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. 20 Thank you Questions?
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