Credit Scoring for FInancial Institutions, Eland Capital

Eland Capital Division of

The Williams Capital Group, L.P.

September 19, 2011

Credit Scoring for Financial Institutions

Gopalkrishna Rajagopal

Eland Capital, a division of

THE WILLIAMS CAPITAL GROUP, L.P.



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Disclaimer

1



Table of Contents

1. Sector Description & Dynamics

2. Scoring Methodology for Issuer Credit

3. Identification of Key Drivers of Default

4. Testing of Model

5. Conclusions

2



Sector Description & Dynamics

SECTION 1

3



What is a Financial Company ?

We define Financial Companies to be those engaged in asset liability

management:

• Examples:

• A Regional Bank Manages Assets in the form of short term

deposits from retail investors and makes loans to commercial

enterprises.

• A broker dealer executes client transaction either as principal or

as agent on behalf of a client.

Example of companies that are not financial companies include:

• Visa: Functions as a servicing organization. Does not perform Asset

Liability Management.

4



Key Sectors & Business Model

Banks

• Vanilla Lending Business Model: Receive short term deposits and

lend money/ make longer dated loans. Warehouse/manage risk unless

securitized.

Broker Dealers/Private Equity/Investment Management Firms

• Receive deposits and make Investments in a variety of

securities/investments with risk profiles ranging from vanilla to

extremely complex. Entity can act as principal or agent. The

assets/liabilities can be liquid/short dated or very illiquid/long dated.

REITS

• Short Term liabilities are used to fund highly illiquid and long term

assets. Property REITS involve an equity stake, and Mortgage REITS

involve mortgage investments by the REIT.

Insurance Companies

• Receive cash from clients and make investments in very long

dated products with market/actuarial risks.

5



Definition of Default

It can be usually defined as

• Failure to pay principal/interest

• Bankruptcy Filing

• Covenant Violation

• Distressed Debt Restructuring where such information is available

Default is sometimes difficult to define because of restructurings that are

motivated by impending default.

6

Positive Resolution

Missed Payment

Grace Period

Negative ResolutionBankruptcy Filing



Historical Default Data

Data from FDIC website for failed banks assisted by FDIC.

409 failed banks (public and private firms) in the last decade

Defaults are cyclic as expected. Peaks in the business cycle have almost

no defaults. Troughs in the business cycle have more defaults.

Failures dominated by the credit events of 2008-2009.

Any credit model/scoring framework for banks would rely heavily on this

period in 2008-2011 to enrich the dataset

7

0

20

40

60

80

100

120

140

160

180

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

FDIC Failed Banks

Count



Scoring Methodology for Issuer Credit

SECTION 2

8



An Economic Model for Default: The Structural or Merton Model

Merton Model Assumptions

• Applicable to Public Firms with debt

• Firm is solvent if Assets are greater than Liability

• Equity is a Call option on Assets of firm

• If Assets go below liability, we have default

• Probability that Assets go below Liability over a horizon period =

Probability of Default

• Asset follows a log-normal process.

• Asset price is determined by inverting Black Scholes Formula

9

Liability

Asset

Probability

of default

Asset Price

Liability

Asset Price

Distribution

Horizon of

Interest

Time



An Economic Model for Default: The Structural or Merton Model

Strict Merton Model does not connect Default Probability to Default Data

Hence we abstract a parameter called Distance to default

Distance to Default (Leverage to Risk Ratio)-

Empirical Default Probability measured from Empirical Default

Distribution

The smaller the DD, the closer the firm is to Default

Empirical Default Probability = f (DD) where f: non linear function

10

T

TF

A

AEDD

A

A

A

2ln

ln

2



An Economic Model for Default: The Structural or Merton Model for

Financial Institutions - Discussion Points

Probability of Default: Good measure that can be used to compare firms

of different sizes

Liabilities (Corporate debt etc) contribute to financing cash flows in non-

financial sectors but are part of the operational cash flows in the financial

sector.

• What are the liabilities ? Only Face value is obtained from Filings. For

1 year horizons does short term debt play a bigger role due to roll-over

risk ?

• Do we include deposits as liabilities ? FDIC insurance exists upto

$250,000 USD. How do we account for that ?

• Asset Liability mixes can change very rapidly in financials firms. Do

Equity prices transmit those changes to DD ?

11



Identification of Key Drivers of Default

SECTION 3

12



Identification of Drivers of Default

General Drivers of Default: Leverage (Distance to Default, Debt to

Equity), Liquidity Ratios (Quick Ratio, Current Ratio)

Sector Specific Drivers of Default

• Banks:

Asset Quality:

• Non Performing Loans to Loan Loss Reserve,

• Texas Ratio (Non Performing Loan + Real Estate

Owned divided by Book Value of equity + Loan Loss

Reserve)

• Tier 1 Capital divided by Risk Weighted assets

Earnings quality:

• Financing Cash Flow to Net Income

Profitability

• Net Interest Margin

13



Drivers of Default

Synovus:

• Deteriorating Loan Porfolio in 2008 resulted in NPL/LLR reaching level of

over 2 at the height of the credit crisis

• Equity Markets punished Synovus resulting in more than 90% of equity

being wiped out.

• Approximate DD reduces dramatically being driven by the high leverage of

the firm during the height of the crisis

14

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

01-1

2-20

06

01-0

5-20

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01-1

0-20

07

01-0

3-20

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01-0

8-20

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01-0

1-20

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01-0

6-20

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01-1

1-20

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01-0

4-20

10

01-0

9-20

10

01-0

2-20

11

NPL/LLR

DD (approx)

Normalized Equity



Examples of Drivers of Default

Broker Dealers

Asset Quality:

• Tier 1 Capital divided by Risk Weighted assets

• In general firms with better credit have higher Tier 1 Capital

to Risk Weighted Assets. WAMU showed a far lower tier1 to

capital ratio than the other firms in its last days.

15

0

2

4

6

8

10

12

14

16

18

01

-09

-20

06

01

-01

-20

07

01

-05

-20

07

01

-09

-20

07

01

-01

-20

08

01

-05

-20

08

01

-09

-20

08

01

-01

-20

09

01

-05

-20

09

01

-09

-20

09

01

-01

-20

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01

-05

-20

10

01

-09

-20

10

01

-01

-20

11

01

-05

-20

11

WAMU

C

JPM

GS



Off Balance Sheet Liabilities

FAS 140 covered QSPEs (Vanilla Securitizations)

FIN 46 R from 2003 arose as a response to Enron and resulted in

coverage of VIEs (including CP backed VIEs).

FAS 166/167 arose in response to the credit crisis and replaced the

formulaic approach to determining if consolidation was warranted with a

more judgment based approach. Unconsolidated VIE assets as of 31-Dec

2010 are shown below:

Judgment should be exercised to decide if the off balance sheet items

are

• a legal liability of the bank

• The extent of Govt. support that is forthcoming for the off balance

sheet liabilities

16



Scoring Model: Putting it all together

Empirical Default Probability = f(DD, Leverage, Asset Quality, Liquidity,

Earnings Quality ratios). Called an Enhanced Merton Model.

Toy Example: Empirical Default Probability versus DD. Higher DD implies

lower Default Probability

17

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

-0.5 0 0.5 1 1.5 2 2.5 3

DD

ln(p)



Tests For Scoring Model

SECTION 4

18



0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100

% D

efa

ult

ed

Fir

ms

% Total Firms

Accuracy RatioTest

Scoring Model: Testing

Accuracy Ratio Test: Designed for Default Probability type models

•More convex the Cumulative Accuracy Profile is, the better the model

19

Cumulative

Accuracy

profile

Better Model



Scoring Model: Testing

Ex Ante/Ex Post Test

•Plot of Ex Ante versus Ex-Post Default Probability should lie on a 45

degree line

• Successful test indicates absence of bias

20

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

-1.00% 4.00% 9.00% 14.00%

Ex A

nte

De

f. P

rob

Ex Post Def. Prob

45 degree test



Conclusions

SECTION 6

21



Conclusions

An outline for a Scoring Frameworks for Financials Institutions is

provided

Sector Dynamics that drive issuer credit risk are discussed.

Pros and Cons of the methodology are discussed

Tests for the scoring method are discussed

22

Economy & Finance

Credit Scoring for FInancial Institutions, Eland Capital