How to Build an How to Build an Internal Rating System Internal Rating System

for Basel IIfor Basel II

Aidan O’MahonyManaging DirectorTel: +44 207 826 3518

Standard & Poor’s Risk Solutions

204/13/23

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

304/13/23

Agenda

1. Key attributes of an Internal Rating System

2. Expected Loss Framework

3. Rating and PDs

4. Exposure and Facility tracking

5. Loss Given Default

6. Case Study – Rating Management System

7. Concluding Comments

404/13/23

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

504/13/23

Facility/ Exposure details

Ratings summary

Collateral and LGD details

Qualitative inputs

Audit Trail

Overview of a

Rating Management System

Quantitative inputs

604/13/23

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

704/13/23

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

804/13/23

2. Expected Loss Framework

Each prospective or existing loan facility must undergo three consecutive stages to determine expected loss.

Stage 1 Stage 3Stage 2

x x = Expected Expected LossLoss

Rating (PD)

CorporatesBanksInsuranceProject FinanceSME

Exposure Exposure at Defaultat Default

SeniorityMaturity etc

DataCollateralHaircut Policy

Loss Given Default

904/13/23

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

Retail

SMEs

Large Corporates

Banks Insurance

Specialised Finance

Public Sector

High volume of data + Low Exposure

MODELS ARE SUITABLE

Low volume of data + High Exposure

RATING TEMPLATES ARE SUITABLE

Typical Loan BookTypical Loan Book

1004/13/23

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.

1104/13/23

European Bank

Evaluation of

Qualitative Factors

Credit factors

Weights

Large corporates and specialised lending Sample template – Insurance Companies

1204/13/23

Clear and consistent

rating criteria

Large corporates and specialised lending Sample template – Insurance Companies

1304/13/23

Evaluation of Quantitative Factors

European Bank

Large corporates and specialised lending Sample template – Insurance Companies

1404/13/23

Quantitative Assessment Based on S&P’s Experience

Benchmarks are provided per sector and market

All Combined 1 2 3 4 5

TAC/Total Assets >45% 20%-39% 5%-20% 2%-4% <2%

Pre-Tax Rtn on Assts >8% 2%-7% (0.2)%-2% (2.1)%-(0.2)% <(2.1)%

Gross Ex/GWP <5% 5.1%-17% 17.1%-39.0%

39.0%-45.1% >45.1%

Growth in gross premium (%)

>20% 10%-20% 1%-10% (5)%-1% <(5)%

Gross Premium Income (USD Millions)

>900 500-900 30-500 30-10 <10

Net Inv Yield >10.1% 5%-10.1% 2%-5% 0.5%-2% <0.5%

Inv Assets - (Bonds+Cash)/TAC

<2% 2.1%-5% 5%-8% 8%-12% >12%

Cash In/Cash Out >200% 99%-200% 20%-99% 10%-20% <10%

Short Term Assets + Bonds / Total Assets

>90% 75%-90% 50%-75% 30%-50% <30%

Large corporates and specialised lending Sample template – Insurance Companies

1504/13/23

1

2

3

4

5

6

S&P 1-yr PD

AAA 0

AA 0.02

A 0.02

BBB 0.19

BB 0.88

B 5.44

CCC 23.76

1 2 3 4 5 6

AAA 1

AA 3 2 1

A 5 1

BBB 5 1

BB 1 6 1

B 2 1 4 2

CCC 1 4

S&

P S

cale

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

1604/13/23

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

1704/13/23

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

1804/13/23

5. LGD and Definition of default

US

BASEL II

UK

FRANCE

GERMANY

ITALY

Credit obligation

default

90 days credit obligation

default

Debt restructuring

Bankruptcy

The definition of default is not the same in all countries, often bank behaviour is linked to national legal specificities

1904/13/23

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5. LGD – Loss Given Default -

LGD Behaviour in the US

Average Overall Recovery By Industry, some differencesIndustries with 9+ Observations

2004/13/23

LGD Behaviour

LGD Behaviour by debt Structure and Industry

Overall - No Clear pattern!!Overall - No Clear pattern!!

Need More data

Clear definitions

Need to pool data

2104/13/23

Data Pooling Exercise: Project Finance - Case Study

Project Finance Study consisted of 4 pioneer banks with historical data through the 1st 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

2204/13/23

• 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%

2304/13/23

Data Pooling: Next Steps

Other data pooling initiatives underway:

SMEs: pan-European data pooling initiative

Leveraged Finance:

Large Corporates

2404/13/23

Loss Given Default

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

2504/13/23

Expected Loss

2604/13/23

Concluding Comments

To build an internal rating system for Basel II you need:

1. Consistent rating methodology across asset classes

2. Use an expected loss framework

3. Data to calibrate Pd and LGD inputs

4. Logical and transparent workflow desk-top application

5. Appropriate back-testing and validation.

Standard & Poor’s Risk Solutions

2704/13/23

Aidan O’MahonyManaging DirectorStandard & Poor’s Risk SolutionsTel: + 44 20 7826-3518Fax: + 44 20 7826-3565E-mail: Aidan_O’Mahony@StandardAndPoors.com

Standard &Poor’s Risk SolutionsGarden House

18 Finsbury CircusLondon EC2M 7NJ

United Kingdom

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