Credit Scoring:

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Going Beyond the

Usual

PERC Presentation: March 12th, 2015Urban Institute—Washington, DC

Select PERC Supporters Include…Foundations& Nonprofits

Government & Multilaterals

Trade Associations

Private Organizations

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Our Footprint

AfricaCameroonKenyaSouth AfricaTanzania

North America/ CaribbeanCanadaMexicoTrinidad & TobagoUnited States of America

AsiaBruneiChinaHong KongIndiaIndonesiaJapanMalaysiaPhilippinesSingaporeSri LankaThailand

Australia/OceaniaAustraliaNew Zealand

EuropeFrance

Central/South AmericaBoliviaBrazilChileColombiaGuatemalaHonduras 3

PERC’s Alternative

DataInitiative

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

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

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PERC’s ADI ResearchSelect 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 Inclusion2012 The Credit Impacts on Low-Income Americans from

Reporting Moderately Late Payment Data

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Research has shown that using alternative data for

credit granting results in:

Increased, Safer, Sounder,

Fairer and Broader

Lending

What have we found?

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A New Pathway to Financial Inclusion:

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

June 2012

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Consistent credit score impacts over time…

VantageScore Change with Alt Data, All Consumers

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Much more ‘positive’ impact for thin-file

VantageScore Change with Alt Data, Thin-file

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VantageScore Tier Change with Alt Data

Uses the ‘ABC’ Tiers:900-990 is an A800-899 is a B700-799 is a C600-699 is a D501-599 is an FUnscoreable defined as lowest tier

More tier rises than falls

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Change in Acceptance by Household Income(at 3% portfolio target default rate)

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Score Change with Alt Data: Lowest Income

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Change in Acceptance by Age(at 3% portfolio target default rate)

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VantageScore Score Change with Alt Data,Helps those with damaged credit (PR & 90+ dpd)

55.8% see score increases, 30.2% see decreases

Research Consensus Confirms Benefits of Alternative Data

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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.(

Many Organizations Examined Alternative Data

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?

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

Predicting Financial Account Delinquencies with Utility and Telecom Payment Data

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March / April 2015

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

30+ DPD Delinquency Rate on Mortgage Accounts (July 2009- July 2010)*

Alt Data is Predictive of Mortgages

*Only includes those with an active mortgage

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

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

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 InformationThat 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…

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‘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)

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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

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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 patternso Social collateral from call log data

Smart (Philippines), M-Shwari (Kenya), Cignifi (Brazil) Mobile data in bank lending First Access (Tanzania)

Hurdles to Reporting (US)

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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?

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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.

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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.

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