I NFORMATION P OLICY I NSTITUTE

INFORMATION POLICY INSTITUTE

By Michael Turner, Ph.D.

Presentation prepared for the The Fourth Annual Consumer Credit Reporting World ConferenceBeijing, China September 28, 2004

The Economic and Social Benefits of a Full File Credit Reporting System

2

Introduction

Measuring the impact of full file credit reporting

Implications

Agenda

3

Introduction


Implications

Agenda

4

Why on earth do banks share information on debtors? Solves a host of informational problems

associated with making loans:o Adverse Selectiono Moral Hazardo Cheaper than research on individual with each loan

applicationso Reduces Informational Rents (Jappelli & Pagano)o Richer and standardized information allows lenders to

model behavior credit decision pricing

The Advantages of Full File Credit Reporting I

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Why on earth do banks share information on debtors? (con’t) Other benefits

o Reduces overall indebtednesso Facilitates non-collateralized lendingo Facilitates securitizationo Reduces cross-subsidy from the risk-averse to the risk-

loving (reduces average interest rates)

The Advantages of Full File Credit Reporting II

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What is Full File?:Black vs. White Data

Report of delinquencies and defaults

Indebtedness levels

Report of timeliness of

payments

Black Data

White Data

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How Consumers Benefit from Full-file Regime

Price reflect individual circumstances and not societal average, declining cross-subsidy from low-risk to high-risk borrowers

Better access to credit because lower information barriers increases suppliers

Quicker rewards for responsible credit behavior

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How Lenders Benefit from Full-file Regime

Better identification of the riskiness of a loan

Can loan to broader risk segments. Very low-risk consumers enter market as prices drop and loans are priced to reflect individual and not average risk.

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How Government Benefits from Full-file Regime

Better information on the state of the finance sector

More stable finance sector and reduce need to “bail out” banks

Stabilizes the allocation of capital

10

Introduction


Implications

Agenda

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Factors Contributing to Increased Credit Access

Four simultaneous and interdependent factors: 1. Laws permitting the collection and distribution of detailed personal credit data to those with a permissible purpose;

2. The development of statistical scoring techniques for predicting borrower risk;

3. The repeal of legislated interest rate ceilings which had limited the ability of creditors to price their loan products according to risk.

4. The ability to tap credit bureau data to pre-screen consumers in order to identify creditworthy individuals and target solicitations for new credit products.

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Full-file Performance: Access to Credit Cards

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Full-file Performance: Access to Credit Cards

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Full-file Performance: Credit Card Interest Rates

Share of card account balances by interest rate tier

Declining cross-subsidy from the creditworthy to the credit-risky

Declining cross-subsidy from the creditworthy to the credit-risky

0% 20% 40% 60% 80%

< 5.5 %

5.5-10.99 %

11 – 16.49 %

16.5 -17.99 %

18% and over

0% 20% 40% 60% 80%

< 5.5 %

5.5-10.99 %

11 – 16.49 %

16.5 -17.99 %

18% and over

0% 20% 40% 60% 80%

< 5.5 %

5.5-10.99 %

11 – 16.49 %

16.5 -17.99 %

18% and over

0% 20% 40% 60% 80%

< 5.5 %

5.5-10.99 %

11 – 16.49 %

16.5 -17.99 %

18% and over

0% 20% 40% 60% 80%

< 5.5 %

5.5-10.99 %

11 – 16.49 %

16.5 -17.99 %

18% and over

0% 20% 40% 60% 80%

< 5.5 %

5.5-10.99 %

11 – 16.49 %

16.5 -17.99 %

18% and over

Credit card interest rate tier

1990 2002

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Full-file Performance: Access to Home-Secured Debt

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Full-file Performance: Home Ownership

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Full-file Performance: Home Mortgage Loans

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If spreads today (2.5%) were at their early 1980s levels (3.5%), the interest rate on a 30-year fixed-rate mortgage would be about 1% (100 basis points) higher than it is today.

With a total mortgage stock of $5.4 trillion in 2001, a 1% savings in the cost of mortgage funds translates into $54 billion in annual savings to consumers.

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Before pervasive use of AUS, approving a loan application close to 3 weeks. In 2002, over 75% of all loan applications received approval in 2 to 3 minutes. (Mortech)

Lenders that integrated AUS at the POS reduced origination costs by 50%, or roughly $1,500 per loan. (Fannie Mae).

Applied to the 12.5 million sales of new and existing homes in 2002, this would produce savings of $18.75 billion.

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Full-file Performance: Prescreened Credit Offers

Prescreening accounts for over two-thirds of all new account acquisitions in the U.S.—by far the largest method with direct mail non-prescreened a distant second (18%).

Account acquisition costs in those countries that do not prescreen are roughly $15 higher per account.

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Full-file Performance: Prescreened Credit Offers

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Full-file Performance: Is Credit in the U.S. Too Easy?

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Measuring the Impact of Full-file: Scenarios

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Measuring the Impact of Full-file: Scenarios

Scenario A (13% reduction of trade lines. Purges of credit card information only. Data furnishers vary).

Scenario B (21% reduction of trade lines. Purges of revolving and non-revolving data. Large furnishers only).

Scenarios C &D restrictions of kind of data in consumer credit report.

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Analogous Restrictions

Scenarios A &B akin to full-file regimes with limited inter-sectoral data exchanges

Scenarios C & D akin to full-file regimes with varying obsolescence rates and delinquency reporting intervals

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Impact: Findings on a Commercial Model

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9 in 10 consumer credit scores change Scenario A & B all score ranges

affected Scenario C & D commercial model

seriously underestimates high risk loans. (13% subprime move up).

Predictive power of models erodes between 1% and 15%

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Between 14 and 41 million U.S. consumers who currently qualify for credit, would be denied credit.

Delinquencies increase between 10 and 70%, resulting in higher fees or interest rates on cards to offset $3 to $21 billion charge-offs. That is $40 to $270 per family on average.

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Findings on a Simulated Model (Staten 2000)

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Responses: Retooling a Commercial Model

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Predictive power is lost even when models are retooled to cope with loss of information (Kolmogorov-Smirnoff Statistics)

Assessing the Retooled Model I

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Assessing the Retooled Model II

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Backward bent to the

unmodified model

Assessing the Retooled Model III: Worsening Trade-Offs

37

Introduction


Implications

Agenda

38

Implications

Access to consumer credit relatively more restricted in negative only countries.

Impact of credit restrictions greatest upon traditionally underserved communities—young, elderly, poor, women, minorities.

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Implications

Delinquency and default rates higher in negative only countries for any given amount of consumer borrowing activity per capita.

As a result, costs increase relatively more in negative only countries as the amount of credit per capita increases.

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Implications

Consumer credit in those countries with credit bureaus that do not maintain derogotories for at least 7 years will be more expensive and less accessible.

Consumer credit more expensive and less accessible in countries with credit bureaus that fail to report delinquencies in 30 day intervals.

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Call for Future Research

Comparative analysis necessary Learn from first generation studies Second generation must distinguish

different borrowers (consumer and commercial, not just “credit”)

Second generation should examine institutional differences (concentration of financial services sector)

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