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Document Sample
Risk Rating and Credit Scoring
for SMEs
March 27, 2012
Washington | London | Amman | Johannesburg | Mexico City | Ramallah | Islamabad
Introduction
DAI is a global development consulting agency, with
40 years of experience and offices in Washington
D.C., London, Amman, Johannesburg, Mexico City,
and Ramallah
With experience in 150 countries, DAI implements
international development projects across the sectors
of Corporate Services, Economic Growth,
Environment and Energy, Governance, Health and
Security
Introduction
DAI has extensive experience in management,
economic and financial consultancy services to
business and government clients throughout the
world, focussing particularly on the area of MSME
Lending and Leasing
These projects have included aspects of credit
scoring and credit risk analysis, mutually reinforcing
business strategies, structures, products, policies,
and procedures and how to strengthen core functions,
such as lending, risk management, marketing,
internal control, and IT/MIS
DAI’s Experience in SME Credit Scoring
31 Financial
Institutions
Key Benefits of Credit Scoring
Credit Scoring provides a consistent, quantitative
estimate of borrower risk
Relative risk allows for differentiation in:
• the loan approval process
• loan conditions and pricing
• collection activities
Scoring leads to process automation (efficiency) and
improved risk measurement (quantification) and
management (consistency)
Working with Limited Data
Data availability affects how we weigh factors
Factor selection always involves expert judgment
Assuming we develop the best scorecard possible given
our data and resources constraints, all scorecards are
again “equal” in that they must be monitored,
periodically validated, and adjusted or re-developed
Scorecard Development
The greatest challenge is not statistical or
technical ("accuracy") but rather human and
organizational ("practicality")
How Scoring Works
Scoring models assume the future will be like the past
– Based on historic data when available
– Based on organizational experience in all cases
Scoring models should include the same set of key
financial and non-financial risk factors that banks
analyze subjectively
Assign points for the different risk characteristics – the
point total for any given client is its “score”
How Scores Estimate Probability of Default
Group scores into some number risk classes
Evaluate borrower performance over time
Historic performance by risk class becomes the
probability of default estimate for future periods
Example of Scoring Policy Table
Risk Class Decision Policy Interest Margin Historic PD
1 Branch Approval 1.25 0,00%
2 Branch Approval 1.75 0.08%
3 Branch Approval 2.25 1.9%
4 Branch Approval 3.00 3.6%
5 Central Review 5.00 6.8%
6 Exception Only 7.00 9.12%
7 Reject N/A 18.4%
Example SME Loan Process Without Scoring
Client Completes Application
Check Credit Report / Minimum Criteria
Visit Client’s Place of Business
Create Loan Memorandum
Present Loan to Credit Committee
30% 70%
Example SME Loan Process With Scoring
Client Completes Application
Check Credit Report / Minimum Criteria
Visit Client’s Place of Business
Scoring
10% 60%
Short-form documentation
Further Review / Documentation at Next Approval Level 30%
Example of Time and Money Savings
Reduce time documenting 70% of loans
Reduce time spent by Credit Committee reviewing
70% of loans
Systematically eliminate the riskiest 10% of applicants
Scorecard Deployment
A web-based online
“single-entry, multiple
use” application
processing system is
the most appropriate
long-term model
deployment solution.
SME Lending Scorecards Summary
1. No “right” set of factors
2. Factors should generally be those that the Bank
considers the most important when subjectively
deciding to issue or not issue a loan
3. If the factors make sense individually, then as a
group of factors, the scorecard will be able rank risk
SME Lending Scorecard Example Factors
CAPACITY CAPITAL
Total Sales Client Contribution to Financing
Loan Size as % of Sales Average Bank Account Turnover / Turnover
Total Debt/Equity from Income Statement
Current Ratio Average Bank Account Balance
Inventory Turnover
Interest Coverage
COLLATERAL
Debt Coverage Ratio
Loan To Value Ratio
CHARACTER Type of Collateral
Years with Bank Presence of Additional Guarantees
Business Credit History
Owner's Personal Credit History CONDITIONS
Years Experience in Business Sector Risk
Type of Legal Entity Key Buyer/Supplier Dependencies
How Statistical Scoring Works
For each potential factor, count the number of good and
the number of bad contracts for across each possible
“category”
Look for meaningful patterns of increasing/decreasing risk
Assign point weights equal to the concentration of bad
loans per category
Models Rank Applicants by Risk
Single Factor Cross Tabulation: Risk = “Bad Rate”
Example Variable “Requested Loan to Turnover”
Higher Ratio is Higher Risk
4,000 Loans, 200 are “Bad”
Loan Size to
Turnover # Good #Bad Total Bad Rate Points
< 10% 1,000 0 1,000 0.0% 0
11 - 25% 970 30 1,000 3.0% 3
26 - 50% 930 70 1,000 7.0% 7
>50% 900 100 1,000 10.0% 10
TOTAL 3,800 200 4,000 5.0%
Models Rank Applicants by Risk
Full Model Cross Tabulation: Risk = “Bad Rate”
More Points Indicate Higher Risk
4,000 Loans, 200 are “Bad
Score Range Goods Bads Total Bad Rate
0 - 14 200 0 200 0.0%
15 - 28 397 3 400 0.8%
29 - 42 792 8 800 1.0%
43 - 57 1,179 21 1,200 1.8%
58 - 71 745 55 800 6.9%
72 - 85 348 52 400 13.0%
86 - 100 139 61 200 30.5%
TOTAL 3,800 200 4,000 5.0%
Application Credit Scorecards and Basel 2
Basel 2 has spurred financial institutions to develop
internal ratings based models to calculate expected loss
based on predictions of its components:
Expected Loss =
Probability of default (PD) x
Loss given default (LGD) x
Exposure at Default (EAD)
The Basel 2 accord does not specify any one methodology for the
development of credit scorecard, but several overriding principles.
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Thank you
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