By Sam Omukoko
Managing Director
Metropol Corporation Limited.
Copyright © 2013, Metropol Corporation Ltd
 A Survey carried out in 1999 in Kenya by:
 USAID
 GOK
 K-Rep
 Established that out of the estimated 1.3 Million Micro and
Small enterprises, less than 5 % had access to formal
finance.
 SME’s Contributed nearly 75% of the employment and
about 18.5 % to GDP.
Copyright © 2013, Metropol Corporation Ltd
• Metropol undertook a Mini survey on lenders and SME
businesses in 2003.
• In 2004, we put a proposal to Pro-Invest of the
European Union to assist us to develop a Credit scoring
tool that Lenders could use in a low data environment.
• APDF- A program of IFC also later joined on board and
provided funding as well as expertise in scoring
development.
• Metropol Worked with a team consisting of : the Kenya
Institute of Bankers; Uganda Institute of Bankers and
Tanzania Institute of bankers together with 40 banks
from Kenya , Uganda and Tanzania.
Copyright © 2013, Metropol Corporation Ltd
This is what they said…………
Lenders.
• High mortality rates;
• Most SMEs don’t survive to the third
year.
•
Lack of business data;
• Scanty and unreliable data.
• Small transaction values.
• Higher cost/income ratios.
• Diverse sources of income.
• The source of repayment may not
be the one initially appraised.
• Lack of collateral.
SME Businesses.
•
They are unable to access long term
funding.
•
•
High Cost of Capital.
•
•
Too many documents required leading to
lengthy waiting periods and stringent terms.
Lenders don’t understand their
businesses.
•
•
Interest rate charged is too high.
Stringent Appraisal Mechanisms.
•
•
Most support is short term.
Lenders emphasize too much on collateral and
never analyze their business models.
Lack of capacity.
•
SME’s lack the capacity to provide
complicated business plans and cash flow
analyses.
• No brick and Mortar support.
Copyright © 2013, Metropol Corporation Ltd
The entire project took 18 Months….. From 2004 to 2006.
Involved a lot of shuttling between Nairobi. Kampala and Dar es salaam.
•Initial attributes
•Def of SME
•Work framework
(Attributes agreed
Upon 56)
Workshop with
Bankers from
across the
region
IFC Input
• Reduced
attributes to 36
• 1,800 files from
25 banks
• Data Cleaning
• Cross
tabulation
East Africa
SME Data
Regression
Analysis
•At Bivariate Level
(17 attributes)
•Multivariate Level
(5 Predictive
attributes)
The central hypothesis of the project was that SME businesses are largely owner managed
and their success depends on his character and business acumen.
Copyright © 2013, Metropol Corporation Ltd
INTERNAL MODEL
• Bank implements the scoring
tool internally.
REACTIVE MODELSITE VERIFICATION
• SME approaches the bank
• Bank Forwards request for Investigation to Metropol.
BUREAU MODEL
PROACTIVE MODELSMEBS PROGRAM
• SME approaches the Banks.
• Bank References in the Bureau database.
• SME approaches Metropol for profiling.
• SME approaches the bank for facilities.
• Bank Pulls the SME report on file for credit evaluation.
Copyright © 2013, Metropol Corporation Ltd
Case Study 1: A MAJOR LARGE BANK CLIENT.
Performance
Defaulted
Sample size: 88 Customers.
Total Customers:504
Period : 2010- 2011
Performance
No. Businesses
in %
4
5%
Not Defaulted
84
95%
Total
88
Performance in %
Not
Defaulted
95%
Defaulted
5%
Case Study 1: Of the 4 Companies that
defaulted…
No. Defaults
Period Of default after
verification .
Company
Default Insitution
Company 1
Other Bank
1
2 years
Company 2
Other Bank
1
1 year
Company 3
Other Bank
2
2 years
Company 4
Other Bank
2
2 years
• None of them defaulted with the Bank doing the Site Verification
but defaults happened with their other lenders.
• Company 1 and 2 were listed with dormant accounts whose
defaulted amount <1,000 suggesting the proprietors may have just
assumed they finished paying off the loans.
• Company 3 and 4 had both a loan and overdraft facility with other
institutions. The loans appears to have been taken around the
same period the application to the Bank was made.
• Additionally, the defaults for 3 and 4 started 2 years after the site
verification highlighting the need for regular monitoring.
Case Study 2: A MAJOR REGIONAL HOTEL.
Ratings
Assigned
A
B
C
NB
NR
Total
Number
14
19
14
1
26
Prompt
Payment
85%
65%
75%
100%
73%
74%
Irregular
Payment
15%
35%
25%
0%
27%
26%
Default
ed
0
0
0
0
0
0
Sample size: 74
Total Customers :504
Period 2010- 2011.
Chart 2
100%
90%
80%
Prompt Payment
Irregular Payment
Defaulted
70%
60%
50%
40%
30%
20%
10%
0%
A
B
C
NB
NR
Total
Partner &Membership Program.
Copyright © 2013, Metropol Corporation Ltd
The SMEBS process is designed to accommodate the needs of
lenders offering credit to the various different sizes of small
business from entry-level, through micro and very small, up to full
SME size.
This ensures appropriate:
 SMEBS fees for the SME
 Amount of data to be collected and verified for SMEBS
 Tier of SMEBS membership based primarily on data availability
and credit risk, i.e. SMEBS score
 Costs of SMEBS processing
 Costs for inquiries to MCRB for SMEBS Reports
Copyright © 2013, Metropol Corporation Ltd
550
SMEBS Credit Score
650
750
1,000
Rich
Low
SMEBS Quantitative
data
SMEBS Qualitative data
SMEBS Site Verification data + MCRB Alternative data*
Basic KYC and MCRB SME bureau credit report inquiry data
Entry-level
Micro
Very small
Typical SME Size#
Data Composition &
SMEBS Report content
SMEBS Costs & Fees
High
Basic
Full SME
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Data is acquired by the analyst / agent using a questionnaire structured to ensure
that information is gathered incrementally as required in order to perform a
rating appropriate to each level of membership.
Bronze – SMEBS credit score range 0 to 500
1. Basic contact information [sufficient to pass KYC and to make an MCRB Report
inquiry]
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SMEBS
00010001000
SME Ltd
Silver – SMEBS credit score range 550 to 650
1. Bronze data
2. Site Verification Data. This includes All the 17 predictive factors that were established
in the Regression Analysis.
*NNN = score cut-off point(s)
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SMEBS
00010001000
SME Ltd
Gold – SMEBS credit score range 650 to 750
1. Bronze data + Silver data
2. Qualitative data
 Owner manager characteristics.
 Psychometric factors.
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Platinum – SMEBS credit score over 750
1. Bronze data + Silver data + Gold data
2. Quantitative data.
3. Statutory Compliance.
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Register
• This establishes the SME
business profile on the
Bureau.
Data
Collection
Process
• Analysts collect data from
various sources including
site visits.
Generate
• Data is keyed into
ratings
the SME profiles.
category.
TRAINING:
ISSUE OF eCertification
Card.
• Off to the Lender
Monitoring
.
• Manage the
relationship- Data
updates.
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SME’s
• To grow their business
and contribute more to
employment and
wealth creation.
Lenders
To offer SME’s credit
and other products
that are profitable to
them.
Metropol
Other Stakeholders.
To create credit
profiles and reduce
Cost/Income ratio and
Credit risk.
To help build
capacity for
increased credit flow
to the SME Sector.
Copyright © 2013, Metropol Corporation Ltd
Copyright © 2013, Metropol Corporation Ltd