
(5) Bankruptcy profile
(6) Bad profile
Higher risk
(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 © 2014
 7

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%
14%
15.8%
5.6%
66.4%
100%
4 3
Segment ID
% Of New Scoring Population % of Scorecard
13
VantageScore Solutions, LLC © 2014
 8

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%
40.0%
30.0%
20.0%
10.0%
0.0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
VantageScore Solutions, LLC © 2014
 9

Presence of a file and sufficient data?
CREDIT FILE COMPOSITION
Number of accounts
Frequency of update
SCORED BY
Volumes
(millions)
Conventional
Models
VantageScor e 3.0
Mainstream -
Thick File
High (=>3)
Mainstream -
Thin File
1 or 2
Infrequent Any
Rare User Any
High (within 6 months)
No Trades
Only collections or public records
Any
Exclusions Inquiry only/Deceased
No File No Hit/No Files
High
Moderate (6-24 months)
New Entrant < 6 months old Any
Low (> 24 months)
No File Less than 18 years (ineligible)
160
20
✔
✔
✔
✔
13 ✗ ✔
1 ✗ ✔
13 ✗ ✔
13 ✗ ✔
7
10
71
Total: 308
Insufficient Data
VantageScore Solutions, LLC © 2014
 10

Roughly 20% of protected class populations have insufficient credit file data for conventional scoring models – but can be scored by newer models
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
6.7
6.0
1.4
0.3
Black Hispanic Asian
Conventional New Scoring
Native Am
Populations and distributions approximated using 2010 US Census data
25.0
All else
VantageScore Solutions, LLC © 2014
 11

New Scoring Distribution
Approximately 35-40* million additional consumers can be scored
12.0%
New Scoring Consumer Volumes
10.0%
• 500-580 : 21 million
• 580+ : 13 million
• 580–620 : 6 million
8.0%
45.0%
40.0%
35.0%
30.0%
6.0%
4.0%
2.0%
0.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Mainstream
Infrequent
No Trade
Mainstream PD
Rare
New Scoring PD
New Entrant
VantageScore Solutions, LLC © 2014
 12

•
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 © 2014
 13

Scoring ‘everyone else’…. leveraging alternative data
308
U.S. Population
71
10 {
No hit/No files
Illegal status
Appro x 227
180
47
Scored by conventional scoring models
Typically un-scoreable by conventional models
•
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
*
~15 million with newer models, eg. VS3.0
~55 million with conventional models
VantageScore Solutions, LLC © 2014
 14

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%
48%
41%
43%
40%
30%
20%
11%
10%
0%
0%
No-hit
Before trade added
Thin File
After trade added
Thick File
Source: Experian RentBureau ‘Credit for Renting’, 2014
VantageScore Solutions, LLC © 2014
 15

Scoring ‘everyone’…. leveraging alternative data
40%
30%
20%
10%
0%
• Substantial improvement in credit quality expanding access to credit at better terms
70%
Risk segment migration
65%
60%
53%
50%
6%
3%
Score Exclusion Subprime
Before trade added
12%
23%
Nonprime
After trade added
17%
21%
Prime
Source: Experian RentBureau ‘Credit for Renting’, 2014
VantageScore Solutions, LLC © 2014
 16

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 © 2014
 17

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 +
11.2%
Performance
Consumers with only Utility
Trades
Performance on Utility Trade Performance on Non-Utility
Trade
Consumers with Utility and Non-Utility Trades
VantageScore Solutions, LLC © 2014
 18

th

Foundations
& Nonprofits
Government &
Multilaterals
Private
Organizations
Trade
Associations
20

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
Asia
Brunei
China
Hong Kong
India
Indonesia
Japan
Malaysia
Philippines
Singapore
Sri Lanka
Thailand
Australia/Oceania
Australia
New Zealand
Europe
France
21

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

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

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

25

ALTERNATIVE DATA,
CREDIT BUILDING, AND
RESPONSIBLE LENDING
IN THE WAKE OF THE
GREAT RECESSION
June 2012
26

Remain a no score
Can now be scored
Increase >= 50
2%
2%
2%
2%
3%
3%
4%
5%
7%
11%
Increase between 25 and 49
Increase between 10 and 24
Increase less than 10
No change
Decline less than 10
Decline between 10 and 24
Decline between 25 and 49
Decline >= 50
0%
4%
6%
4%
3%
3%
3%
2%
2%
10%
19%
19%
2005 'Utility Sample'
20% 30%
2009
40%
44%
48%
50%
VantageScore Change with Alt Data, All Consumers
27

Remain a no score
4%
9%
Can now be scored
Increase >= 50
Increase between 25 and 49
Increase between 10 and 24
Increase less than 10
No change
Decline less than 10
Decline between 10 and 24
Decline between 25 and 49
Decline >= 50
0%
4%
2%
7%
5%
6%
5%
3%
1%
3%
1%
3%
1%
0%
3%
1%
4%
3%
1%
10% 20%
2005 Utility
28
30% 40% 50%
2009
60%
60%
VantageScore Change with Alt Data, Thin-file
70%
74%
80%

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

(at 3% portfolio target default rate)
30%
25%
20%
15%
10%
5%
0%
< $20K $20-$29K $30-$49 $50-$99 $100K+
2009/2010 2005/2006
30

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

(at 3% portfolio target default rate)
20%
15%
10%
5%
0%
18-25yr 26-35yr 36-45yr 46-55yr 56-65yr 66yr+
2009/2010 2005/2006
32

Helps those with damaged credit (PR & 90+ dpd)
40%
35%
30%
25%
20%
15%
10%
5%
0%
≥ 50 pt
25-49 pt 10-24 pt
Decrease
< 10 pt No Change Can Now be
Scored
Increase
Remain a
"No Score"
55.8% see score increases, 30.2% see decreases
33

Research Consensus Confirms
Benefits of Alternative Data
March 2015
34

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

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

Predicting Financial Account Delinquencies with
Utility and Telecom Payment Data
March / April 2015
37

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

Alt Data is Predictive of Mortgages
30+ DPD Delinquency Rate on Mortgage Accounts
(July 2009- July 2010)*
80%
70%
60%
50%
40%
30%
20%
10%
0%
7.50%
10.20%
Never 30+
DPD on Alt
Tradeline
No 90+ DPD ever on Alt
Tradeline
13.40%
All
59.80%
70.00%
1 90+ DPD on an Alt tradeline previous 12 months
>1 90+ DPD on Alt tradelines previous 12 months
*Only includes those with an active mortgage

Alt Data is Predictive of Clean Mortgages
30+ DPD Delinquency Rate on a previously Clean Mortgage
Accounts (July 2009-July 2010)*
30%
26.20%
25%
22.30%
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 1 90+ 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

Alt Data is Predictive of Clean Mortgages after
Accounting for Traditional Data
30+ DPD Delinquency Rate on previously Clean Mortgage Accounts
(July 2009- July 2010) by VantageScore Credit Score*
40%
36.60%
35%
30% 28.30%
27.10%
25%
20% 18.70%
16.90%
15%
10%
5%
11.30%
8.40%
0%
4.70%
1.10%
3.00%
900-990 800-899
Never 30+ DPD on Alt Data
700-799 600-699 501-599
1 90+ 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

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…

43

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

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

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

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

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

302 East Pettigrew Street
Suite 130
Durham, NC 27701 www.perc.net
(919) 338-2798 x803

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.

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

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

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

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

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