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RMPG Learning Series CRM Workshop Day 3
1. Agenda for Day 3
Credit Rating Models
Lunch Break
Case Studies
Open Session/ Q&A
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 1
2. Introduction to credit risk modeling – What is a model
Risk Score = Co-eff1*Leverage + Co-eff2 *Current Ratio +…….
Co-eff6 *Integrity +….. Co-eff8 *Industry Phase….
IM aCS 2010
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For Classroom discussion only Page 2
3. Credit Risk Models - Some Examples
Altman’s Z - score model (Multiple Discriminant)
Merton model
Judgmental
Hybrid
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 3
4. 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
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
IM aCS 2010
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For Classroom discussion only Page 4
5. 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
< 1.81 - Failing Zone
Z 1.81 to 2.99 - Ignorance Zone
> 2.99 - Non-failing Zone
IM aCS 2010
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For Classroom discussion only Page 5
6. Merton Model
Expected Default Frequency - is calculated using 3 steps
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
IM aCS 2010
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For Classroom discussion only Page 6
7. Calculating distance to default: Merton
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.
in
•Amount
IM aCS 2010
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For Classroom discussion only Page 7
8. 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
Professional Judgement Check for
for weights consistency
Construct indices
Statistically explanatory set of variables
IM aCS 2010
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For Classroom discussion only Page 8
9. The benefit of the model
Reduces the dimensionality of space of the credit officer
IM aCS 2010
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For Classroom discussion only Page 9
10. Banks need different risk scoring models for different
credit segments
Corporate Small Bank
Retail Loan
Credit Business Exposures
Quality of
financial Reasonably Less Partial More
statements Reliable Reliable Information Reliable
Global, Global,
Market Regional Local
National or National or
Situation or Local
Regional regional
Type High value & Lower value & Low value & High Value &
Low Numbers Higher Numbers High Numbers Low numbers
IM aCS 2010
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For Classroom discussion only Page 10
11. 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
IM aCS 2010
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For Classroom discussion only Page 11
12. 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
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 12
13. Data Collection - What Type of Data is required to be collected
Financial Information – Balance Sheet, Profit and Loss, Cash Flow
Data
(Historical) Management
Qualitative Data
Industry
Firm Standing
Conduct of Account
IM aCS 2010
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For Classroom discussion only Page 13
14. 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
IM aCS 2010
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For Classroom discussion only Page 14
15. 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
IM aCS 2010
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For Classroom discussion only Page 15
16. The Broad Model Construction Philosophy
Phase I Phase II Phase III
Parameter Selection Modeling Technique Risk Grading
Implied probabilities
Choose Universal set of (Output of the Statistical
Limit/Filter parameters Technique)
Risk Drivers
Qualitative variables Risk Grading
Transform Parameters (by probability)
Index construction
Shortlist Predictive Adjustment for account
Parameters Statistical Technique -
Operations
(DA, LR, Probit etc)
(Modified Borrower
risk score)
IM aCS 2010
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For Classroom discussion only Page 16
17. 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
IM aCS 2010
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For Classroom discussion only Page 17
18. Choosing of predictor parameters – The art and science of it
Analysis Univariate relation of predictor parameters to default (Financial Ratios)
Aid the modeler
in answering
• The curve – Shape of the relationship between the predictor variable and
default (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
IM aCS 2010
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For Classroom discussion only Page 18
19. Forward selection process
• Start with variables with the highest univariate correlation with default
and 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 sample
performance)
The essence
of the activity
•Selection done based on suggestion of univariate power
•Validation done in a multivariate framework
IM aCS 2010
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For Classroom discussion only Page 19
20. The Broad Model Construction Philosophy
Most critical processes in model construction
Phase I Phase II Phase III
Parameter Selection Modeling Technique Risk Grading
Implied probabilities
Universal set of (Output of the Statistical
Predictor Parameters Limit/Filter parameters Technique)
Qualitative variables Risk Grading
Transform Parameters (by probability)
Index construction
Choose Predictive Adjustment for account
Statistical Technique -
Parameters Operations
(DA, LR, Probit etc)
(Modified Borrower
risk score)
IM aCS 2010
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For Classroom discussion only Page 20
21. Transformations applied to Predictor Parameters
Why is there a need to
apply transformations??
Movement of Leverage from 1-2 The idea behind applying
is not at as risky as a movement transformations is to mimic this analysis
from 2-3 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
IM aCS 2010
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For Classroom discussion only Page 21
22. The Borrower Risk Score will be adjusted for risk impact of
account operations
Financial Risk Account Operations*
Industry Risk
Borrower Adjusted
Score
Borrower Score
Management
* For existing accounts
Risk
Firm Standing
IM aCS 2010
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For Classroom discussion only Page 22
23. The monitoring parameters will be set in consultation with the
management and will be an input for deriving modified risk grade
Factors on which monitoring levels are to be set are as follows:
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
IM aCS 2010
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For Classroom discussion only Page 23
24. The weightages of the various components – Concept of
Dynamic Weights
A Linear Rating Model
40 % Financial Risk
15% Industry Risk
Borrower
Score
Management
15 % Risk
10% Firm Standing
Account
20 % Operations
IM aCS 2010
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For Classroom discussion only Page 24
25. 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
IM aCS 2010
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For Classroom discussion only Page 25
26. 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
IM aCS 2010
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For Classroom discussion only Page 26
27. 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 weights concept where the importance of every parameter
would depend on the value allotted to it by the Credit officer
IM aCS 2010
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For Classroom discussion only Page 27
28. Model Calibration – The Process
LR Output Model Output
Account 1 – 0.001
Account 2 – 0.002
Account 3 – 0.004
RG1 0.00 – 0.05
Account 4 – 0.007
………………….. RG2 0.05 – 0.08
……………………
…………………… RG3 0.08 - 0.12
………………….. ……………….
…………………… Model Calibration
……………………. Process ……………….
…………………….
……………….
……………………..
……………………. ……………….
Account 347 – 0.97
Account 348 – 0.98
……………….
Account 349 – 0.99 RG10 0.85 – 1.00
IM aCS 2010
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For Classroom discussion only Page 28
29. 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
IM aCS 2010
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For Classroom discussion only Page 29
30. Model Calibration Process – What are the guidelines of the
process
90.00% 4.0%
80.00% 3.5% There should be no
70.00%
3.0% overlap of PDs by
Probability of Default
Probability of Default
60.00%
2.5% grade
50.00%
2.0%
40.00%
30.00% 1.5%
20.00% 1.0%
10.00%
0.5%
0.00%
1 2 3 4 5 6 7 8 9 10 11 0.0%
0 1 2 3 4 5 6 7
Risk Grade
R isk Rating
45% Reduce
40% concentrations in 39%
35% any one rating
grade
Percent of Borrowers
30%
25% 23%
20%
20%
15% 13%
10%
5%
2% 2%
1%
0%
1 2 3 4 5 6 7
Risk Rating IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 30
31. Entry and exit criterion
100% 100% 1. At an operating level, an
90% 89% entry grade of RG 6 or
80% 82% better would roughly
70% 73% correspond to the credit
63% Exit
60% acceptance levels based on
50% 52% Entry risk appetite.
40% 40% 41%
30%
32% 2. The exit criteria (in case
20% 20% 27%
23% this means exiting from the
10% 11%
0% 0% 0% 9% portfolio to other banks)
0% may be set slightly lower at
RG1 RG2 RG3 RG4 RG5 RG6 RG7 RG8 RG9
RG 7
% defaults % portfolio
Relative Risk of Default
3. The monitoring intensity
1 3 5 7 10 may be set depending on the
•Gr 1 •Gr 2 •Gr 3 •Gr 4 •Gr 5 •Gr 6 •Gr 7 to •Gr 9
grades , which need to be
Strong Credit Quality
annually re-evaluated
Low Risk Green Risk scores between 1 & 3 Good quality credit
Zone
Yellow Risk scores > 3 & up to 5 No immediate concern
Zone
Amber Risk scores > 5 & up to 7 Requires intensive
Zone monitoring IM aCS 2010
•High Red NPA/ Could turn NPA
Risk scores greater than 7Classroom discussion only
For Printed 11-M ay-11
Page 31
Zone over the medium term
•Risk
32. Risk Scoring Model - the end product
NPA/ Could turn NPA
Risk over the medium term
Requires
intensive
No monitoring
Good quality immediate
credit concern
Risk Scale
1 2 3 4 5 6 7 8 9
IM aCS 2010
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For Classroom discussion only Page 32
33. The criticality of model calibration
A Model may be powerful (able to distinguish between good and bad)
BUT
It maybe be incorrectly calibrated
IM aCS 2010
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For Classroom discussion only Page 33
34. Model Validation Results- Cumulative Accuracy Profile (CAP)
Plots
CAP Plot Perfect Model
120%
Percentage reduction in NPA
100%
80%
Rating Model
60%
40%
Random Model
20%
0%
0% 20% 40% 60% 80% 100%
Percentage of Proposals accepted
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 34
35. 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
IM aCS 2010
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For Classroom discussion only Page 35
36. Classification Matrix
Classification Matrix
Classification Results
Predicted Group Membership Total
Default Non Default
Count Default 43 13 56
Non Default 78 323 401
Percentage Default 76.79 23.21 100
Non Default 19.45 80.55 100
80.1% of original grouped cases correctly classified.
Error Type Matrix
Number of
Accounts Percentage
Type 1 Error 13 2.84
Type 2 Error 78 17.06
IM aCS 2010
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For Classroom discussion only Page 36
37. Firm defaulted
Graphical Back Testing at this point
Movement of R isk Grade (N PA Account)
Model signalled 9
default well in 8
advance of the event7
6
Risk Grade
5
4
3
2
1
0
1999 2000 2001 2002 2003
Ye ar
• Ability of the model to signal default before the actual occurrence
• Critical attribute of a robust credit risk model as a signal in advance gives
the Bank time to take precautions (sell of the asset)
IM aCS 2010
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For Classroom discussion only Page 37
38. 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%
IM aCS 2010
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For Classroom discussion only Page 38
39. Methods to generate Probability of Default – Basel II
recommended techniques
Every Risk Grade of the model has a unique Probability of Default
Probability of Default
Based on Internal Mapping to Statistical Model
Default experience external data Estimates (LR)
IM aCS 2010
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For Classroom discussion only Page 39
40. Method 1 – Internal Default Experience
Static Pool of
Borrowers
Transition of Borrower
Risk Grades over Time
Horizon – Transition Matrix
RG1 RG2 RG3 RG4 RG5 RG6 RG7
RG1 0.04
RG2
RG2 0.1
RG3
RG3 0.2
Probability of
RG4 RG4 0.3
Default estimates
RG5
RG6 RG10 0.98
RG7
IM aCS 2010
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For Classroom discussion only Page 40
41. Method 2 – Mapping to external ratings
2 2
R = 0.4991 R = 0.631 Mappings R2 = 0.631 2
R = 0.5048
10
2
R = 0.5533 Mapping the Internal
9 2
R = 0.6309
8
7
2
R = 0.5994 Ratings to Risk Grades
Risk Scores
Series1
6
Expon. (Series1)
5
4
Linear (Series1) of select External Credit
Log. (Series1)
3 Power (Series1)
2 Poly. (Series1)
Rating agencies
1 Poly. (Series1)
1 2 3 4 5 6 7 8 9 10 Poly. (Series1)
Ratings
IM aCS 2010
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For Classroom discussion only Page 41
42. Method 2 – Mapping to external ratings
IM aCS 2010
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For Classroom discussion only Page 42
43. Method 2 – Mapping to external ratings
IM aCS 2010
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For Classroom discussion only Page 43
44. Method 2 – Mapping to external ratings
IM aCS 2010
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For Classroom discussion only Page 44
45. Method 3 – Statistical Probability of Default estimates
Probability of Default based
Account LR Model on the estimating equation
Calibration
Scale
PD Table
Calibration Scale
RG1 - 0.025
RG1 0.00 – 0.05
RG2 - 0.075
RG -> 3 Average PD estimates
RG2 0.05 – 0.08
RG3 - 0.10
PD -> 0.1 ………………. for every RG
RG3 0.08 - 0.12
……………….
……………….
……………….
……………….
……………….
……………….
……………….
……………….
……………….
RG10 - 1.00
RG10 0.85 – 1.00
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 45
46. Where does this model fit in to the IRB(F) approach
Regulator
LGD
EAD estimator
M
• RAROC
Corporate Business • Provisioning
PD estimates • Expected Loss
Segment Model • Unexpected Loss
• Pricing
• Economic Capital for Credit Risk
• Investor Transparency
• Regulatory Transparence
• Securitisation
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 46
47. IMaCS LGD Calc – An overview
Categories of CRM
Collateral
Guarantee Structure
(asset)
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
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only
Loss Given Default Page 47
48. 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”
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 48
49. DISCUSSIONS
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 49
50. 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.
IM aCS 2010
Printed 11-M ay-11
For Classroom discussion only Page 50