The document discusses how to build an internal rating system for Basel II compliance. It outlines key attributes such as a consistent analytical approach, transparency, and central data storage. An expected loss framework is recommended using ratings, probability of default, exposure at default, and loss given default. Case studies demonstrate rating templates for different asset classes and the benefits of data pooling to estimate default and recovery rates.

1. How to Build an Internal Rating System for Basel II Aidan O’Mahony Managing Director Tel: +44 207 826 3518 Standard & Poor’s Risk Solutions

2. Risk Solutions was formed in 2001 in response to client demand for tools and services to better manage credit risk exposures. Risk Solutions is the customised risk management services arm of Standard & Poor’s focusing on: 1. Customised Credit Services (internal rating systems) 2. Credit Tools, Models, Data & Research (eg Pd & LGD) 3. Credit Training (Open enrolment and Custom Courses). Standard & Poor’s Risk Solutions

3. Agenda Key attributes of an Internal Rating System Expected Loss Framework Rating and PDs Exposure and Facility tracking Loss Given Default Case Study – Rating Management System Concluding Comments

4. What is an Internal Rating System ? Consistent rating approach across all classes Desk-top IT application – intranet delivered across an organisation Analytical and Management tool for tracking credit exposures and linking into Raroc models Satisfies Basel II Internal Ratings-Based Approach requirements

5. Facility/ Exposure details Ratings summary Collateral and LGD details Qualitative inputs Audit Trail Overview of a Rating Management System Quantitative inputs

6. Rating and PDs: Internal Ratings System (RMS) Qualitative assessment Quantitative Assessment S&P’s Rating Templates External Ratings External Models Peer comparison Bank’s own internal view

7. 1. Key Attributes of an Effective Internal Rating System Consistent analytical approach to ratings and PDs – all asset classes Transparency of methodology; Visible audit trail; Logical workflow, including sign-off and permissions; Open architecture with a modular approach that is easily adaptable and scalable; Data access aligned with roles and responsibilities; and Centralised information storage

8. 2. Expected Loss Framework Each prospective or existing loan facility must undergo three consecutive stages to determine expected loss. Stage 1 Stage 3 Stage 2 x x = Expected Loss Rating (PD) Corporates Banks Insurance Project Finance SME Exposure at Default Seniority Maturity etc Data Collateral Haircut Policy Loss Given Default

9. 3. Ratings and Pds Across different asset classes The methodologies used for assessment of creditworthiness of different asset classes should balance: the volume and scope of data available, with the relative exposure of the bank High volume of data + Low Exposure MODELS ARE SUITABLE Low volume of data + High Exposure RATING TEMPLATES ARE SUITABLE Typical Loan Book

10. Large corporates and specialised lending Characteristics of these sectors Relatively large exposures to individual obligors Qualitative factors can account for more than 50% of the risk of obligors Scarce number of defaulting companies Limited historical track record from many banks in some sectors Statistical models are NOT applicable in these sectors: Models can severely underestimate the credit risk profile of obligors given the low proportion of historical defaults in the sectors. Statistical models fail to include and ponder qualitative factors. Models’ results can be highly volatile and with low predictive power.

11. Large corporates and specialised lending Sample template – Insurance Companies European Bank Evaluation of Qualitative Factors Credit factors Weights

12. Clear and consistent rating criteria Large corporates and specialised lending Sample template – Insurance Companies

13. Large corporates and specialised lending Sample template – Insurance Companies Evaluation of Quantitative Factors European Bank

14. Quantitative Assessment Based on S&P’s Experience Benchmarks are provided per sector and market Large corporates and specialised lending Sample template – Insurance Companies

15. S&P Scale Internal Rating Scale Use of external default data Prepare for CBO/CLO Satisfy board regarding the validity of an internal rating system Identify areas of inconsistency in order to improve an internal ratings process Backtest model results versus S&P ratings or estimates Compare results and map the scales Backtesting and Mapping to External Indicators of PD Large corporates and specialised lending Sample template – Insurance Companies 6 5 4 3 2 1 23.76 CCC 5.44 B 0.88 BB 0.19 BBB 0.02 A 0.02 AA 0 AAA 1-yr PD S&P 4 1 CCC 2 4 1 2 B 1 6 1 BB 1 5 BBB 1 5 A 1 2 3 AA 1 AAA 6 5 4 3 2 1

16. In the experience of S&P Risk Solutions, over the last few years, banks have adopted different modelling techniques which in turn produce results in different scales. Once an internal model is in place, it is important to ensure that the choice of methodology is adequate to the bank’s requirements / data, and that the methodology is applied consistently and produces reliable results Modelling SMEs

17. 4. Exposure and Facility Analysis Stage 2: Exposure and Facility Analysis - Typically a corporate obligor will have a number of facilities with a bank, including secured and unsecured loans and overdraft facilities

18. 5. LGD and Definition of default US BASEL II UK FRANCE GERMANY ITALY Credit obligation default 90 days credit obligation default Debt restructuring Bankruptcy T he definition of default is not the same in all countries, often bank behaviour is linked to national legal specificities

19. 0 10 20 30 40 50 60 70 Recovery (%) 5. LGD – Loss Given Default - LGD Behaviour in the US Average Overall Recovery By Industry, some differences Industries with 9+ Observations Automotive Comp. & Elec Retail Food & Drug Gaming & Hotel Services & Leasing Real Estate Metals & Mining Retail Textile & Apparel Transportation Average Building Materials Healthcare Oil & Gas Television Manu. & Machinery Printing & Pub. Food & Beverage

20. LGD Behaviour LGD Behaviour by debt Structure and Industry Overall - No Clear pattern!! Need More data Clear definitions Need to pool data

21. Data Pooling Exercise: Project Finance - Case Study Project Finance Study consisted of 4 pioneer banks with historical data through the 1 st quarter 2002. Now 20 banks worldwide involved in data pooling with S&P Risk Solutions Definitions were agreed upon of : Project Finance Loans were agreed upon Default definitions were agreed upon Definitions of emergence was agreed upon Data was collected from as far back as 1983 Data was validated Projects with multiple bank participants were matched together Basel willing to accept pooled data

22. Default analysis was performed Cumulative Probabilities of Default were calculated Confidentiality was maintained throughout the process Data Pooling Exercise: Case Study Analysis: Results: Average default rate of 7% Average Recovery Rate of 75%

23. Data Pooling: Next Steps Other data pooling initiatives underway: SMEs: pan-European data pooling initiative Leveraged Finance: Large Corporates

24. Loss Given Default Stage 3. Loss Given Default: LGD information is scarce and complicated

25. Expected Loss

26. Concluding Comments To build an internal rating system for Basel II you need: Consistent rating methodology across asset classes Use an expected loss framework Data to calibrate Pd and LGD inputs Logical and transparent workflow desk-top application Appropriate back-testing and validation. Standard & Poor’s Risk Solutions

27. A idan O’Mahony Managing Director Standard & Poor’s Risk Solutions Tel: + 44 20 7826-3 518 Fax: + 44 20 7826-3565 E-mail: Aidan_O’Mahony @StandardAndPoors.com Standard &Poor’s Risk Solutions Garden House 18 Finsbury Circus London EC2M 7NJ United Kingdom Contacts