PERC_BIG Data CreditScoring_102015_1(2)

Credit Scoring

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Credit Scoring & “Big Data”

PERC Presentation: Dr. Michael A. TurnerOctober 26th, 2015Credit Reporting in Asia-Pacific and Personal Data ProtectionXi’an, Peoples Republic of China

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

Research Consensus Confirms Benefits of Alternative Data

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March 2015

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

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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

Just what is “Big Data” anyway?

“Even with decades of this data, what would we know?”

• More marketing term than discipline• Impetus was harnessing large volumes of data

from http servers (make sense of users of e-commerce and social media)

• Unstructured and interpreted rather than specific answers to specific questions (interpret people’s intentions by analyzing their clickstreams)

Credit Reporting is Ripe for a Disruption

Updates take at least 30 days.Blind to critical differences among borrowers (deadbeats vs. victims of business cycle)

Their data is old. Their approach archaic and shrouded in mystery. ”

“All data is credit

data.”

Big Data Meets Underwriting

Updates take at least 30 days.Blind to critical differences among borrowers (deadbeats vs. victims of business cycle)

Big Data Start Ups

The future is already here in credit risk assessment

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Big Data Caveat hic

esse draconum

While “Big Data” holds forth great promise, it carries significant near term risks:• Misplaced faith• Data Fiefdoms • Investments misallocated away from “trenches” to what’s “trendy”

Big Data Leads to BIG Failures

Spurious Correlations

Big Data Explains Everything And Nothing

Big Data uses more false informationNoise overwhelms the signal in large data setsWidespread overfitting of data with model

The single most predictive variable of credit risk is “dishwasher ownership.”

Big Data Entirely

RemovesThe

ConsumerConsumers would be overwhelmed by use of thousands of variables in credit grantingOECD Fair information Practices place consumer at center of data sharing regime. Big Data would severely challenge Notice, Choice, Access, Redress

Big Data & Data

Fiefdoms

Obsession with Big Data may counter-intuitively result in reduced data access

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Big DataSome final thoughts:

Approach with EXTREME caution Big Data is a complement to, not substitute for established

predictive data Dishwashers aren’t causally related to credit risk

Discourage Data Fiefdoms› Prohibit conflicts of interest in ownership structures› Encourage reasonable access to vital data sets

Keep Consumers at the Center Big Data removes consumer Big Data threatens privacy

Leap into the Trenches› Invest in efforts to digitize “traditional” alternative data› Encourage use of established predictive data in origination process

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