RMPG Learning Series CRM Workshop Day 3
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Transcript of 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
IMaCS 2010Printed 11-May-11
Page 2For Classroom discussion only
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….
IMaCS 2010Printed 11-May-11
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Credit Risk Models - Some Examples
� Altman’s Z - score model (Multiple Discriminant)
� Merton model
� Judgmental
� Hybrid
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
=
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� 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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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The benefit of the model
Reduces the dimensionality of space of the credit officer
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
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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
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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
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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
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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)
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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)
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
Page 20For Classroom discussion only
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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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:
IMaCS 2010Printed 11-May-11
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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 %
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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The weightages of the various components – Dynamic Weights
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
IMaCS 2010Printed 11-May-11
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The weightages of the various components – Dynamic Weights
• 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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
Page 31For Classroom discussion only
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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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The criticality of model calibration
A Model may be powerful (able to distinguish between good and bad)
BUT
It maybe be incorrectly calibrated
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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%
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
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Method 2 – Mapping to external ratings
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Method 2 – Mapping to external ratings
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Method 2 – Mapping to external ratings
IMaCS 2010Printed 11-May-11
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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
IMaCS 2010Printed 11-May-11
Page 46For Classroom discussion only
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 2010Printed 11-May-11
Page 47For Classroom discussion only
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
IMaCS 2010Printed 11-May-11
Page 48For Classroom discussion only
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”
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DISCUSSIONS
IMaCS 2010Printed 11-May-11
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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.