Transcript 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 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
Value of Loans < 100k
Number of Loans
2003
125.7
14.1
2002
128.9
15.7
2001
126.8
10.8
1999
113.9
7.7
1997
108.2
6.7
1995
100.4
4.9
Value of Loans < 250k
Number of Loans
224
14.9
225
16.5
218.4
11.6
195
8.4
178.8
7.4
163.9
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

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Faster response times
Better risk assessment
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Small business definitionUnited States
 Annual Sales of
up to $5 million
 Credit of up to $250,000
 Types of Small Businesses:
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

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

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
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 power
 Application
 Financial
statements
 Demographic information
 Currently building pooled models in Hong Kong
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Definition of Small Business
in Latin America
There is no consistent definition between lenders in Latin America.

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
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
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Distribution by business
activity
COLOMBIA
MEXICO
7%
11%
9%
12%
3%
8%
8%
18%
30%
48%
15%
2%
4%
16%
8%
1%
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Acceptance and booked rates
90%
80%
70%
60%
50%
Acceptance Rate
Booked Rate
40%
30%
20%
10%
0%
Colombia
Mexico
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Distribution of delinquencies
3.0%
2.5%
2.0%
One Cycle
Two Cycles
Written Off
1.5%
1.0%
0.5%
0.0%
Colombia
Mexico
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Application processing time
12
10
D
A
Y
S
8
6
4
2
0
Colombia
Mexico
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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 data
High
Business
Value
Personal
Low
Small
Size of Company
Large
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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
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