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Credit Scoring
0
Credit Scoring &
“Big Data”
PERC Presentation: Dr. Michael A.
Turner
October 26th, 2015
Credit Reporting in Asia-Pacific and
Personal Data Protection
Xi’an, Peoples Republic of China
Select PERC Supporters Include…
Foundations
& Nonprofits
Government &
Multilaterals
Trade
Associations
Private
Organizations
1
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/Oceania
Australia
New Zealand
Europe
France
Central/South
America
Bolivia
Brazil
Chile
Colombia
Guatemala
Honduras
2
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
Research Consensus Confirms
Benefits of Alternative Data
4
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
• 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
6
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
11
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 information
Noise overwhelms the signal in large data sets
Widespread overfitting of data with model
The single most
predictive variable of
credit risk is “dishwasher
ownership.”
Big Data
Entirely
Removes
The
Consumer
Consumers would be overwhelmed by use of thousands of
variables in credit granting
OECD 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
17
Big Data
Some 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
6409 Fayetteville Road
Suite 120-240
Durham, NC 27713
www.perc.net
(919) 338-2798 x803

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PERC_BIG Data CreditScoring_102015_1(2)

  • 1. Credit Scoring 0 Credit Scoring & “Big Data” PERC Presentation: Dr. Michael A. Turner October 26th, 2015 Credit Reporting in Asia-Pacific and Personal Data Protection Xi’an, Peoples Republic of China
  • 2. Select PERC Supporters Include… Foundations & Nonprofits Government & Multilaterals Trade Associations Private Organizations 1
  • 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/Oceania Australia New Zealand Europe France Central/South America Bolivia Brazil Chile Colombia Guatemala Honduras 2
  • 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. Research Consensus Confirms Benefits of Alternative Data 4 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.(
  • 6. 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
  • 7. 6 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
  • 8. 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)
  • 9. 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.”
  • 10. Big Data Meets Underwriting Updates take at least 30 days. Blind to critical differences among borrowers (deadbeats vs. victims of business cycle)
  • 11. Big Data Start Ups The future is already here in credit risk assessment
  • 12. 11
  • 13. 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”
  • 14. Big Data Leads to BIG Failures Spurious Correlations
  • 15. Big Data Explains Everything And Nothing Big Data uses more false information Noise overwhelms the signal in large data sets Widespread overfitting of data with model The single most predictive variable of credit risk is “dishwasher ownership.”
  • 16. Big Data Entirely Removes The Consumer Consumers would be overwhelmed by use of thousands of variables in credit granting OECD Fair information Practices place consumer at center of data sharing regime. Big Data would severely challenge Notice, Choice, Access, Redress
  • 17. Big Data & Data Fiefdoms Obsession with Big Data may counter-intuitively result in reduced data access
  • 18. 17 Big Data Some 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
  • 19. 6409 Fayetteville Road Suite 120-240 Durham, NC 27713 www.perc.net (919) 338-2798 x803

Editor's Notes

  1. 18