Benos - SME Risk Rating

8/7/2019 Benos - SME Risk Rating

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Group Risk Management Division 1

A Simple Credit Risk Modelfor SMEs

Dr. Alexandros Benos

National Bank of Greece


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What is a credit risk model?

Its a mathematical function which,given a set of criteria as input(independent variables), returns ameasure for credit risk as an output(dependent variable).


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Measures of Credit Risk Probability of Default measures

At OBLIGOR level PD Score Rating

Loss Given Default measure At FACILITY level

Depends mainly on the debt seniority and collateral

Portfolio measures Expected Loss Unexpected Loss VaR

Loss Distribution

Estimate for the obligors creditworthiness


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The use of PD models

Classification of Obligors depending ontheir probability to default

Used for

Credit Granting

Credit Pricing



The Credit Granting problem

To lend or not to

lend?

To lendTo lend or not toor not to

lend?lend?



An example (1)

Firm A (Clothes manufacture) requestsa loan of 5.000.000

To lend or not to lend ?

Can we answer the question?

No, we need more information!



An example (2)

Firm A requests a loan of 5.000.000 Criteria:

Industry: Clothes manufacture HIGH RISK

Size: Annual sales 50.000.000 GOOD

Leverage: Equity / Liabilities = 1:12 BAD Liquidity: Current ratio = 1,1 MEDIUM

Earnings: NI / Sales = 15% GOOD

To lend or not to lend ? Can we answer the question NOW?

Maybe some statistics would help



An example (3)

Statistical inferences on the historic data ofthe specific portfolio:

Mean default frequency = 3,8%

The default probability for a firm with the aboveproperties is 3,4%

To lend or not to lend ?

Can we answer the question NOW ?

Yes, the granting of the loan will improveimprove thequality of the portfolio.



Pricing the Credit in example (4)

Take into account other parameters too: The LGD for the specific type of credit and

collateral.

Using it we can estimate EL(%) =PD x LGD The proposed spread must be greater than EL(%) for

the credit to be profitable

Example: In case LGD = 50%, EL(%) = 3,4% x 50% =

1,7%, hence

The minimum spread (due to credit risk) is170bps



Why a model then?

The statistics make the difference, butthe initiating of a new statistical projectfor each credit request:

is unpractical,

charges the credit granting process with

additional time and costs and most interestingly it is not necessary!

Instead, we can use a PD model



A PD model

Contains in it, all the correlations between theselected criteria and the historically observeddefaults.

Achieves the same results with the originalstatistical process.

Is costless in use and returns its results

instantly. Is created once but can be used to evaluate

any obligors PD for a long time period.


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Different PD Measures The absolute value of PD is impractical

As a point-in-timemeasure, it is relatively unstable throughtime.

Credit scores Are indicators in a continuous numeric scale. Have lower and upper bounds, representing relative default

risk.

Credit ratings Are ranks which stand for similar credit risk profiles of the

obligors. They can be numerical (1,2,) or symbolic (AA+, A, BB- etc)

They are usually much more stable than instant PDs. They are often considered to be through-the-cycle

measures Compatible with Basle 2 IRB approaches


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NBGs Model

Supports evaluation of firms located in different countries (mostly Balkan

ones),

with or without full financial statements, for which their shareholders liability extends to

their personal property

Forward looking features Through cash flow auto and manual projections

Uses both qualitative & financial criteria


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Groups of Criteria Environment

Country rating Sector risk Competition

Management

Managers experience Stability

Enterprise Years of operation Customers concentration Operational risks

Credit history Court decisions Credit Bureau Black list

Defects in operations Signs of Illiquidity

Frequent past dues to Bankand other creditors

Leverage

Debt To Assets ratio Shareholders property

Expected cash flows Debt capacity and free capital Current past dues Financial projection

Cash flows volatility


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NBGs Rating Scale

A2 to AaaA3

Baa1

Baa2

Baa3Ba1

Ba2

Ba3

B1

B2

B3

CaaMoodys

B+

0,08%A to AAAA10,12%A-

A2

0,20%BBB+A3

0,30%BBBB1

0,40%BBB-B20,60%BB+

B3

1,50%BBC1

2,30%BB-C2

3,50%C35,85%

D1

7,45%B

D2

12,20%B-D3

18,00%CCC+ES&PModel

Mean

PD

Rating

It directlycorresponds to S&Pand Moodys scales Naming convention

is indicative only.

It can be adaptedaccording to theconcentrations of thefirms in the ranks


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The Application Supports the full evaluation process for corporate

obligors

Provides tools for financial and ratio analysis

cash flow analysis financial projections

Accommodates the Credit Risk Model

Can be structured either as a full-featured independent network application, or

as a module that can be incorporated in the banks coresystem


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The ArchitectureClientsClients

User interface

Application ServerApplication Server

Model

Business logic

Data serverData server

Databases

Data maintenance

Internet

Core systemCore system


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Adoption Steps Exploration of

the existing credit granting and risk measuring processes the risk managers and the credit officers point of view the available data the IT systems in place

Data analysis and initial model calibration Design for the incorporation of the model into the

banks processes and IT systems

Implementation and documentation of the newprocesses Users training Operation and maintenance Model validation and recalibration on an annual

basis

Benos - SME Risk Rating

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