Standard & Poor’s Risk Solutions Data Consortia June, 2010 Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. Copyright © 2010 Standard & Poor’s Financial Services LLC, a subsidiary of The McGraw-Hill Companies, Inc. All rights reserved. Agenda • Standard & Poor’s Risk Solutions – Introduction • Data Consortium – What is it? • Why are Consortia Needed? • Benefits of a Credit Data Consortium • What does Standard & Poor’s Provide? – Step 1: Initial diagnosis – Step 2: Implementation of the consortium – Step 3: PD data pooling, cleaning, aggregating, testing and analysis of the data – Step 4: Reporting & Deliverables – Step 5: Building models on the aggregated data • Standard & Poor’s Consortia Experience Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 2. Standard & Poor’s Risk Solutions - Introduction Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 3. Standard & Poor’s Risk Solutions - Introduction • Standard & Poor's Risk Solutions provides financial analysis and risk management solutions to assist credit sensitive institutions make informed decisions regarding originating, measuring and managing credit risk arising from their day-to-day business activities • We address all major components of financial analysis, including data, methodologies and processes for the analysis of probability of default, loss given default and exposure at default • These integrated credit risk management solutions leverage Standard & Poor's experience in credit assessment to help institutions manage credit risk, calculate economic and regulatory capital, and manage their balance sheets more effectively Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 4. Standard & Poor’s Risk Solutions - Introduction • Core Competencies – Internal Rating Systems Internal rating systems design, assessment and improvement Obligor and facility ratings Validation – Models. Off-the-shelf and custom models to measure PD, LGD or to estimate credit ratings – Data. Globally we facilitate or run a significant number of data collection exercises – PD & LGD. PD & LGD data collection, analysis and modeling. S&P Risk Solutions is a leader in this field Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 5. S&P Risk Solutions – corporate structure • Confidential information is “firewalled” between Risk Solutions and the Rating Services of Standard & Poor’s. Risk Solutions is a “nonratings” business of Standard & Poor’s Standard & Poor's Fixed Income & Risk Management Services Leveraged Commentary & Data Risk Solutions Rating Services Structured Finance Ratings Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 6. Corp. & Govt. Ratings Data Consortium – What is it? Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 7. Data Consortium – What is it? • A Data Consortium is a group of banks that agree to pool data, usually on a confidential basis, to a central repository, whereupon data cleansing, aggregation and analysis takes place • The data will typically relate to one or more homogeneous asset class and may be examining default or both default and recovery, or just recovery • Standard & Poor’s preserves the confidentiality of both the bank’s clients and the performance of the individual bank’s portfolio • Reporting outputs by Standard & Poor’s are agreed collectively with the participating banks • Standard & Poor’s could develop PD & LGD Models from the aggregated data Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 8. Why are Consortia needed? Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 9. Why are Consortia needed? • Individual banks’ default and loss experience is relatively sparse within specific asset, industry and collateral sub-groups – often relatively few defaults a year – resolution of final losses can take considerable time – scarcity drives compromise; one must balance statistical significance against granularity of estimates produced • Need bigger, deeper data set to provide more statistically robust information quicker – to achieve objective of estimating PD and LGD as accurately as possible – difficult for banks to address individually – it may be that the whole market does not have statistically robust data for certain asset classes, but this should be demonstrated Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 10. Why are Consortia needed? • Importance of robust Probability of Default (PD) and Loss Given Default (LGD) benchmarks – Pressure for change in approach to credit risk measurement Risk based pricing and economic capital allocation require the separate consideration of PD & LGD Basel II Internal Ratings Based Approaches (Foundation and Advanced) – Both are important in determining expected loss and unexpected loss For level of capital – capital is a buffer against uncertain outcome For capital allocation – risk-based pricing & performance management For credit risk management processes Multi-dimensional ratings Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 11. PD and LGD meeting banking needs Business Development Loan Origination Credit Approval Portfolio Management • Database on clients and prospects • Benchmark comparison • Model • Pro forma pricing • Loan/Credit MIS (Mgt info System) • Stress Test • Formal assessment of pricing • Financial Statement Spreading • Stress Test (Company and industry) • Pricing assessment • Is credit rated properly? • Stress Test • Portfolio analysis • Risk Mitigation • Expected loss Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 12. Treasury/ CFO/CEO • Economic Capital • Securitisation • Regulatory Capital management • RAROC • Unexpected loss Benefits of a Credit Data Consortium Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 13. Benefits of a Credit Data Consortium • With Basel II, Banks have to move away from the traditional assessment of lending on an “Expected Loss” basis and separate it into the probability of default (PD) and the loss given default (LGD). The data collected in pooling exercises greatly facilitates this exercise, both by providing more robust statistics and, in certain instances, by enabling the construction of quantitative models • All banks will benefit by the more rapid aggregation of data and the building of a robust set of normalized statistics. In fairly short order the banks will receive their own conformed default experience compared with the industry as a whole, together with some key financial statement benchmarks • Stakeholders – Banks (large & small) – Regulator – Data Agent & Supplier of Services Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 14. Benefits of a Credit Data Consortium • For the larger banks: – Those aspiring to Advanced IRB status can build up more observations on recovery more quickly. LGD has to be captured over a period of time, often considerable, whereas default is a binomial, instantaneous event – The consortium can decide to exchange data with a consortium in another country, which would prove useful should the bank be in that market or considering entry – Although a bank may be large, smaller banks often have interesting regional or industry-specific data, so that their data, whilst not so numerous, may still add value to the larger bank – Large banks, when using the benchmark data to present comparative analysis to external parties, such as regulators or rating agencies, can refute suggestions of “cherry picking” if they include all the banks – The banks receive expert advice on how to compile an appropriate database of its own credit experience Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 15. Benefits of a Credit Data Consortium • For the smaller banks: – Access to countrywide experience – A benchmarking portfolio that replicates the market – Insight on the experience in particular industrial sectors, in which it is not presently participating, thus informing expansion decisions – Some of the “large” bank benefits apply – for instance, a “small” bank in the corporate market may be a large retail lender that would benefit from attaining the Advanced IRB standard Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 16. Benefits of a Credit Data Consortium • For the larger and smaller banks: – Top management has benchmarks against which to assess the performance of their own bank – The business development area has benchmark comparisons on lending decisions and pricing – Credit Risk departments can benchmark their internal credit ratings – Guidance for stress-testing and scenario analysis – An informed strategy and risk appetite, from industry and regional analysis – More accurate pricing and analytical assumptions for CDOs. – The underpinning by facts of assumptions for RAROC models Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 17. Benefits of a Credit Data Consortium • For the Regulator: – A reliable historical benchmark against which the performance of each bank can be measured using conformed data. Interpretation of the results is still essential – a higher default rate may be indicative of a greater risk appetite in that bank and supported by higher margins – The bigger, deeper data set should lead to an improvement in the quality of risk management throughout the industry – Successful implementation of the consortium would cement a reputation as a forward-looking regulator. For instance, Saudi Arabia has led the way and other regulators are contemplating consortia Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 18. Benefits of a Credit Data Consortium • Benefits of (i.e. data driven) quantitative Models: – A robust benchmark for a bank’s own IRB internal rating system Or, an input to a bank’s own IRB with the bank’s expert judgment overlay – Leverage of S&P’s expertise, with the overhead effectively spread over the members of the consortium – An effective tool for the analysis of structured transactions – A quick and effective input to pricing and economic capital allocation models – A tool for rapid assessment of potential new business, marketing approaches, etc. Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 19. Data Ownership • Ownership of the data remains with the banks throughout • We are highly experienced in maintaining the confidentiality of information – it is core to many facets of our business • All distribution of conformed statistics back to banks does not reference individual customers and is sufficiently aggregated to disguise the portfolio of individual banks • We could build models trained on the aggregated data, but it does not distribute the data in any manner – Numerical identifiers can be substituted Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 20. What does Standard & Poor’s Provide? Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 21. What does Standard & Poor’s provide? • Step 1: Initial Diagnosis of potential data availability – Detailed Structured Questionnaire – Management Interviews – Security Requirements – Questions & Answers for consortium members • Step 2: Implementation of the Consortium – Agreement on the consortia structure and terms of reference – Agreement on the deliverables • Step 3: Pooling, cleaning, aggregating, testing and validation of the data • Step 4: Delivering the data reports • Step 5: Building models on the aggregated data Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 22. What does Standard & Poor’s provide? Step 1: Initial diagnosis of potential data availability Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 23. What does Standard & Poor’s provide? • Step 1: Initial diagnosis of potential data availability – For each Member Bank review the existing data and workflows and so determine: Definitions and standards of default, emergence, and recovery Volume and historical timeframe of existing datasets Format and structure of non-electronic documentation Data storage format – in databases, desktop PC’s, paper files Data storage location geographically Early view of portfolio (to help develop segmentation) Workflows for existing loans, distressed and defaulting credits Structure of datasets versus an “ideal” dataset The IT environment of the bank – Leading to an efficient and effective implementation of the consortium Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 24. What does Standard & Poor’s provide? Step 2: Implementation of the consortium Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 25. What does Standard & Poor’s provide? - Governance • Step 2: Implementation of the consortium – It is important to establish the “rules of the game” at the outset – There are a number of feasible structures – We favour an appropriately resourced two-committee structure A Management Committee to take policy decisions, inevitably all events cannot be predicted at the outset A Methodological Committee dealing with technical issues in more detail – Standard & Poor’s can assist in drawing up Terms of Reference for the Committees Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 26. What does Standard & Poor’s provide? - Consortium Organization Management Committee S&P Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 27. Methodology Committee S&P What does Standard & Poor’s provide? - Consortium Organization • Management Committee decisions – acceptance of new members – communicating with banks not in compliance – sharing some statistics with other consortia • Methodology Committee – minimum standards (“must have” data fields & quantity) – model drivers discussion with Standard & Poor’s experts – Standard & Poor’s contributes knowledge and experience Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 28. Probability of Default (PD) Data Consortium Basics • For each bank in the consortium S&P links the history of Borrower’s Credit performance and Other Borrower Data (qualitative) to the history of that borrower’s financial performance • The aggregate set allows predictive modeling of credit performance based on time series of financial accounts • Approach effective for middle-market and corporates where financial performance determines credit performance and a statistically large number of cases can be collected Borrower Credit Performance Histories and Other Borrower Information Borrower Financial Performance Histories Link Industry Geography Company Type Asset Class Instrument Payment Delinquencies Write-offs Financial Statement Accounts Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 29. What does Standard & Poor’s provide? Step 3: PD data pooling, cleaning, aggregating, testing and analysis of the data Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 30. What does Standard & Poor’s provide? • Step 3: PD data pooling, cleaning, aggregating, testing and analysis of the data – Objective - aggregate a robust PD dataset for quantitative modeling and statistical benchmarking – Collect a sufficient number of observations (both defaulters and performing companies) – Best practices PD data set – combination of borrowers’ credit histories and their financial histories – Rely on objective data elements (financials, balances, days past due, etc.) – Aggregate a chronologically “deep” data set - covering one economic cycle – Quality of data: ensure that all aspects of consortium data are a close representation of the credit reality in the marketplace Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 31. Middle Market PD Data for Model Development – Data Quantity • Corporate/SME modelling • To develop a powerful model, a data set of 400 to a500 defaulted entities (entire consortium) • Most effective way to achieve consortium goals – historical PD data submission (3-4 years) + data going forward, and LGD collection (a ”goforward approach”) Borrower Count Cumulativ e Distribution for Pe rforming and De faulte d Borrowe rs 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 Performing Defaults 1 2 3 4 5 6 7 Year Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 32. 8 PD Data Process Flow Mapping Data Validation Routines Matching, Linking Extracts, Treating Duplicates, i.e. Data Standardization Develop a “System” Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 33. Data Consolidation Reporting PD Data Structure Loan Accounting System Borrower 1 Statement FYE 1 Statement FYE 2 Statement FYE 3 Financial Statements from Spreading System Borrower 1 Borrower 2 Borrower 3 Borrower 2 Statement FYE 1 Statement FYE 2 Statement FYE 3 Portfolio Default Report Industry Geography Company Type Asset Class Instrument Payment Delinquencies Borrower 3 Statement FYE 1 Statement FYE 2 Statement FYE 3 Balance Sheet Items Income Statement Items Statement Period (Year) Audit Quality Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 34. Counts of Defaulters vs. all companies in portfolio Bank’s Historical Financial Statements - Scenario 1 Borrower Financial Statements Bank-analysts have already input over the years Statements already in database format Database Many 1000s of Statements Project Action: Data is extracted for matching and clean-up Name Matching Statements Table (“unrefined” data) Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 35. Loan Accounting System Bank’s Historical Financial Statements - Scenario 2 Borrower Financial Statements Bank-analysts have already input over the years Extracts containing multiple electronic borrower files Data Aggregation Project Action: Data is extracted from many hard-drives and aggregated Name Matching Statements Table (“unrefined” data) Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 36. Loan Accounting System Data Clean-up Tools Example Name-matching Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 37. Data Standardization – Chart of Accounts Mapping Bank-specific Chart of Accounts Total Assets Total Assets Trade Receivabes Accounts Receivable Subsidiary Receivables Other Receivables Turnover Consulting Income Revenues Standard Chart of Accounts Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 38. Total Net Worth Total Equity Proposed Data Validation Process Data Quality Assessment Stage 2 Borrower Matching And Removal Of Duplicates Data Quality Assessment Stage 3 Portfolio Level Data Analysis Methodology Committee Provides Guidance Data Quality Assessment Stage 1 Automated Data Integrity Checks Management Committee Provides Feedback And Directs Action Data Quality Workshops Are Held At the Beginning Of Every New Collection Cycle Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 39. Management Committee Data Quality Report and Review Data Validation Process – Automated Data Checks Rule No. Data Secti on Data Table Rule Name Rule Ensures Financial statement date must be provided. 11 PD FSData FS Date check (blank) 17 PD FSData Audit Qual check (blank) Audit Quality must be provided. 19 PD FSData Curr check Currency must be provided. State or Province Code is provided. 5 PD FSCompany State/Province Code check 6 PD FSCompany Country Code check Country Code is not null or invalid. 76 LGD LASBorrower Pub/Priv check Public/Private Indicator must be provided. 77 LGD LASBorrower 78 LGD LASData 79 LGD LASData Hold/Oper check Loan ID.01 check Loan ID.02 check LGDBorrower Borrower Linking Check BorrowerID must be the same and exist in all tables Collateral ID must link to a LoanID or BorrowerID 13 LGD Holding/Operating Indicator must be provided. Loan ID or Facility ID must be provided. Loan ID or Facility ID must be unique for each loan/facility. 14 LGD LGDCollateral Collateral Linking Check 91 LGD LASData Orig Dt.05 check Origination Date < Default Date Origination Date < Resolution Date 92 LGD LASData Orig Dt.06 check 93 LGD LASData Orig Dt.07 check Origination Date < Last Date Cash Paid 208 LGD Recoveries Recov. Cash Balance Balance-at-Default - sum( Principle recovery cashflows) >= 0 (10% exc.) 212 PD FSData Company Size Check Total Assets < 1% of country GDP Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 40. Mandatory Elements Checks Relational Rules Verification Logical Tests Data Validation Process – Automated Data Checks Rule Data No. Section Data Table Rule Name Rule Description 26 PD FSData Ttl Curr Asst.02 check Total Current Assets > 0 28 32 PD PD FSData FSData Ttl NonCurr Asst.02 check Ttl Asst.01 check 33 PD FSData Ttl Asst.02 check 35 41 51 52 PD PD PD PD FSData FSData FSData FSData Ttl Current Liab.01 check Ttl LTD Ttl COGS check GrossPrft.01 check 63 7 PD PD FSData FSCompany 8 80 315 PD LGD LGD FSCompany LASData LoanData 18 PD FSData Total NonCurrent Asset > 0 Total Assets > 0 Total Assets = Total Liabilities + Total Net Worth (+/- 2) Current Liabilities sub-items balance with Total Current Liabilities Total Long Term Debt > 0 Total COGS > 0 Operating Profit > 0 Net Sale <> 0, Total Operating Profit <> 0, Net NI.01 check Income <> 0 PostalCode check Postal Code is not null or invalid. Industry Code is not null, invalid or does not Industry Code check correspond to Industry Classification. As Of Dt.01 check As Of Date must be a valid date. LnTypeCheck Loan type code is not null or invalid. Financial statements where audit quality is not null, 10-Q, projection, proforma, interim. Audited, Qualified, Management prepared statements are AuditQualPrioritization prioritized. Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 41. Financial Statement Validity Rules Qualitative Data Validity Rules Prioritization Rules LGD/Recovery Data – Credit Events and Time-points of Interest Borrower Characteristics Instrument Information Security Details Guarantor Description approx. 1 ~ 5 years O O: D – 1: D: R: CF: D–1 D 1st CF 2nd CF Origination One-Year Prior to Default Default Resolution Cash Flow Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 42. Nth CF R LGD Data Structure • Basel II requires LGD estimates at the facility level. So LGD data has to be collected on the borrower, loan and credit mitigation/cashflow level Borrower ABC Loan 1 Collateral Cash Recovered Loan 2 Guarantor Collateral Cash Recovered Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 43. Guarantor LGD Data Process Flow KEY ACTIVITIES 95% of value added Mapping Collateral Records Input of 30 Resolved Defaulters Per Year Into “Rec. System” Resources Data Team: S&P Loss Data System + Bank’s Analyst + S&P Credit Data Expert Data Data Validation Routines Standardi zation Automated Processes Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 44. Data Aggrega tion Reporti ng S&P Consortium analysts What does Standard & Poor’s provide? Step 4: Reporting & Deliverables Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 45. PD Data Quality Benchmarks and Bank Ranking Reports • Absolute Score Default Rate Identification Accuracy 100% Develop a confidence interval regarding model accuracy based on data quality 80% 60% 40% Portfolio Distribution 20% • Relative (bankspecific) Score Quantify bank-specific data quality, and at the same time compare that to consortium benchmark Historical Coverage 0% Benchmark Bank1 Business Rules Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 46. Data Completeness (minimum quality standard) PD Data Quality Benchmarks and Bank Ranking Reports Data submission comparison on all aspects of quality – PD data DefaultRate Default Rate Subm. Default Distribution Acct Sys Default Distribution Total Wtd 60% 20% 20% 100% ABC 93% 73% 60% 45.0% BCD 0% 29% 0% 5.9% EFG 69% 67% 19% 31.0% GFH 0% 80% 0% 16.0% HDD 93% 77% 66% 77.8% BOS 87% 1% 13% 20.3% CID 61% 81% 29% 66.3% CCM 0% 49% 0% 9.8% XYZ 75% 43% 59% 64.8% ABD 91% 72% 60% 81.4% HIB 0% 0% 0% 0.0% Audit Quality Audited Statements Wtd 100% ABC 28.1% BCD 51.0% EFG 40.7% GFH 17.0% HDD 41.7% BOS 22.2% CID 49.8% CCM 61.1% XYZ 29.4% ABD 29.2% HIB 59.2% Distribution Size Distribution Industry Distribution Total Wtd 70% 30% 100% ABC 53.2% 56.7% 54.3% BCD 59.2% 70.6% 62.6% EFG 62.9% 77.3% 67.2% GFH 65.2% 75.6% 68.3% HDD 65.8% 77.3% 69.3% BOS 67.1% 70.6% 68.2% CID 53.6% 81.4% 61.9% CCM 49.9% 60.1% 53.0% XYZ 65.5% 71.9% 67.4% ABD 63.7% 74.5% 66.9% HIB 52.8% 75.0% 59.4% Data Check Customer Information Financial Statment Accounting System Total Wtd 25% 40% 35% 100% ABC 90.2% 96.3% 99.9% 96.0% BCD 80.0% 96.3% 0.0% 58.5% EFG 91.2% 96.5% 45.6% 77.4% GFH 96.6% 96.5% 74.2% 88.7% HDD 87.5% 88.6% 69.6% 81.7% BOS 98.4% 96.4% 67.0% 86.6% CID 85.8% 89.7% 23.0% 65.4% CCM 90.1% 97.0% 82.0% 90.0% XYZ 98.6% 75.9% 60.3% 76.1% ABD 95.6% 94.0% 95.4% 94.9% HIB 93.3% 96.9% 0.0% 62.1% Business Rules Quality Rate Outlier Rate Total Wtd 50% 50% 100% ABC 100.0% 85.3% 92.7% BCD 100.0% 85.5% 92.7% EFG 100.0% 87.2% 93.6% GFH 100.0% 87.9% 93.9% HDD 100.0% 88.7% 94.3% BOS 100.0% 86.6% 93.3% CID 100.0% 91.0% 95.5% CCM 100.0% 94.5% 97.3% XYZ 100.0% 89.7% 94.8% ABD 100.0% 88.5% 94.3% HIB 100.0% 90.7% 95.4% Historical Reporting Year Distribution Nb of Stmts per Cust > 5 Current Rate Diff < 15 Month Total Wtd 30% 30% 15% 25% 100% ABC 82.5% 55.3% 0.0% 64.3% 57.4% BCD 69.4% 5.9% 0.0% 0.0% 22.6% EFG 76.1% 73.4% 0.0% 25.0% 51.1% GFH 82.0% 68.1% 0.0% 70.4% 62.6% HDD 86.3% 77.2% 53.3% 89.4% 79.4% BOS 88.0% 67.7% 0.0% 23.8% 52.7% CID 83.9% 67.8% 37.5% 75.7% 70.0% CCM 85.5% 52.4% 0.0% 58.4% 56.0% XYZ 83.5% 71.1% 77.2% 86.0% 79.5% ABD 86.9% 74.8% 61.8% 89.0% 80.0% HIB 84.8% 75.9% 0.0% 0.0% 48.2% Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 47. PD Data Quality Benchmarks and Bank Ranking Reports • Example: Number of historical financial statements per borrower as submitted by the banks 80% 70% 60% 50% 40% 30% 20% 10% 0% 1 2 3 4 5 6 7 Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 48. 8 9 10 PD Benchmark Reporting Deliverables • Database containing aggregate, anonymized consortium data • Electronic Reports • Reports will contain: – ratio analyses, averages, medians, quartiles for different regions and industry sectors and size – probability of default averages, medians, quartiles by industry sector, region and size – statistics comparing financial performance of defaulters vs. nondefaulters – correlation analyses – mostly industry based Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 49. PD Reporting Examples Financial Statement Ratio Analysis Industry Region Chemical Production West Saudi Arabia Working Capital Ratio Liquidity Quick Ratio Cash Ratio Receivables Turnover Asset Turnover Inventory Turnover Debt Ratio Leverage Debt-To-Equity Ratio Interest Coverage Return on Assets Profitability Return on Equity Gross Profit Margin 25th % 0.22 0.15 0.03 0.35 0.84 0.53 0.89 0.20 -0.25 -0.21 -0.33 Defaults 50th % 2.15 0.37 0.09 1.22 2.93 0.81 1.01 0.79 -0.02 0.35 -0.25 75th % 3.37 1.20 0.11 1.65 3.96 2.40 1.58 1.37 1.70 2.60 0.61 Average 1.66 0.97 0.06 0.89 2.14 1.33 0.98 0.66 1.12 0.45 0.22 25th % 0.40 0.27 0.05 0.44 1.05 0.31 0.51 0.12 0.11 0.05 0.12 Non-Defaults 50th % 75th % 3.87 6.07 0.67 2.16 0.16 0.20 1.53 2.06 3.66 4.95 0.47 1.39 0.59 0.92 0.46 0.79 0.21 0.34 0.20 1.51 0.25 0.35 Industry Default Correlations Media & Telecom Media & Telecom Oil & Gas Power Metals & Mining Oil & Gas Power Metals & Mining 0.266 0.675 0.433 0.266 0.466 -0.256 0.675 0.466 0.24051473 0.433 -0.256 0.241 * Correlation coefficient varies between plus 1 (perfect positive correlation) and negative 1 (perfect negative correlation). A correlation of 0 indicates no relationship between the time-series being correlated. Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 50. Average 2.99 1.75 0.11 1.11 2.67 0.77 0.57 0.38 0.22 0.26 0.13 LGD Analytics and Reporting Typical Recovery Distribution 25th percentile Average 75th percentile Median % of Resolved Instruments 21 18 15 12 9 6 3 18 0 62 55 20 40 60 Recovery Rate (%) Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 51. 90 80 100 LGD Reports • Database containing aggregate, anonymized consortium data • Electronic Reports • Reports will contain: – recovery/LGD medians, quartiles for different regions and industry sectors and size – recovery medians, quartiles by industry sector, region and size – EAD and utilization statistics – correlation analyses – default rate in relation to recoveries – time to default and time to resolution statistics Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 52. What does Standard & Poor’s provide? Step 5: Building models on the aggregated data Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 53. What does Standard & Poor’s provides? • Step 5: Building models on the aggregated data – Combining Standard & Poor’s credit analytics and quantitative expertise we build PD and LGD Solutions based on state of the art statistical analysis The data collected in pooling exercises greatly facilitates this exercise, both by providing robust statistics and, enabling the constriction of quantitative models. All banks will benefit by more rapid aggregation of default & recovery data and the building of a robust set of normalized statistics Which significantly enhance credit quality assessments with assist in pricing decisions for loans and debt securitisations and aid in the more precise allocation of capital for lenders Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 54. Standard & Poor’s Consortia Experience Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 55. Standard & Poor’s Consortia Experience • Standard & Poor’s Risk Solutions has developed and manages numerous data consortia for banks globally. They include the: – Credit Data Consortia in Kingdom of Saudi Arabia – Global Project Finance PD and LGD (Default & Recovery) data consortium – European Leverage Loan PD and LGD consortium – Greek data and modelling consortium – Europe Small & Medium Enterprise (SME) Study – CreditPro® and LossStats® data base for the observed default rates and rating transitions for S&P’s corporate, structured and sovereign ratings Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 56. Standard & Poor’s Consortia Experience - Credit Data Consortia • Kingdom of Saudi Arabia Credit Data Consortia – Ongoing consortium established in 2008, 12 current members Initially S&P RS performed a Credit Data Pooling Assessment Project for 11 banks in the Middle East in 2007 – Presently, SIMAH, Saudi Credit Bureau is the client of S&P Risk Solutions – Goal of consortium is to collect default and recovery data for large corporate and mid-market loans – Train PD model on data – Latest benchmark report issued in January 2010 – Consortium meets on a regular basis to discuss results, methodology and ongoing goals – Last general meeting held in January 2010 in Riyadh Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 57. Standard & Poor’s Consortia Experience – Project Finance • Standard & Poor’s has substantial experience in managing data consortia that bring significant value to their members on an ongoing basis – Global Project Finance Consortium (Default & Recovery) Ongoing consortium established in 2001, 26 members currently (4 at the start) Initial goal of consortium was to obtain lower capital allocation rates for project finance assets under Basel II Members submit project finance performance data annually with S&P assistance ─ Each member receives 2 annual studies: General study that includes benchmarks based on the data aggregated from all members Confidential study which compares and benchmarks the member’s data and performance against the pool of data aggregated from all members • The studies produced under this consortium have resulted in lowering Basel II capital allocation for Project Finance asset class ─ Consortium meets on a regular basis to discuss results, methodology and ongoing goals Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 58. Standard & Poor’s Consortia Experience – Leverage Finance • European Leveraged Loan Consortium – Ongoing consortium established in 2004, 10 current members – Consortium established to provide empirical data for CDO pricing models and to validate recovery ratings – Members submit leveraged loan performance data annually with S&P assistance ─ Each member receives 2 annual studies: General study that includes benchmarks based on the data aggregated from all members Confidential study which compares and benchmarks the member’s data and performance against the pool of data aggregated from all members ─ Consortium members meet on a regular basis to discuss results, methodology and ongoing goals ─ Next annual study to be released in November 2010 Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 59. Standard & Poor’s Consortia Experience – Modeling data consortium • Greece Small & Medium Enterprise (SME) Consortium – Ongoing consortium established in 2005, 4 current members – Consortium established to collect default data and develop a Probability of Default Model for Greek SME’s – Members submit data annually with S&P assistance – S&P-developed PD model (Credit Risk Tracker Greece) released in April 2007 • Europe Small & Medium Enterprise (SME) Study – One-time consortium effort during 2002-2004 with 10 participating institutions – Goal was to analyze the impact that differing creditor rights in France, Germany & UK have on recovery – S&P assisted each institution to collect and submit the data – S&P produced a report based on data submitted – Academic paper on the results of this study published in the “Journal of Finance” in 2007 by Professor Franks of the London Business School Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 60. Standard & Poor’s Consortia Experience • Standard & Poor’s success in managing various data consortia, which continue to bring significant value to their members, is a direct result of our capabilities and our approach: – A consortium management philosophy ensures that members play a significant role, and the consortium is focused on meeting the needs of its members – High level of hands-on assistance and customer service throughout At the start of each consortium effort, Standard & Poor’s personnel visit each consortium member to assist and train member staff for the data collection effort. The assistance also includes the development of automated data interfaces where applicable to reduce the effort required for data collection in each bank On an ongoing basis, while we provide automated data collection tools, Standard & Poor’s also provides a high level of assistance to each member during data collection ensuring that any issues are addressed and overcome promptly. This includes trouble shooting, refresher training sessions and modifications needed due to system changes in the bank Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 61. Credit Data Strategy & Operations Group and Expertise • Resources and staffing – 93 credit data experts – over 20 dedicated IT professionals • Reach across the globe – global platform with offices in New York, London, Mumbai, Taipei – local resources, data collection assistance and data experts are across offices – multiple languages spoken (A to Z) • Set-up to protect confidentiality Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 62. spread Contacts Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 63. Contacts Bayan Uralbayeva Relationship Manager, EECCA +44 (0) 207 1763919 bayan_uralbayeva@standardandpoors.com Michael Baker Director, Head of Analytical Services +44 (0) 207 176 3610 michael_baker@standardandpoors.com Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 64. www.standardandpoors.com Note: Business Unit Legal Disclaimers differ by Business Unit. Please contact Brand Management to obtain the appropriate disclaimer for your business/product/use. Every PowerPoint presentation MUST contain a legal disclaimer. Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s. 65.