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June, 2010
Standard & Poor’s Risk SolutionsData Consortia
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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
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Standard & Poor’s Risk Solutions - Introduction
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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
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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
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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 “non-ratings” business of Standard & Poor’s
RiskSolutions
StructuredFinanceRatings
Corp. &Govt.
Ratings
Rating ServicesFixed Income & Risk Management Services
Standard & Poor's
Leveraged Commentary
& Data
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Data Consortium – What is it?
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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
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Why are Consortia needed?
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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
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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
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PD and LGD meeting banking needs
Business Development
Loan Origination
Portfolio Management
Treasury/ CFO/CEO
• 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
• Economic Capital
• Securitisation• Regulatory
Capital management
• RAROC• Unexpected
loss
Credit Approval
• Stress Test• Portfolio
analysis• Risk
Mitigation• Expected loss
• Stress Test (Company and industry)
• Pricing assessment
• Is credit rated properly?
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Benefits of a Credit Data Consortium
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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
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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
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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
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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
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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
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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.
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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
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What does Standard & Poor’s Provide?
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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
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What does Standard & Poor’s provide? Step 1: Initial diagnosis of potential data availability
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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
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What does Standard & Poor’s provide? Step 2: Implementation of the consortium
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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
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What does Standard & Poor’s provide? - Consortium Organization
Management Committee
Methodology Committee
S&P S&P
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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
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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
IndustryGeographyCompany TypeAsset ClassInstrument Payment DelinquenciesWrite-offs
Link
Financial StatementAccounts
BorrowerCredit Performance Histories and OtherBorrower Information
BorrowerFinancial Performance Histories
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What does Standard & Poor’s provide? Step 3: PD data pooling, cleaning, aggregating, testing and analysis of the data
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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
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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 ”go-forward approach”)
0500
1,0001,5002,0002,5003,0003,5004,000
Bor
row
er C
ount
1 2 3 4 5 6 7 8
Year
Cumulative Distribution for Performing and Defaulted Borrowers
Performing
Defaults
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PD Data Process Flow
Loan Accounting
System Extract
(Borrowers&Loans) Matching, Linking Extracts,
TreatingDuplicates, i.e.
Develop a “System”
Data Validation
Routines
Data
Standardization Data Consolidation Reporting
Mapping
Financial
Statements
Extract
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PD Data Structure
Borrower 1
Borrower 2
Borrower 3
Borrower 1Statement FYE 1Statement FYE 2Statement FYE 3
Borrower 2Statement FYE 1Statement FYE 2Statement FYE 3
Borrower 3Statement FYE 1Statement FYE 2Statement FYE 3
IndustryGeographyCompany TypeAsset ClassInstrument Payment Delinquencies
Balance Sheet ItemsIncome Statement ItemsStatement Period (Year)Audit Quality
Loan AccountingSystem
Financial Statementsfrom Spreading System
Portfolio Default Report
Counts of Defaultersvs. all companiesin portfolio
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Bank’s Historical Financial Statements - Scenario 1
Database
Statements Table(“unrefined” data)
BorrowerFinancialStatements
Bank-analystshave alreadyinput over the years
Statements already in database format
Project Action: Data is extracted for matching and clean-up
Many 1000s of Statements
Loan Accounting
System
Name Matching
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Bank’s Historical Financial Statements - Scenario 2
Data Aggregation
Statements Table(“unrefined” data)
BorrowerFinancialStatements
Bank-analystshave alreadyinput over the years
Project Action: Data is extracted from many hard-drives and aggregated
Loan Accounting
System
Name Matching
Extracts containingmultiple electronic borrower files
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Data Clean-up Tools ExampleName-matching
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Data Standardization – Chart of Accounts Mapping
Total Assets Trade Receivabes
Subsidiary Receivables
Turnover Total Net Worth
Consulting Income
Total EquityAccounts Receivable
RevenuesTotal Assets Other Receivables
Standard Chart of Accounts
Bank-specific Chart of Accounts
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Data Quality AssessmentStage 2
Borrower Matching And Removal Of Duplicates
Data Quality AssessmentStage 1
Automated Data Integrity Checks
Data Quality Workshops
Are Held At the Beginning Of Every New Collection Cycle
Data Quality Assessment Stage 3
Portfolio LevelData Analysis
Management Committee
Data QualityReport and Review
Methodology CommitteeProvides Guidance
Management CommitteeProvides FeedbackAnd Directs Action
Proposed Data Validation Process
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Data Validation Process – Automated Data Checks
Rule No.
Data Secti
on Data Table Rule Name Rule Ensures
11 PD FSData FS Date check (blank) Financial statement date must be provided.
17 PD FSData Audit Qual check (blank) Audit Quality must be provided.
19 PD FSData Curr check Currency must be provided.
5 PD FSCompany State/Province Code check State or Province Code is provided.
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 Hold/Oper check Holding/Operating Indicator must be provided.
78 LGD LASData Loan ID.01 check Loan ID or Facility ID must be provided.
79 LGD LASData Loan ID.02 check Loan ID or Facility ID must be unique for each loan/facility.
13 LGD LGDBorrower Borrower Linking Check BorrowerID must be the same and exist in all tables
14 LGD LGDCollateral Collateral Linking Check Collateral ID must link to a LoanID or BorrowerID
91 LGD LASData Orig Dt.05 check Origination Date < Default Date
92 LGD LASData Orig Dt.06 check Origination Date < Resolution Date
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
Mandatory ElementsChecks
Logical Tests
Relational RulesVerification
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Data Validation Process – Automated Data Checks
Financial Statement Validity Rules
Prioritization Rules
Qualitative Data Validity Rules
Rule No.
Data Section Data Table Rule Name Rule Description
26 PD FSData Ttl Curr Asst.02 check Total Current Assets > 0
28 PD FSData Ttl NonCurr Asst.02 check Total NonCurrent Asset > 0
32 PD FSData Ttl Asst.01 check Total Assets > 0
33 PD FSData Ttl Asst.02 check Total Assets = Total Liabilities + Total Net Worth (+/- 2)
35 PD FSData Ttl Current Liab.01 check
Current Liabilities sub-items balance with Total Current Liabilities
41 PD FSData Ttl LTD Total Long Term Debt > 0 51 PD FSData Ttl COGS check Total COGS > 0 52 PD FSData GrossPrft.01 check Operating Profit > 0
63 PD FSData NI.01 check Net Sale <> 0, Total Operating Profit <> 0, Net Income <> 0
7 PD FSCompany PostalCode check Postal Code is not null or invalid.
8 PD FSCompany Industry Code check Industry Code is not null, invalid or does not correspond to Industry Classification.
80 LGD LASData As Of Dt.01 check As Of Date must be a valid date. 315 LGD LoanData LnTypeCheck Loan type code is not null or invalid.
18 PD FSData AuditQualPrioritization
Financial statements where audit quality is not null, 10-Q, projection, proforma, interim. Audited, Qualified, Management prepared statements are prioritized.
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LGD/Recovery Data – Credit Events and Time-points of Interest
O D – 1 D R1st CF Nth CF2nd CF
O: OriginationD – 1: One-Year Prior to DefaultD: DefaultR: ResolutionCF: Cash Flow
approx. 1 ~ 5 years
Borrower CharacteristicsInstrument InformationSecurity DetailsGuarantor Description
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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
Guarantor
Loan 2
GuarantorCash RecoveredCollateral Cash
RecoveredCollateral
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LGD Data Process Flow
Post-Default
Recovery
Records
AccountingSystem Extract
(Borrowers&Loans)Input
of 30 Resolved Defaulters
Per YearInto “Rec. System”
Data Validation
Routines
Data
Standardization
Data Aggregation
Reporting
Mapping
Resources -Data Team:S&P Loss Data System +Bank’s Analyst + S&P Credit Data Expert
S&PConsortiumanalysts
Automated Processes
Collateral
Records
Fina
ncia
l R
ecor
ds
KEY ACTIVITIES 95% of value added
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What does Standard & Poor’s provide? Step 4: Reporting & Deliverables
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PD Data Quality Benchmarks and Bank Ranking Reports
• Absolute Score
Develop a confidence interval regarding model accuracy based on data quality
• Relative (bank-specific) Score
Quantify bank-specific data quality, and at the same time compare that to consortium benchmark
0%
20%
40%
60%
80%
100%
Default Rate IdentificationAccuracy
Historical Coverage
Data Completeness (minimumquality standard)Business Rules
Portfolio Distribution
BenchmarkBank1
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PD Data Quality Benchmarks and Bank Ranking Reports
DefaultRate Wtd ABC BCD EFG GFH HDD BOS CID CCM XYZ ABD HIBDefault Rate 60% 93% 0% 69% 0% 93% 87% 61% 0% 75% 91% 0%Subm. Default Distribution 20% 73% 29% 67% 80% 77% 1% 81% 49% 43% 72% 0%Acct Sys Default Distribution 20% 60% 0% 19% 0% 66% 13% 29% 0% 59% 60% 0%Total 100% 45.0% 5.9% 31.0% 16.0% 77.8% 20.3% 66.3% 9.8% 64.8% 81.4% 0.0%
Audit Quality Wtd ABC BCD EFG GFH HDD BOS CID CCM XYZ ABD HIBAudited Statements 100% 28.1% 51.0% 40.7% 17.0% 41.7% 22.2% 49.8% 61.1% 29.4% 29.2% 59.2%
Distribution Wtd ABC BCD EFG GFH HDD BOS CID CCM XYZ ABD HIBSize Distribution 70% 53.2% 59.2% 62.9% 65.2% 65.8% 67.1% 53.6% 49.9% 65.5% 63.7% 52.8%Industry Distribution 30% 56.7% 70.6% 77.3% 75.6% 77.3% 70.6% 81.4% 60.1% 71.9% 74.5% 75.0%Total 100% 54.3% 62.6% 67.2% 68.3% 69.3% 68.2% 61.9% 53.0% 67.4% 66.9% 59.4%
Data Check Wtd ABC BCD EFG GFH HDD BOS CID CCM XYZ ABD HIBCustomer Information 25% 90.2% 80.0% 91.2% 96.6% 87.5% 98.4% 85.8% 90.1% 98.6% 95.6% 93.3%Financial Statment 40% 96.3% 96.3% 96.5% 96.5% 88.6% 96.4% 89.7% 97.0% 75.9% 94.0% 96.9%Accounting System 35% 99.9% 0.0% 45.6% 74.2% 69.6% 67.0% 23.0% 82.0% 60.3% 95.4% 0.0%Total 100% 96.0% 58.5% 77.4% 88.7% 81.7% 86.6% 65.4% 90.0% 76.1% 94.9% 62.1%
Business Rules Wtd ABC BCD EFG GFH HDD BOS CID CCM XYZ ABD HIBQuality Rate 50% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%Outlier Rate 50% 85.3% 85.5% 87.2% 87.9% 88.7% 86.6% 91.0% 94.5% 89.7% 88.5% 90.7%Total 100% 92.7% 92.7% 93.6% 93.9% 94.3% 93.3% 95.5% 97.3% 94.8% 94.3% 95.4%
Historical Reporting Wtd ABC BCD EFG GFH HDD BOS CID CCM XYZ ABD HIBYear Distribution 30% 82.5% 69.4% 76.1% 82.0% 86.3% 88.0% 83.9% 85.5% 83.5% 86.9% 84.8%Nb of Stmts per Cust > 5 30% 55.3% 5.9% 73.4% 68.1% 77.2% 67.7% 67.8% 52.4% 71.1% 74.8% 75.9%Current Rate 15% 0.0% 0.0% 0.0% 0.0% 53.3% 0.0% 37.5% 0.0% 77.2% 61.8% 0.0%Diff < 15 Month 25% 64.3% 0.0% 25.0% 70.4% 89.4% 23.8% 75.7% 58.4% 86.0% 89.0% 0.0%Total 100% 57.4% 22.6% 51.1% 62.6% 79.4% 52.7% 70.0% 56.0% 79.5% 80.0% 48.2%
Data submission comparison on all aspects of quality – PD data
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PD Data Quality Benchmarks and Bank Ranking Reports
• Example:Number of historical financial statements per borrower as submitted by the banks
0%
10%
20%
30%
40%
50%
60%
70%
80%
1 2 3 4 5 6 7 8 9 10
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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. non-defaulters
– correlation analyses – mostly industry based
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PD Reporting Examples
Industry Chemical ProductionRegion West Saudi Arabia 25th % 50th % 75th % Average 25th % 50th % 75th % Average
Working Capital Ratio 0.22 2.15 3.37 1.66 0.40 3.87 6.07 2.99Quick Ratio 0.15 0.37 1.20 0.97 0.27 0.67 2.16 1.75Cash Ratio 0.03 0.09 0.11 0.06 0.05 0.16 0.20 0.11Receivables Turnover 0.35 1.22 1.65 0.89 0.44 1.53 2.06 1.11Inventory Turnover 0.84 2.93 3.96 2.14 1.05 3.66 4.95 2.67Debt Ratio 0.53 0.81 2.40 1.33 0.31 0.47 1.39 0.77Debt-To-Equity Ratio 0.89 1.01 1.58 0.98 0.51 0.59 0.92 0.57Interest Coverage 0.20 0.79 1.37 0.66 0.12 0.46 0.79 0.38Return on Assets -0.25 -0.02 1.70 1.12 0.11 0.21 0.34 0.22Return on Equity -0.21 0.35 2.60 0.45 0.05 0.20 1.51 0.26Gross Profit Margin -0.33 -0.25 0.61 0.22 0.12 0.25 0.35 0.13
Leverage
Profitability
Defaults Non-Defaults
Liquidity
Asset Turnover
Media & Telecom Oil & Gas Power Metals & MiningMedia & Telecom 0.266 0.675 0.433Oil & Gas 0.266 0.466 -0.256Power 0.675 0.466 0.24051473Metals & Mining 0.433 -0.256 0.241
Financial Statement Ratio Analysis
Industry Default Correlations
* 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.
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LGD Analytics and Reporting
Recovery Rate (%)
% o
f Res
olve
d In
stru
men
ts
0 20 60 80 100
3
6
9
12
15
18
21
40
Typical Recovery Distribution
25th percentile
18
Average
55
Median
62
75th percentile
90
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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
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What does Standard & Poor’s provide? Step 5: Building models on the aggregated data
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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
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Standard & Poor’s Consortia Experience
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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
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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
58.Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s.
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
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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
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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
61.Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s.
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
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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 spread across offices
– multiple languages spoken (A to Z)
• Set-up to protect confidentiality
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Contacts
64.Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s.
Contacts
Bayan Uralbayeva
Relationship Manager, EECCA
+44 (0) 207 1763919
Michael Baker
Director, Head of Analytical Services
+44 (0) 207 176 3610
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