RMPG Learning Series CRM Workshop Day 3

IMaCS 2010Printed 11-May-11

Page 1For Classroom discussion only

Agenda for Day 3

Credit Rating Models

Lunch Break

Case Studies

Open Session/ Q&A



Introduction to credit risk modeling – What is a model

Risk Score = Co-eff1*Leverage + Co-eff2 *Current Ratio +……. Co-eff6 *Integrity +….. Co-eff 8 *Industry Phase….



Credit Risk Models - Some Examples

� Altman’s Z - score model (Multiple Discriminant)

� Merton model

� Judgmental

� Hybrid



Z = 0.012 X 1 + 0.014 X 2 + 0.033 X 3 + 0.006 X 4 + 0.999 X 5

Where,

• X 1 = Net Working Capital / Total Assets

•X 2 = Retained earnings / Total Assets

•X 3 = PBIT/ Total Assets

•X 4 = Market value of equity/ Book Value of Total Liabilities

•X 5 = Sales/ Total Assets

Altmans’s Z Score Model



Z = 0.012 X 1 + 0.014 X 2 + 0.033 X 3 + 0.006 X 4 + 0.999 X 5

Z

< 1.81 - Failing Zone

1.81 to 2.99 - Ignorance Zone

> 2.99 - Non-failing Zone

Altmans’s Z Score Model



Merton Model

� Step 1: Estimate asset value and asset volatility from equity value and

volatility of equity return

� Step 2: Calculate distance Asset Value - Default point

to default (DfD) Asset Value * Asset Volatility

� Step 3: Calculate expected default frequency

� Step 1: Estimate asset value and asset volatility from equity value and

volatility of equity return

� Step 2: Calculate distance Asset Value - Default point

to default (DfD) Asset Value * Asset Volatility

� Step 3: Calculate expected default frequency

Expected Default Frequency - is calculated using 3 steps

=



� The market value of a firm’s assets and its historical volatility imply a distribution of future firm value

� Given today’s obligations (debt), we can calculate the probability that the market value of assets will be

lower than the firm’s obligations one year from now (i.e., default)

� Distance to default is mean value minus debt, normalized by S.D.

•Am

ount

in

Calculating distance to default: Merton



The quantitative model would derive its strength from the Bank’s data and the human expertise and experience of CO

Industry Firm Standing Management….

Convert into proxies

Construct indices

Professional Judgementfor weights

Check for consistency

Statistically explanatory set of variables



The benefit of the model

Reduces the dimensionality of space of the credit officer



Small Business

Retail LoanBankExposures

LessReliable

Partial Information

MoreReliable

Regional or Local

Global, National or regional

Local

Corporate Credit

Reasonably Reliable

Global,National orRegional

High value & Low Numbers

Lower value & Higher Numbers

Low value & High Numbers

High Value &Low numbers

Quality of financial statements

Market Situation

Type

Banks need different risk scoring models for different credit segments



No. of rating models/ borrower categories in new systems

� The number of rating models should be determined:

� Based on the current portfolio of the bank

� Based on business strategy and focus areas of the bank

� A good thumb rule, is that 80-85% of the bank’s credit portfolio should be risk rated.

For the remaining portfolio, the bank could use pool-based approach

� Banks use the following models:

� Corporate Segment: Large, SME and Small Business models;

� Retail Segments: Home Loan, Personal Loan & Credit Card models;

� Commercial Segment: Bank and NBFC models;

� Project Models: Infrastructure, Green-field and Brown-field models



Data Collection - What Type of Data is required to be collected

Accounts (On which data is being collected)

Performing Accounts Non - Performing Accounts

This sample of accounts has to be representative of the Bank’s portfolio



Data Collection - What Type of Data is required to be collected

Data(Historical)

Financial Information – Balance Sheet, Profit and Loss, Cash Flow

Qualitative Data

Management

Industry

Firm Standing

Conduct of Account



Why do we need to collect this data ?

• Historical Data is the basis of estimating the model equation (along with expert opinion)

• What is the model ? Risk Score = A*Leverage + B* Current Ratio +C*Sales/Total Assets……

• The Data would be the basis for both deducing the predictor variables and the coefficients of the model equation (along with expert opinion)

• In other words, the fact that Leverage is to be chosen in the model and the A (coefficient of Leverage) is both coming from the Bank’s historical data



Data Collection – The criticality of this exercise

The model is only as good as the data used to construct it

• The Data sample used to estimate the model should be representative of the Bank’s portfolio

• The Data sample has to be accurate



The Broad Model Construction Philosophy

Choose Universal set ofRisk Drivers

Shortlist Predictive Parameters

Qualitative variablesIndex construction

Limit/Filter parameters

Transform Parameters

Phase IParameter Selection

Phase IIModeling Technique

Phase IIIRisk Grading

Statistical Technique -(DA, LR, Probit etc)

Implied probabilities(Output of the Statistical Technique)

Risk Grading (by probability)

Adjustment for account Operations(Modified Borrower risk score)



Choosing of predictor parameters – The art and science of it

How are financial ratios related to default ?

• There exists a correlation between select ratios and default

• The relation is non-linear (at no point is default certain)

• Default would depend upon other predictor variables of the account



Choosing of predictor parameters – The art and science of it

Aid the modelerin answering

• The curve – Shape of the relationship between the predictor variable anddefault (In essence, what default probability corresponds to what parameter values)

• What are the most potent ratios (What profitability ratio is the most potent predictor• How do correlations affect the coefficients in a multivariate model framework

Analysis Univariate relation of predictor parameters to default (Financial Ratios)



Forward selection process

• Start with variables with the highest univariate correlation with defaultand add more until additional variables have no additional importance

• Ensure that variables selected do not suffer from “multicollinearity” (The wrong sign problem, inflated variances of coefficients, poor out of sampleperformance)

The essence of the activity

•Selection done based on suggestion of univariate power•Validation done in a multivariate framework



The Broad Model Construction Philosophy

Universal set of Predictor Parameters

Choose Predictive Parameters

Qualitative variablesIndex construction

Limit/Filter parameters

Transform Parameters

Phase IParameter Selection

Phase IIModeling Technique

Phase IIIRisk Grading

Statistical Technique -(DA, LR, Probit etc)

Implied probabilities(Output of the Statistical Technique)

Risk Grading (by probability)

Adjustment for account Operations(Modified Borrower risk score)

Most critical processes in model construction



Transformations applied to Predictor Parameters

Why is there a need toapply transformations??

Movement of Leverage from 1-2 is not at as risky as a movement from 2-3

The idea behind applying transformations is to mimic this analysis happening in the credit officers mind

Movements of values in predictor variables result in non-linear Credit Risk profile is highly non-linear. We need to transform predictor variables to factor this



The Borrower Risk Score will be adjusted for risk impact of account operations

Financial Risk

Management Risk

Industry Risk

Firm Standing

Borrower Score

Account Operations*

Adjusted Borrower Score

* For existing accounts



The monitoring parameters will be set in consultation with the management and will be an input for deriving modified risk grade

1. No. of days delay in receipt of principal/interest instalments

2. Submission of progress reports

3. Compliance with sanctioned/disbursement conditions

4. Key employees turnover

5. Comments on operations/assets during site visits

6. Change in accounting period during the last five years

7. No. of times rescheduling/relief obtained from lending institutions

Factors on which monitoring levels are to be set are as follows:



The weightages of the various components – Concept of Dynamic Weights

Financial Risk

Management Risk

Industry Risk

Firm Standing

Borrower Score

Account Operations

A Linear Rating Model

40 %

15%

15 %

10%

20 %



The weightages of the various components – Dynamic Weights

Credit Risk is highly non-linear.

• Borrower scoring low on integrity will not be accepted irrespective of scores on other parameters

• Borrower with a leverage of 10 would not be accepted irrespective of scores on other parameters

It is critical that the risk-scoring model mimics this non – linear thinking of a experienced credit risk officer




Case Study – Consider a account which got the following scores in Management Risk

Parameter Risk Score

� Integrity----------------------------------------------------------------------------- 4

� Diversion of Funds-----------------------------------------------------------------4

� Business Commitment-------------------------------------------------------------3

� Payment Record of Group companies-------------------------------------------4

� Internal Control---------------------------------------------------------------------4

� Succession Planning----------------------------------------------------------------4

The scale is defined such that 1 is the best and 4 is the worst




• The Borrower has a very high management risk. The Credit officer automatically

recognizes this and would not lend no matter how impressive the financials or business

• The credit risk model has to adjust accordingly to mimic this non-linear analysis

happening in the credit officer’s mind. It cannot be churning out a safe risk-grade for

such an obviously high risk account

• The solution is the dynamic weightsconcept where the importance of every parameter

would depend on the value allotted to it by the Credit officer



Model Calibration – The Process

LR OutputAccount 1 – 0.001Account 2 – 0.002Account 3 – 0.004Account 4 – 0.007…………………..……………………………………………………………..………………………………………….…………………….……………………..…………………….Account 347 – 0.97Account 348 – 0.98Account 349 – 0.99

Model CalibrationProcess

Model Output

RG1 0.00 – 0.05

RG2 0.05 – 0.08

RG3 0.08 - 0.12

……………….

……………….

……………….

……………….

……………….

RG10 0.85 – 1.00



Model Calibration Process – What are the guidelines of the process

� For Basel II IRB compliance, each risk grade is to be mapped to a unique PD - No overlap of

risk

� There should be no undue concentrations of borrowers in any one risk grade

� Number of Risk grades and interpretation desired is decided apriori and the spreading is done

based on this

� Ensure that the statistical PD estimates for every risk grade follow a desired trend



0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

1 2 3 4 5 6 7 8 9 10 11

Risk Grade

Pro

bab

ility

of

Def

ault

1% 2%

20%

39%

23%

13%

2%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1 2 3 4 5 6 7

Risk Rating

Per

cen

t o

f B

orr

ow

ers

Reduce concentrations in any one rating grade

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

0 1 2 3 4 5 6 7

Risk Rating

Pro

bab

ilit

y o

f D

efau

lt

There should be no overlap of PDs by grade

Model Calibration Process – What are the guidelines of the process



Entry and exit criterion

1. At an operating level, an entry grade of RG 6 or better would roughly correspond to the credit acceptance levels based on risk appetite.

2. The exit criteria (in case this means exiting from the portfolio to other banks) may be set slightly lower at RG 7

3. The monitoring intensity may be set depending on the grades , which need to be annually re-evaluated

Entry

Exit

41%

100%

11%

20%

40%

52%

63%

73%

82%89%

100%

9%0% 0% 0%

32%27%

23%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

RG1 RG2 RG3 RG4 RG5 RG6 RG7 RG8 RG9

% defaults % portfolio

Risk scores between 1 & 3Green Zone

Risk scores > 3 & up to 5Yellow Zone

Risk scores > 5 & up to 7AmberZone

Risk scores greater than 7RedZone

Good quality credit

No immediate concern

Requires intensive monitoring

NPA/ Could turn NPA over the medium term

Low Risk

•High •Risk

1 3 5 7 10

Strong Credit Quality

•Gr 1 •Gr 2 •Gr 3 •Gr 4 •Gr 5 •Gr 6 •Gr 7 to •Gr 9

Relative Risk of Default



Risk Scoring Model - the end product

Risk

Risk Scale

1 2 3 4 5 6 7 8 9

Good quality credit

No immediate concern

Requires intensive monitoring

NPA/ Could turn NPA over the medium term



The criticality of model calibration

A Model may be powerful (able to distinguish between good and bad)

BUT

It maybe be incorrectly calibrated



Model Validation Results- Cumulative Accuracy Profile (CAP) Plots

CAP Plot

0%

20%

40%

60%

80%

100%

120%

0% 20% 40% 60% 80% 100%

Percentage of Proposals accepted

Per

cen

tag

e re

du

ctio

n in

NPA

Random Model

Rating Model

Perfect Model



CAP curve metric to assess Model Power – The GINI coefficient

• The Gini Coefficient of the CAP plot is defined as the ratio of the

area between the model curve and the random plot and area

between the perfect model and random plot. Consequently the

closer the AR of the model is to one the better the discriminatory

power of the model is.

• Gini Coefficient (AR) = Area between model curve and

random plot / Area between Perfect model and Random plot



Classification Matrix

Predicted Group Membership TotalDefault Non Default

Count Default 43 13 56Non Default 78 323 401

Percentage Default 76.79 23.21 100Non Default 19.45 80.55 100

80.1% of original grouped cases correctly classified.

Classification Results

Number of Accounts Percentage

Type 1 Error 13 2.84Type 2 Error 78 17.06

Classification Matrix

Error Type Matrix



Graphical Back Testing

Movement of Risk Grade (NPA Account)

0

1

2

3

4

5

6

7

8

9

1999 2000 2001 2002 2003Year

Ris

k G

rad

e

• Ability of the model to signal default before the actual occurrence• Critical attribute of a robust credit risk model as a signal in advance givesthe Bank time to take precautions (sell of the asset)

Firm defaulted at this point

Model signalled default well in advance of the event



Definition of Probability of Default (PD)

� PD is the greater of� One-year PD associated with the internal borrower grade to which

that exposure is assigned, OR� 0.03% per annum

� PD of borrowers assigned to a default grade(s) is 100%



Methods to generate Probability of Default – Basel II recommended techniques

Probability of Default

Based on InternalDefault experience

Mapping to external data

Statistical Model Estimates (LR)

Every Risk Grade of the model has a unique Probability of Default



Method 1 – Internal Default Experience

Static Pool of Borrowers

RG1 RG2 RG3 RG4 RG5 RG6 RG7

RG2

RG3

RG4

RG5

RG6

RG7

RG1 0.04

RG2 0.1

RG3 0.2

RG4 0.3

RG10 0.98

Transition of BorrowerRisk Grades over Time Horizon – Transition Matrix

Probability of Default estimates



Method 2 – Mapping to external ratings

Mappings

R2 = 0.5533

R2 = 0.5994

R2 = 0.4991 R

2 = 0.5048

R2 = 0.6309

R2 = 0.631R

2 = 0.631

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10

Ratings

Ris

k S

core

s

Series1

Expon. (Series1)

Linear (Series1)

Log. (Series1)

Power (Series1)

Poly. (Series1)

Poly. (Series1)

Poly. (Series1)

Mappings

R2 = 0.5533

R2 = 0.5994

R2 = 0.4991 R

2 = 0.5048

R2 = 0.6309

R2 = 0.631R

2 = 0.631

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10

Ratings

Ris

k S

core

s

Series1

Expon. (Series1)

Linear (Series1)

Log. (Series1)

Power (Series1)

Poly. (Series1)

Poly. (Series1)

Poly. (Series1)

Mapping the Internal

Ratings to Risk Grades

of select External Credit

Rating agencies












Method 3 – Statistical Probability of Default estimates

Account LR Model Probability of Default based on the estimating equation

Calibration Scale

RG1 0.00 – 0.05

RG2 0.05 – 0.08

RG3 0.08 - 0.12

……………….

……………….

……………….

……………….

……………….

RG10 0.85 – 1.00

Calibration Scale

Average PD estimatesfor every RG

PD Table

RG1 - 0.025

RG2 - 0.075

RG3 - 0.10

……………….

……………….

……………….

……………….

……………….

RG10 - 1.00

RG -> 3 PD -> 0.1



Where does this model fit in to the IRB(F) approach

Corporate BusinessSegment Model

PD estimates

• RAROC• Provisioning• Expected Loss • Unexpected Loss• Pricing • Economic Capital for Credit Risk• Investor Transparency • Regulatory Transparence• Securitisation

Regulator

LGDEAD estimatorM



IMaCS LGD Calc – An overview

Categories of CRM

StructureCollateral

(asset)Guarantee

Haircuts and other deductions

Estimated Net Realisable Value of CRM

Claims by senior lenders & adjustments with pari passu claims

Value of CRM available to YBL

Loss Given Default



Characteristic of a good risk scoring model

� Ability of the model to distinguish “good” borrower from a “weak” borrower

� Ability of the model to “measure change” in the credit quality of a borrower on a time series

� Ability of the model to “predict defaults”



DISCUSSIONS



All the contents of the presentation are confidential and

should not be published, reproduced or circulated without the

written consent of IFC, Bangladesh Bank and IMaCS.

Economy & Finance

RMPG Learning Series CRM Workshop Day 3