data consortia taoufik oucheikh

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.

June, 2015

Standard & Poor’s Risk SolutionsData ConsortiaOUCHEIKH Taoufik

2.Permission to reprint or distribute any content from this presentation requires the prior written approval of Standard & Poor’s.

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


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



• 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


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


Data Consortium – What is it?


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


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



• 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


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?


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



• 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



• 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



• 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



• 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



• 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.


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


What does Standard & Poor’s Provide?


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


What does Standard & Poor’s provide? Step 1: Initial diagnosis of potential data availability


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


What does Standard & Poor’s provide? Step 2: Implementation of the consortium


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


What does Standard & Poor’s provide? - Consortium Organization

Management Committee

Methodology Committee

S&P 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


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


What does Standard & Poor’s provide? Step 3: PD data pooling, cleaning, aggregating, testing and analysis of the data


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


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


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


PD Data Structure

Borrower 1

Borrower 2

Borrower 3

Borrower 1Statement 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


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


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


Data Clean-up Tools ExampleName-matching


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


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


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


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.


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


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


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


What does Standard & Poor’s provide? Step 4: Reporting & Deliverables


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



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



• 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


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


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.


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


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


What does Standard & Poor’s provide? Step 5: Building models on the aggregated data


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


Standard & Poor’s Consortia Experience


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


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


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


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


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


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


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


Contacts


Contacts

Bayan Uralbayeva

Relationship Manager, EECCA

+44 (0) 207 1763919

[email protected]

Michael Baker

Director, Head of Analytical Services

+44 (0) 207 176 3610

[email protected]

mailto:[email protected]

mailto:[email protected]


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