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					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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