The Value of Comprehensive Credit Reports: Lessons from the U.S. Experience

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The Value of Comprehensive Credit
ReportsLessons from the U.S. Experience

• Michael E. Staten
• McDonough School of Business
• Georgetown University

The Legal and Regulatory Environment for Credit
Reporting Systems, sponsored by the World Bank
Washington, DC June 18, 2001

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Credit Products Have Achieved Deep Penetration of
U.S. Households with Relatively Low Delinquency
Rates

• Percent of Households Percent of Borrowers
• with Product 30 days Delinquent
• 1998 1st Quarter 2000

Mortgage 43.1 2.8 Closed-end inst
allment 43.7 6.2 Bank Credit Card
67.6 4.6
Source Federal Reserve Boards 1998 SCF and
Trans Union LLP.
3
Bankcard Ownership by Household Income
Percent of households with at least one
bankcard.
Source Federal Reserve Board.
4
How Did U.S. Creditors Achieve Deep Penetration
with Low Delinquency Rates?

• Legal rules which permit the collection of
  personal credit data, both positive and negative
  
• The development of statistical scoring techniques
  to predict borrower risk
  
• Risk-based pricing made possible by the repeal of
  legislated interest rate ceilings
  
• Use of credit bureau pre-screening to target
  offers of credit
  

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Simulations to Demonstrate How More Complete
Credit Histories Improve A Scoring Models
Predictive Power

• Begin with sample of U.S. credit reports
• Build scoring model to predict delinquency on new
  accounts
  
• Drop selected data fields to simulate what is
  missing from credit files in other countries
  
• Re-build a restricted model on remaining data
  to predict delinquency on new accounts
  
• Compare results

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Simulation of a Negative-Only Environment

• Full model built under U.S. reporting rules
• positive and negative data, plus inquiries
• Negative-only model built under Australian
  rules
  
• negative data, plus inquiries
• Sample of 312,000 borrowers with new accounts
  opened in May 1997
  
• Models estimate probability of 90 day
  delinquency within 24 months

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Caveats Regarding Model Construction

• Data and analytical advice provided by Experian
• Our model contained 56 of 400 Experian
  variables
  
• Variables were selected from 4 major categories
• Outstanding debt and types of credit, all
  sources
  
• Length of credit history
• Applications for new credit (Inquiries)
• Payment behavior (delinquency, chargeoff,
  bankruptcy, etc)

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Effects on Default Rates of Adopting a
Negative-only Credit Scoring Model for Various
Approval Rates
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Effects on Credit Availability of Adopting a
Negative-only Credit Scoring Model for Various
Default Rates
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Implications for a Negative-data-only Market
(relative to markets with full bureau data)

• Consumer credit will be generally less available
  (smaller percent of households owning product for
  each socioeconomic category)
  
• Smaller consumer borrowing will impair growth of
  consumer spending and durable goods industries
  
• Higher price for credit as penetration rises

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Fragmented-Reporting ScenarioBureau Data
Restricted By Type of Lender

• Typical scenario in many Latin American countries
  and Japan.
  
• Credit reporting historically driven by either
  bank or retailer alliances.
  
• Mostly negative data but some positive data are
  present in credit files.
  
• Simulations measure the impact of having data
  available for only one type of lender the
  borrowers total debt exposure is unknown
  

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Simulation to Compare Models Built under Full
Reporting vs. Retail-only

• Full model (U.S.) includes positive and
  negative data, all lenders
  
• Retail-only model includes only retailer
  account experience, positive and negative (no
  data about loans from banks)
  
• Sample of 67,000 borrowers with new retail
  accounts
  
• Model estimates probability of 90 day
  delinquency on these new retail accounts within
  24 months

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Effects on Retail Loan Default Rates of a
Retail-only Credit Scoring Model for Various
Retail Loan Approval Rates
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Effects on Credit Availability of a Retail-only
Credit Scoring Model for Various Retail Loan
Default Rates
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Conclusion Privacy Restrictions that Put Limits
on Credit Reporting Impose a High Cost

• The ability of U.S credit bureaus to report
  positive as well as negative credit data has
  meant
  
• Greater access to credit products for all
  socioeconomic groups
  
• Reduction in loan losses that would result from
  such market penetration without positive bureau
  data
  
• Creditors can monitor accounts and use scoring to
  adjust credit lines and prevent excessive debt
  buildup