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Small Business Credit Scoring

An Empirical Analysis of the Viability of Pooled Data SME

Scoring Models in Latin America

Presented at the Conference on Small and Medium Enterprises, Washington, D.C., Oct. 15, 2004

Margaret Miller

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Project Objective

To determine if pooled data SME credit scoring tools, which have proved very successful in the U.S. and in a few other select markets, could be developed for large emerging markets in Latin America

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Relevant Literature on Credit Reporting

Credit reporting can facilitate access to credit Positive relationship between private credit /

GDP and credit reporting (Doing Business) Credit reporting reduces credit constraints on

firms (Galindo & Miller 2001, Love & Mylenko 2003)

Credit reporting reduces the impact of bank concentration on access to finance (Beck, Demirguc-Kunt & Maksimovic 2004)

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Relationship Lending in the Small Business Credit Market

Old paradigm – SME loan market focuses on relationship lending (Petersen & Rajan 1994, Berger & Udell 1995, Miller 1995)

New paradigm – SME loan market segmented with local lenders employing relationship lending technologies and national lenders using automated scoring (Petersen & Rajan 2002, Dell’Ariccia & Marquez 2003, Hauswald & Marquez 2002, Brevoort & Hannan 2004)

Newest paradigm? Many lenders using both technologies?

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Literature on Small Business Credit Scoring (SBCS)

Large banks in the U.S. were more likely to adopt SBCS first (Akhavein, Frame & White 2001)

U.S. banks that adopted SBCS increased their SME lending by 8.4% on average – about $4 billion in increased lending per institution (Frame, Srinivasan & Woosley 2001)

Increased lending volumes from SBCS served to increase access to marginal or riskier borrowers (Berger, Frame & Miller 2002)

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Significant Increase in Number of SME Loans Extended in U.S. Since Introduction of SBCS

U.S. Commercial Lending, Dollars in billions, numbers in millions

2003 2002 2001 1999 1997 1995Value of Loans < 100k 125.7 128.9 126.8 113.9 108.2 100.4Number of Loans 14.1 15.7 10.8 7.7 6.7 4.9

Value of Loans < 250k 224 225 218.4 195 178.8 163.9Number of Loans 14.9 16.5 11.6 8.4 7.4 5.4

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Small Business Scoring Still Limited in Developing Countries

Only largest financial institutions have adopted SBCS Have funds to invest in expert or custom models View SBCS and their SME portfolio data as key

elements of their competitive edge Lenders are reluctant to share data, especially

on the SME market segment; not used to working in common for a pooled model

Difficult for external technology providers to create consortiums

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Deficiencies in the small business lending

Identify improvements in the banking and lending industry to allow small businesses growth and development Manage overall portfolio risk Streamline operations for increased cost efficiencies Increase number of profitable relationships Identify predictive data elements to analyze the credit risk

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How scoring technology can help the lenders?

Reduces cost by increasing efficiency and speed Make consistent ranking of risk and objective

decisions Accurate risk prediction

Majority of risk identified at origination!

Competitive edge Faster response times Better risk assessment

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Small business definition- United States

Annual Sales of up to $5 million Credit of up to $250,000 Types of Small Businesses:

Sole Proprietors Partnerships Corporations

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Small Business Scoring Service (SBSS) in the United States

Fair Isaac partners with Robert Morris Associates (RMA) 17 banks each contribute 100 goods, 100 bads, 100

declines Precoding sheets filled out by hand Paper credit bureau reports

Fair Isaac data entry staff creates electronic database 1995 - Fair Isaac SBSS releases the first empirically

derived commercial scorecards

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Small Business Pooled Models Improvements

Over 250,000 small businesses contributed by 25 banks Data extracted electronically from application processing and

master billing file systems 100% availability of consumer bureau information

Business data provides increase in predictive power Application, Business reports, Financial statements

2001 - Fair Isaac releases second generation of commercial scorecards

Currently building the New Small Business Pooled Models

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Small business scoring pooled models in Asia

Pooled Models in Japan Over 3,500 small businesses contributed by 13 banks Data transferred from application processing and master

billing file systems and availability of consumer bureau information

Business data provides increase in predictive powerApplicationFinancial statementsDemographic information

Currently building pooled models in Hong Kong

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Definition of Small Business in Latin America

COLOMBIA Number of Employees up to

200 Annual Sales of up to $1

million Credit of up to $200,000 Types of Small Businesses:

Sole Proprietors Partnerships Corporations

MEXICO Number of Employees up to

100 Annual Sales of up to

$500,000 Credit of up to $200,000 Types of Small Businesses:

Sole Proprietors Partnerships Corporations

There is no consistent definition between lenders in Latin America.

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Distribution by business activity

30%

16%

7%

8%

11%

8%

2%

18%

COLOMBIA MEXICO

48%

9%

4%

15%

1%

12%

3%

8%

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Acceptance and booked rates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Colombia Mexico

Acceptance Rate

Booked Rate

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Distribution of delinquencies

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

Colombia Mexico

One Cycle

Two Cycles

Written Off

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Application processing time

0

2

4

6

8

10

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Colombia Mexico

DAYS

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Pooled data development sampling

A random representative portion of accounts with known payment behavior plus declined accounts

Data provided by all institutions participating in the pool

Predictive information and performance information

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Data sources - Personal information about principals

Application Financial information Consumer credit reports

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Value of dataHighHigh

BusinessBusinessPersonalPersonal

Size of CompanySize of Company

LowLow

Small Large

Valu

eVa

lue

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Report for participants

Lending practices List of valuable variable that could be use for

future model development or strategies Summary of individual portfolio performance Validation of questionnaire responses