1. Building an Internal Rating System : Conceptual Framework
Michael Peng

2. 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
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3. What is an Internal Rating System ?
Credit Rating System consists of all of the methods, processes, controls and data collection and IT systems that support the assessment of credit risk, the assignment of internal risk ratings and the quantification of default and loss estimates.
Internal rating system is the prerequisite for advanced credit risk management, and each financial institution is expected to develop its own internal rating system. Every institution faces a different business environment, so each system should have its own design. For example, a more simple framework might be suitable for small institutions . There is no single answer for the framework of internal rating systems, such as the number of rating grades, a definition of each rating grade, and the method of rating assignments. Financial institutions need to introduce their own system depending on the characteristics of their loan portfolios, their operations, the objectives of the rating system, and other factors. Obviously, the institutions need to make necessary adjustments flexibly due to changes in the business environment.
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4. How is IR related to Basel II?
The New Basle Capital Accord – Consultative Document, April 2003
Appropriate rating system for each asset class
Multiple methodologies allowed within each asset class (large corporate , SME)
Two dimensional rating system
Risk of borrower default
Each borrower must be assigned a rating
Transaction specific factors (For banks using advanced approach, facility rating must exclusively reflect LGD)
Minimum of seven borrower grades for non-defaulted borrowers and one for those that have defaulted
CORPORATE/ BANK/ SOVEREIGN EXPOSURES
Each retail exposure must be assigned to a particular pool
The pools should provide for meaningful
differentiation of risk, grouping of sufficiently homogenous exposures and allow for accurate and consistent estimation of loss characteristics at pool level
RETAIL EXPOSURES
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5. Why Building Internal Rating System (1)?
When banks build their internal rating system, their objective is twofold.
First they want to assess the creditworthiness of companies during the loan application process.
Second they want to use rating information to feed their portfolio management tools designed to produce regulatory capital or economic capital measures.
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6. The Use of Internal Rating System
Setting upper credit limits based on rating grades: For example, institutions can extend a smaller amount of loans to low-graded borrowers and thereby avoid the risk of credit concentration in them.
Setting authority ranks for loan approval by rating grade: For example, loan officers at bank branches can make loan decisions for only a limited amount of loans to low-graded borrowers.
Simplifying the loan review process for higher-graded borrowers: Risk-based allocation of risk management resources can improve efficiency of the overall loan review process.
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7. Foundation IRB Vs Advanced IRB Approach
Foundation IRB Approach
Advanced IRB Approach
Values for Loss given default (LGD) and exposure at default (EAD) are provided by the regulatory authority.
Values for Loss given default (LGD) and exposure at default (EAD) are determined by each bank through internal modeling with a data of 5-7 years.
Assessment of values of credit mitigants is done by the regulatory authority.
Banks may assess the value of its credit mitigants.
For retail exposure, there is no foundation IRB (only advanced IRB where besides PD, the bank concerned will have to estimate LGD & EAD.)
Advanced IRB is applicable to retail exposure also.
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8. An Overview of credit risk measurement under BIS II Framework
Internal Rating System
Quantitative Evaluation
Qualitative Evaluation
Internal Rating
Reporting to the Board
Financial Data
Migration Matrix
Probability of Default (PD)
Stress Testing
Portfolio Monitoring
Provisioning
Pricing
Profit Management
Capital Allocation
Loss Given Default
(LGD)
Risk Components
Calculation of Credit Risk Amount
Expected Loss (EL)
Unexpected Loss (UL)
Exposure at Default
(EAD)
Correlation
Quantification of Credit Risk
Internal Use
Source: BoJ Sep 2005
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9. A Simple Look on Pillar 1 IRB Tasks
Architecture of an Internal Rating System,
Estimation of Risk Components
Internal Use
Risk estimates (i.e., PD, LGD, EAD) predictive and accurate?
Quantitative Rating Model
Qualitative Evaluation
“Use Test”*: Pricing,
Portfolio Monitoring,
Credit Risk Quantification?
Validation Work
* Use Test: IRB provision that requires ratings and default and loss estimates to “play an essential role” in the Institution’s credit approval, risk management, internal capital allocations and corporate governance functions.
Source: BoJ Sep 2005
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10. Rating Management System
Overview of a
Rating Management System
Ratings summary
Audit Trail
Facility/ Exposure details
Collateral and LGD details
Quantitative inputs
Qualitative inputs
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11. Internal ratings System (RMS): User Interface
Bank’s own internal view
Rating Templates
Qualitative assessment
Quantitative Assessment
External Ratings
External Models
Peer comparison
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12. 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
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13. 2. Expected Loss Framework
Each prospective or existing loan facility must undergo three consecutive stages to determine expected loss.
Stage 1
Stage 2
Stage 3
Rating (PD) x
Exposure at Default x
Expected Loss
Loss Given
Default =
Corporates
Banks
Insurance
Project Finance
SME
Seniority
Maturity etc
Data
Collateral
Haircut Policy
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14. Across different asset classes
3. Ratings and Pds
Across different asset classes
Low volume of data + High Exposure
RATING TEMPLATES ARE SUITABLE
Typical Loan Book
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
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15. 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.
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16. Evaluation of Qualitative Factors
Large corporates and specialised lending Sample template – Insurance Companies
European Bank
Evaluation of Qualitative Factors
Credit factors
Weights
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17. Clear and consistent rating criteria
Large corporates and specialised lending Sample template – Insurance Companies
Clear and consistent rating criteria
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18. Evaluation of Quantitative Factors
Large corporates and specialised lending Sample template – Insurance Companies
Evaluation of Quantitative Factors
European Bank
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19. Large corporates and specialised lending Sample template – Insurance Companies
Quantitative Assessment Based on S&P’s Experience
Benchmarks are provided per sector and market
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20. Large corporates and specialised lending Sample template – Insurance Companies
Backtesting and Mapping to External Indicators of PD
Backtest model results versus S&P ratings or estimates
Compare results and
map the scales
Internal Rating Scale
S&P Scale 1 2 3 4 5 6
AAA AA A
BBB BB B
CCC
S&P
1-yr PD
AAA AA
0.02 A
BBB
0.19 BB
0.88 B
5.44
CCC
23.76 1 2 3 4 5 6
Satisfy board regarding the validity of an internal rating system
Use of external default data
Prepare for CBO/CLO
Identify areas of inconsistency in order to improve an internal ratings process
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21. Rating Assignment Horizon—Relationship with Business Cycle: point-in-time vs. through-the-cycle system
The time horizon of assessing the creditworthiness of borrowers in assigning ratings is also important. Two different approaches may be taken in considering the effect of the business cycle in assigning ratings. One is a point-in-time point-in-time system (PIT rating). In PIT rating, risks are evaluated based on the current condition of a firm regardless of the phase of the business cycle at the time of evaluation. The other is a through-the-cycle system (TTC rating). In TTC rating, risks are taken into account on the assumption that a firm is experiencing the bottom of the business cycle and is under
stress.
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22. PIT Rating vs TTC Rating
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23. 4. Exposure and Facility Analysis
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
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24. 5. LGD and Definition of default
The definition of default is not the same in all countries, often bank behaviour is linked to national legal specificities US
BASEL II UK
FRANCE
GERMANY
ITALY
Bankruptcy
Credit obligation default
90 days credit obligation default
Debt restructuring
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25. 5. LGD – Loss Given Default -
LGD Behaviour in the US
Average Overall Recovery By Industry, some differences
Industries with 9+ Observations 70 60 50
Recovery (%) 40 30 20 10
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
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26. LGD Behaviour Overall - No Clear pattern!!
LGD Behaviour by debt Structure and Industry
Overall - No Clear pattern!!
Need More data
Clear definitions
Need to pool data
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27. Loss Given Default
Loss Given Default: LGD information is scarce and complicated
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28. Expected Loss
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29. Standard & Poor’s Risk Solutions
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
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30. Internal Rating Approach BIS II – Standard Approach
From Pillar 1 to Pillar 2
Business Processes
From Expected Loss to Economic Capital
Risk Appetite
Capital Allocation
Active Portfolio Mgmt.
Mitigation Strategies
Risk Averse Pricing
RAPM & VaR limits
EcoCap Optimisation
Correlations
Portfolio Approach
Regulatory Capital Requirement
Risk-Adjusted Pricing
Provisioning Policies
Limits Based on EL
Early Warnings
BIS II – IRB Advanced
Internal Rating Approach
Regulatory Capital Requirement
Risk-Adjusted Pricing
Provisioning Policies
Limits Based on EL
Early Warnings
Diversification
BIS II – IRB Foundation
Regulatory Capital Requirement
BIS II – Standard Approach
External PD
Supervisory LGD
Supervisory EAD
Internal Estimate PD
Supervisory LGD
Supervisory EAD
Internal Estimate PD
Internal Estimate LGD
Internal estimate EAD
IRB Parameters
Macroeconomic Forecasts
Inputs
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31. Note1: Expected Loss (EL)
Expected Loss is the bank’s cost of doing business. Expected loss has to be provided for.
The Expected Loss (in currency amounts)
EL = PD * EAD * LGD
If expressed as a percentage figure of the EAD
EL = PD * LGD.
The bank should also proactively incorporate an expected loss rate in the estimation of the total spread to be charged on the loan.
Expected loss is not a measure of risk as it is anticipated.
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32. Note 2: Unexpected Loss (UL)
Regardless of how prudent a bank is in managing its day-to-day business activities, there are market conditions that can cause uncertainty in the amount of loss in portfolio value.
This uncertainty, or more appropriately the volatility of loss, is the unexpected loss. Unexpected losses are triggered by the occurrence of higher default rates as a result of unexpected credit migrations.
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33. Note 3:EL Vs UL
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