FSS Workshop
Shadow Bond Approach
through Large Corp
Scorecard (LCS)
Incheol Jo
Head, Integrated Risk
SC First Bank
Contents
1.
Overview Of Large Corp Scorecard
2.
Model Development Approach
3.
Development Process
4.
Model Structure of LCS
2
Scorecard Construction
EXPERT JUDGEMENT
DEVELOPMENT
METHODS
VALIDATION
METHODS
•Selection and weighting of
factors through experts
SHADOW-BOND METHOD
GOOD/BAD ANALYSIS
•Statistical model built to mimic • Statistical comparison of
external ratings, which can
defaulted and non-defaulted
then be applied to unrated
clients to identify factors that
companies
are predictive of default
• Linear regression against
(log) PDs of external ratings
•Preferably logistic regression,
alternatively multivariate
discriminance analysis or
neural nets
•Mapping to external ratings
where possible
• Measurement of ‘matching
statistics’
• Test on hold-out sample
•Back-testing against actual
default experience/external
ratings
•Back-testing against actual
default experience/external
ratings
•Alternative: Cross validation
•Back-testing against actual
default experience
POTENTIAL
DISCRIMINATORY
POWER
• Highly dependent on quality of • High discriminatory power
expert judgment
•Limited by quality of external
• Typically not better than
rating
statistical methods
•Highest discriminatory power
possible, with a danger of
over-fitting
DATA
AVAILABILITY
•Insufficient data to develop a •Insufficient data for good/bad
statistical model (few defaults analysis, but external ratings
or external ratings available)
available for a statistically
significant sample of clients
•Sufficient data available for at
least 100 defaulted and 200
non-defaulted clients
3
LCS at a Glance
Customer Segment
Description
Model Output
Model Approval
Use
Development
& Calibration Data
Corporate with annual sales turnover above USD 375 million
Low default portfolio – Shadow Bond approach using S&P bond
ratings as basis for ordinal ranking of risk to select the most
predictive financial factors for counter-parties using 20 years of
S&P information
Scores which then translates into PD, bucketed into CG
Approval authority lies with the Wholesale Bank Risk Committee
based on recommendation from the Model Assessment
Committee
Credit Approval, Limit Setting, Credit Risk Reporting,
Risk-Based Pricing
252 S&P rated large corporate counterparties in SCB from 20012003. (LCS Version 2)
4
Contents
1.
Overview Of LCS
2.
Model Development Approach
3.
Development Process
4.
Model Structure of LCS
5
Model Development Approach
1. Identify appropriate external rating agency as default reference for data sample
2. Specify selection criteria for data sample that reflects SCB’s LCS portfolio
3. Create a list of independent variables
4. Transform each variable
5. Analyze predictiveness and correlation of individual variables
6. Short-list variables and build regression database
7. Run multivariate linear regression
8. Select the best model
9. Test stability
10. Calibrate the model
6
Overview of Data
All Large Corporate Customers with External S&P Local Currency Rating
Methodology





Data, Analysis, Calibration, Re-rating (using recent financials)
Benchmarked against S&P bond ratings
Removed ratings with parental support, sovereign cap/enhancement
307 data points used for development
Data set covering 2000 – 2003
Aim
To achieve a good R² model which fits closely to the S&P Local Currency Rating
Why S&P rating?
To find out rating of a customer’s intrinsic creditworthiness (standalone rating) and
S&P is the only international rating agency to produce a local currency rating
consistent with SCB’s framework
7
2000 – 2003 S&P Ratings used for Large
Corporate Scorecard
Development
Sample: S&P
Rated
307
Developmen
t Calibration
252
Cross
Validation
55
8
Scorecard Development Data Set
No. of Clients by Geographical Region
160
140
131
118
120
100
80
58
60
40
20
0
Europe
Americas
Asia
9
Scorecard Development Data Set
No. of Clients by S&P Rating
60
50
46
41
40
32
29
30
20
36
34
19
16
19
15
7
10
7
3
2
1
0
AAA
AA+
AA
AA-
A+
A
A-
BBB+
BBB
BBB-
BB+
BB
BB-
B+
B-
10
Model Development Approach
1. Identify appropriate external rating agency as reference for model
development
2. Specify selection criteria for a data sample that reflects SCB’s LCS portfolio
3. Create a list of independent variables
4. Transform each variable
5. Analyse predictiveness and correlation of individual variables
6. Short-list variables and build regression database
7. Run multivariate linear regression
8. Select the best model
9. Test stability
10. Calibrate the model
11
Data Sample Selection reflecting the Large
Corporate Portfolio
Selection criteria includes
1.
Existence of external ratings
2.
Only those externally rated accounts without parental support or
sovereign cap/support was admissible to the
development/validation sample
3.
Sample broadly reflects SCB’s overall portfolio characteristics in
terms of industry, size, geography, and type of counterparties
4.
Take random sample from (3) for out-of-sample validation
12
Matching SCB customers with S&P rating
13
Model Development Approach
1. Identify appropriate external rating agency as default reference for data
sample
2. Specify selection criteria for a data sample that reflects SCB’s LCS
portfolio
3. Create a list of independent variables
4. Transform each variable
5. Analyze predictiveness and correlation of individual variables
6. Short-list variables and build regression database
7. Run multivariate linear regression
8. Select the best model
9. Test stability
10. Calibrate the model
14
Financial Factor Selection
Long list of ~ 190+ financial factors

Statistical analysis of individual factors’ predictive power against
external S&P rating
Shortlist of highly predictive factors


Correlation analysis between factors
Statistical analysis to optimise weighting of financial factors
Final selection of financial factor sub-model
15
Factor Long List
16
Single Factor Analysis
Average Default Rates
100%
90%
80%
Portfolio Distribution
Average Default Rate
ADR + 1 stdev
ADR - 1 stdev
Score
70%
60%
50%
40%
30%
20%
10%
50
47
45
42
40
37
35
32
30
27
25
22
20
17
15
12
10
7.
5
2.
0
0%
17
Variable Transformation using
Logistic Function
Transformed Factor
Outliers
Outliers are
are
squeezed
squeezed towards
towards
the
the centre
centre
Largest
Largest
company
company
concentration
concentration isis
pulled
pulled apart
apart
Factor
Outliers
Most companies
fall in this range
1
ScX i 
1  Expc0  c1  X i 
Outliers
Where Xi stands for the realisation of factor
X for client i, and ScXi is the score of factor X
for client i.
18
Variable Transformation using
Logistic Function - Example
Score
1
100
80
0.5
60
0
40
20
-0.5
0
-1
-20
-40
-1.5
-2
Profit007a - Ratio
Profit007a - Score
-2.5
Score
Ratio
Ratio
-60
-80
-100
19
Model Development Approach
1. Identify appropriate external rating agency as default reference for data
sample
2. Specify selection criteria for a data sample that reflects SCB’s LCS
portfolio
3. Create a list of independent variables
4. Transform each variable
5. Analyze predictiveness and correlation of individual variables
6. Short-list variables and build regression database
7. Run multivariate linear regression
8. Select the best model
9. Test stability
10. Calibrate the model
20
Short listing Variables
Variables would be selected if it exhibits:
1) High power-statistics and R²
2) Good data availability
If the ratios satisfy the above criteria,
simple ratios are preferred over
complicated ones
Rank (1-23)
Numerator
1
EBITDA
2
Pretax Profit
PAT +
3
Depreciation/Amort Other Income
4
Pretax Profit
PAT +
5
Depreciation/Amort Other Income
6
EBIT
Denominator
Interest Charge
Total Assets
Good.
Good
Comments
Total Liabilities
Good
Net Sales
Good
Current Liabilities
Total Debt + Equity
Total Liabilities - Cash Fixed Deposits
Good.
7
Pretax Profit
8
Net Cash After
Operations
ST Debt + LT Debt
OK
9
Total Debt
Pretax Profit +
Depreciation Extraordinary Items
OK
10
11
12
13
14
15
Net Cash After
Operations
Gross Cashflow less
Interest and Dividend
Total Equity
Total Equity
Net Sales
Gross Profit (from
Trading)
16
Total Asset Growth
17
Total Equity
18
EBIT + Depreciation
19
21
Equity
Profit After Tax before
Extraordinary Items
Current Assets
22
Current Liabilities
23
Gross Profit Growth
20
Total Liabilities
Good
Total Current Liabilities
Good
Total Assets
-NAInterest Charge
Net Sales
OK
Good. Huge intangibles
OK
Good, sensitive to M&A
activity
Trade Creditors
May not be very intuitive
OK but what happens
Current Liabilities -Cash when Cash > Current
Liabilities?
Sales
May not be intuitive
-NA-
Total Equity
Average
Total Assets
Total Assets
-NA-
Average & may not be
very intuitive
Average, sensitive to
M&A activity
21
Correlation Analysis
-LN(PD)
-LN(PD)
100.0%
DCS001b_old2_incl amort
47.7%
Profit010
41.9%
DSC_EBITDA to Debt 44.4%
Liq017b
37.1%
Profit007
37.4%
DCS104
39.2%
Profit006
34.5%
DCS014_addl1
27.2%
DCS103b
29.2%
Solv006a_checkif
42.6%
DCS018
25.3%
DCS014
21.5%
Gear005
12.0%
Size2
44.9%
Profit018
24.7%
DCS001b_old2
DSC_EBIT
DCS014
Solv006a_c
_incl amort Profit010 DA to Debt Liq017b Profit007 DCS104 Profit006 _addl1 DCS103b
heckif
DCS018 DCS014 Gear005
100.0%
50.3%
77.1%
62.0%
40.9%
51.5%
61.1%
51.0%
58.7%
72.1%
42.8%
32.5%
19.7%
22.1%
17.1%
100.0%
55.9%
68.9%
68.5%
84.7%
76.4%
51.3%
32.9%
56.0%
46.5%
40.2%
25.0%
12.1%
33.2%
100.0%
67.6%
37.0%
65.7%
64.9%
54.7%
74.6%
87.8%
55.8%
35.9%
19.2%
12.2%
11.2%
100.0%
67.7%
56.8%
75.0%
72.4%
51.5%
65.1%
72.2%
59.3%
42.1%
11.8%
34.5%
100.0%
55.6%
63.5%
42.3%
18.5%
39.6%
44.9%
48.5%
34.9%
18.3%
57.8%
100.0%
67.8%
40.8%
36.4%
62.0%
38.3%
28.7%
-6.4%
2.7%
23.5%
100.0%
52.9%
45.2%
59.0%
53.9%
39.1%
26.7%
6.1%
26.1%
100.0%
54.4%
52.9%
68.9%
84.5%
34.1%
10.4%
28.3%
100.0%
63.8%
74.1%
37.0%
18.7%
9.0%
1.1%
100.0%
53.8%
40.1%
18.3%
14.0%
20.8%
100.0%
59.2%
31.0%
7.6%
26.2%
100.0%
27.9%
6.9%
35.1%
Size2
100.0%
17.8% 100.0%
22.2% 15.3%
Profit018
100.0%
Variables are selected based on correlation:


While certain factors seem to be highly predictive, the use of these
factors could sometimes turn out to be less than optimal. This is due to
the highly correlated nature with other variables.
In these cases, the use of a less predictive, but lowly correlated factor in
the final model build may provide a better scoring model.
22
Model Development Approach
1. Identify appropriate external rating agency as default reference for data
sample
2. Specify selection criteria for a data sample that reflects SCB’s LCS
portfolio
3. Create a list of independent variables
4. Transform each variable
5. Analyze predictiveness and correlation of individual variables
6. Short-list variables and build regression database
7. Run multivariate linear regression
8. Select the best model
9. Test stability
10. Calibrate the model
23
Multi-Factor Analysis

Application of multivariate regression to obtain the best
combination of factors to reflect the S&P rating (implied
S&P PD)

Selection of models




Statistically optimal models are shortlisted
Ascertain whether the models can be improved incorporating credit experts’
recommendation without losing statistical power
Choice of qualitative factors is in such a way that there will not be any
double counting with financial ratios
Stability verified through testing the scorecard on a holdout
sample
24
Multi-Factor Analysis
PD
o data
o
100%
o
o o
o
o
i
o
o
o
Score
Y
1
1  exp 0  1  X1 +  2  X 2 +  3  X 3 + .... +  n  X n 
Statistical Optimisation using maximum log-likelihood function
max ln LL   max
 0 ,..., n
 0 ,..., n
 PD
N
i 1
observed , i
 ln PD predicted, i  ( 1  PDobserved , i )  ( 1  ln PD predicted, i )
25
Multi-Factor Analysis
DCS001b
_old2_incl
_amort
Profit010
DSC_EBI
TDA_to_D
ebt
Liq017b
-0.012417
-0.012417
-0.005857 -0.004264 -0.008212
-0.005914 -0.004794 -0.008399
-0.006248
-0.00795
-0.006248
-0.00795
-0.011076
-0.011076
-0.011076
-0.011076
-0.011076
-0.011076
Profit007
DCS104 DCS014
-0.010058
-0.010058
-0.000299 -0.006466 -0.000756
-0.006134
-0.009989
-0.009989
-0.013349
-0.013349
-0.013349
-0.013349
-0.013349
-0.013349
Size2
-0.020543
-0.020543
-0.019455
-0.019412
-0.019812
-0.019812
-0.020108
-0.020108
-0.020108
-0.020108
-0.020108
-0.020108
Profit018
-0.004821
-0.004821
-0.003407
-0.003722
-0.00432
-0.00432
_LNLIKE_
-528.7525
-528.7525
-526.5282
-526.5776
-527.2535
-527.2535
-530.3955
-530.3955
-530.3955
-530.3955
-530.3955
-530.3955
METHOD
BS12
BS10
BS8
BS6
SW20
SW15
SW10
SW05
FS10
FS08
FS06
FS05
Model No.
#
Factor
2
5
8
11
DCS001b_old2_incl amort
1
Debt Servicing 1 (Int. Coverage)
22.5%
22.5%
22.5%
22.5%
Liq017b
2
Liquidity
15.0%
15.0%
15.0%
15.0%
Gear005
3
Gearing
15.0%
15.0%
15.0%
15.0%
Size2
4
Size
15.0%
15.0%
15.0%
15.0%
17.5%
17.5%
NCAO
17.5%
17.5%
RoA (or)
15.0%
DCS014_addl1
5
DCS018
Profit010
Profit007
6
Gross Cashflow (or)
NPM
15.0%
15.0%
TOTAL
100.0%
100.0%
15.0%
100.0%
100.0%
26
Discriminatory Power of Model
Measured by Adjusted Power Statistic
Cumulative Number
of Implied PD
Perfect discriminatory
power (“Crystal Ball”)
Total
Implied PD
Power Statistic =

Highest discriminatory
power: PS = 100%

Lowest discriminatory
power: PS = 0%
B
A
Realistic
model
A
A+B

No
discriminator
y power
Total
Customers
Number of customers
(ordered from worst to best)
27
Model Development Approach
1. Identify appropriate external rating agency as default reference for data
sample
2. Specify selection criteria for a data sample that reflects SCB’s LCS
portfolio
3. Create a list of independent variables
4. Transform each variable
5. Analyse predictiveness and correlation of individual variables
6. Short-list variables and build regression database
7. Run multivariate linear regression
8. Select the best model
9. Test stability
10. Calibrate the model
28
Model Calibration
Continuous function transforms the scores into a PD which
in turn is bucketed into different credit grades based on
SCB’s masterscale
1
PD
=
{1 + e (Co-efficient * Score + Intercept)}
Co-efficient
Indicates correlation of the score with the probability
of default
Score
Overall score which incorporates the aggregated
financial and non-financial scores
Intercept
Value / Intercept used as a factor of transformation /
anchor for the logistic function.
29
Model Calibration
► Scores are transformed into a PD using a mathematical function
► PDs are then bucketed into CGs using the PD ranges in Expanded Credit Master
Scale (ECMS)
Illustrative Calibration Approach
% Customers in Portfolio
Calibration Curve
PD
8%
25%
8.00%
PD Calibration Curve
7.00%
20%
6%
6.00%
PD%
7%
5.00%
5%
15%
4.00%
4%
3.00%
10%
3%
2.00%
2%
1.00%
5%
1%
0.00%
100 90
80
70
60
50
Score
40
30
20
10
0
0%
0%
1A 1B 2A 2B 3A 3B 4A 4B 5A 5B6A 6B7A 7B 8A 8B9A 9B10A10B10C11A11B11C
Credit Risk Grades
30
Contents
1.
Overview Of LCS
2.
Model Development Approach
3.
Development Process
4.
Model Structure of LCS
31
Scorecard Architecture
 Mapping PD into CRG
Financial
Factors
(e.g. size)
Non-Financial
Factors
(e.g. industry)
“Warning
Signals”
(e.g. fraud, Stale
Financials)
Financial Score
Non-Financial Score
Warning Signal
Grade Change
Size Cap/
adjustments
Standalone Rating
Parental Support/Cap
Supported
Rating
Sovereign Ceiling
Final Rating
Override
32
Financial Model
Overall Weight for Financials : 80%
Total Equity
Profit
Before Tax
Net Sales
Total Tangible
Assets
CAPITAL
STRUCTURE
Weight: 15%
EBITDA
Interest
Expense
DEBT
SERVICING
Weight: 22.5%
LIQUIDITY
Profit After
Tax+Dep/AmorOther income
(Non-cash)
Total Debt
Weight: 7.5%
PROFITABILITY
Weight: 17.5%
Financial
Model
Net Cash
After Ops
Total
Liabilities
NET CASH FLOW
Weight: 20%
Total
Equity
SIZE
Weight: 17.5%
33
Non-Financial Model
Overall Weight for Non-Financials : 20%
Industry
Risk
Weight: 60%
Qualitative
Model
Board/CEO
Governance
Weight: 40%
34
Warning Signal
S. No.
1
2
3
4
5
6
7
8
9
10
Warning Signal Type
Request for interest/repayment suspense
Internal incidence of fraud at the board level
Breach of covenants (analyst needs to specify the type of covenant)
Internal incidence of fraud below board level
External incidence of fraud
Unforced changes of auditor (for the worse)
If the auditor is not on the list of SCB-recognized auditors
If the financials do not reflect the true creditworthiness of the borrower
(e.g. “window dressing”, unconsolidated financials)
If the financials are qualified or company prepared
Timeliness of Financials
Impact
1 - 3 notches/ CRG 12
1 – 3 notches
0 – 2 notches
0 – 1 notch
0 – 1 notch
0 – 1 notch
0 – 1 notch
0 – 2 notches
0 – 1 notch
0 – 1 notch
Warning signals: Notching down mechanism
For example:
 For a standalone rating of 7A, if signal 5 (External incidence of fraud) is selected,
then user can decide to notch down by 0 or 1 notch, to either 7A or 8A.
 For a standalone rating of 7B, if signal 5 (External incidence of fraud) is selected,
then user can decide to notch down by 0 or 1 notch, to either 7B or 8B.
35
Scorecard Architecture
 Parental Support
Support Level
Unqualified Contractual
- Unconditional legal obligation
Unqualified Non Contractual
- Standard Letter of Comfort / Awareness
- Non-standard Letter of Comfort / Awareness
- Comprehensive verbal statement of support (needs majority ownership)
Qualified Contractual
- Conditional legal obligation
Qualified Non Contractual
- Non-Comprehensive Verbal Support
Support Mechanics
“P”
“P-1/P-2”
“P-1”
“S” to “S+3”
(no better than “P- 2”)
Eligibility Criteria for Parental Support:
• The borrower has to be of strategic importance to the parent company (e.g similar branding,
same name, etc)
• The borrower has to be of significant economic importance to the parent company
• If the parent and subsidiary operates in different countries, country rating of parent company
has to be CG 6B or better
36