This document discusses best practices for modelling exposure at default (EAD) for calculating regulatory capital requirements. It covers defining and allocating EAD, different EAD ratio methodologies, segmentation of observations, identifying EAD drivers, maximizing discrimination through nested models, and calibrating EAD estimates to downturn levels as required. The key benefits of redeveloping EAD models are identified as improved capital optimization, pricing optimization, customer and product discrimination, and compliance with IFRS 9 and A-IRB requirements.

1. Best Practice EAD Modelling
Methodologies
Credit Modelling Forum, Sydney
14th Dec 2015
1

2. Copyright © 2015 Accenture All rights reserved. 2
Background
Defining and Allocating EAD
EAD Ratios
Observation Definitions
EAD Drivers
Maximising Discrimination Using Nested Models
EAD Calibration
IFRS 9 Impairment Methodology
CONTENTS
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2
3
4
5
6
7
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3. BACKGROUND
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• EAD models are typically much less
sophisticated than PD or LGD models.
• At many Australian banks, redevelopment of
EAD models are “low-hanging fruit”, with
current models showing significant
underperformance.
• Discrimination by product type is often limited,
resulting in mispricing of risk.
• IFRS 9 compliance
• A-IRB-compliant modelling
• Capital optimisation
• Pricing optimisation
• Customer and product
discrimination
Current Status of
EAD Models:
Key Benefits from EAD
Model Redevelopment:

4. DEFINING EAD
0
1
2
3
4
5
6
7
8
Pre-Default Default Date Workout Post-Resolution
Customer Default Example
Limit Balance Balance + Guarantee EAD
EAD observed
at default
EAD observed
post-default
Non-EAD
post-default
lending
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5. DEFINING EAD
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Timing:
While EAD is the exposure at default, it is often not completely known until months or years after default. EAD is not
strictly the balance held by the customer at default:
• Guarantees issued on behalf of the customer may be called by third parties after default. Until these guarantees are
called or expire, EAD cannot be finalised.
• Additional lending made on facilities kept open on the day of default and during workout only constitute EAD if the bank
is contractually or legally obligated to provide the lending.
Allocation to Facilities:
The balance nominally recorded on a given facility need not necessarily be regarded as originating from that facility.
• It is common for balances to transfer during the year from one facility to another, such as when loans rollover,
guarantees are called and debited on overdraft accounts, and overdraft account debt is refinanced into term loans.
• For this reason the true source of the risk in a given dollar of EAD often must be attributed to different facilities.
EAD from New Lending:
For A-IRB modelling, EAD above and beyond a customer’s limits at a given observation date only needs to be captured as
EAD if they represent a risk inherent in the original limits.
• Interest may accrue on a loan before default. This EAD needs to be captured but may best be modelled as a simple
function of the loan’s interest rate and principal.
• New lending approved to a customer less than one year before default does not need to be included, but may be
included as a measure of conservatism. If not included, model performance should also be analysed for shorter time
horizons.

6. ALLOCATION OF EAD
0
2
4
6
8
10
1 Year Prior 6 Months Prior Default Date
Guarantee facility
Limit Oustanding Issuance Paid Out
EAD debited
on overdraft
facility
0
2
4
6
8
10
1 Year Prior 6 Months Prior Default Date
Overdraft facility
Limit Balance
Overdraft
balance:
Guarantee
EAD
Overdraft
EAD
• Balances at default should not always be attributed to the facility on which they are notionally
found.
• The optimal intuitive framework for allocation of EAD across facilities depends on product features.
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7. EAD RATIOS
Ratio Name Formula
Base assumption of customer
behaviour in year leading to default
Stability
Credit
Conversion
Factor (CCF)
EAD – Balance1yp
Limit1yp – Balance1yp
Borrowers attempt to draw the same
percentage of their available limits at all
levels of availability.
Highly unstable when denominator
is very small. Undefined if Limit1yp =
Balance1yp
EAD Factor
(EADF)
EAD – Balance1yp
Limit1yp
Borrowers attempt to draw the same
percentage of their total limits at all levels
of availability.
Stable when Limit1yp exists.
Loan Equivalent
Factor (LEQ)
EAD
Limit1yp
Borrowers attempt to draw up to the same
percentage of their total limits at all levels
of availability.
Stable when Limit1yp exists.
Uplift factor (%
of balance)
EAD
Balance1yp
Borrower EAD is a fraction/multiple of the
balance held one year prior to default.
Stable when a Balance1yp exists;
should not be used if customers
can redraw once loan is fully paid.
1yp: 1 year prior to default
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APS 113 specifies that CCF’s need to be calculated for each facility in an ADI’s portfolio.
• However, it does not require that CCF’s are used to model this.
• Alternative EAD ratios may be more appropriate, depending on the particular product type.
Ratio names are referred to very inconsistently in the academic literature.

8. EAD RATIOS
EAD from new limits are excluded. LEQ is assumed to
have balance/limit ratio at observation included in model
with coefficient of 1.
Different assumptions are in use about borrowing behaviour depending on the model that is chosen.
• Business considerations need to be evaluated to decide which EAD ratio is appropriate.
0
2
4
6
8
10
Observation Date Default Date
Behaviour if CCF = 50%
Limit Undrawn 20% Drawn
40% Drawn 60% Drawn 80% Drawn
0
2
4
6
8
10
Observation Date Default Date
Behaviour if LEQ/EADF = 50%
Limit Undrawn 20% Drawn
40% Drawn 60% Drawn 80% Drawn
Copyright © 2015 Accenture All rights reserved. 7
CCF
assumes
% drawn
ordering of
customers
does not
change
leading to
default.
LEQ and
EADF
assume
many-to-one
mapping of
customers
with higher %
drawn.

9. OBSERVATION DEFINITIONS
• Fixed horizon (one year) vs. cohort methods
• Ideal alignment with PD, LGD observation
dates.
• One observation per year can have seasonal
concerns vs. weighting multiple observations
per year.
• Cohort methods tend to discriminate less well.
Choice of level should depend on account
behaviour, including balance transfers.
• Customer-group level
• Product grouping level
If the level of observations is at or below the
product grouping level, segmentation of
observations by product grouping is ideal.
• APS 113: “Default-weighted EAD”, not CCF.
• Implications for accuracy of chosen ratios.
• Default-weighted ratios will tend to under-
weight customers with large limits.
• Simple limit-weighting can result in excessive
impact from a few observations.
Product features and different levels of account
management are critical concerns in
determining customer segmentation for
modelling purposes.
If segmentation results in splits in a given
customer’s facilities, EAD may need to be re-
allocated within a customer’s facilities.
Segmentation of Observations
Level of ObservationsTiming of Observations
Weighting of Observations
• Legal entity level
• Facility level
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10. EAD DRIVERS
Customer-level and group-level drivers
• Industry of customer
• Customer PD/LGD rating
• Cross collateralization arrangements
• Past default behavior.
Facility-level drivers
• Maturity of loans
• Amortization schedule
• Loan purpose
• Loan collateral type and coverage
• Percentage of limits undrawn
• Prepayments
Product group-level drivers
• Type of product
• Revolving loans vs term loans
• Trade vs overdraft
Macroeconomic and Industrial drivers
• General economic characteristics
• Industry-specific characteristics
• Bank behavior changes
1
Term Loan
Macroeconomic and Industrial state
XYZ Pty Ltd
Trade
2 1
XYZ Group
XYZ NZ
Overdraft
1
+Simplermodel
+Lessautocorrelation
+MoreVariables
+MoreDiscrimination
Variables exist at different levels of data. This can pose
problems:
• If observations are at the customer or group level,
discrimination is limited.
• If segmenting by product group or facility, insufficient
observations may exist to find higher-level variables. In
addition, these observations must be corrected for
autocorrelation.
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11. MAXIMISING DISCRIMINATION WITH NESTED MODELS
When considering observations at the product or facility level, the modeller has three choices for higher
level variables:
• Discard them, losing their potential explanatory power.
• Include them, with the appropriate correction for autocorrelation between observations from the same
legal entity or customer group. If segmentation at the product group or facility levels has occurred,
the explanatory variables may be less precisely estimated than otherwise.
• Use nested models and seemingly unrelated regressions (SUR).
Nested models allow for the benefits of product-group segmentation without the drawbacks of loss of
significance and reduced precision in the estimation of higher-level variables. Modelling the error term
using SUR further increases the precision of the regression parameter estimates.
• The three equation approach to applying nested models and SUR for EAD regressions is shown:
Key Advantage: Maximum discrimination, maximum accuracy in EAD predictions
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𝑦1
𝑦2
𝑦3
=
𝑋1 0 0
0 𝑋2 0
0 0 𝑋3
𝛽1
𝛽2
𝛽3
+ 𝑋 𝐿 𝛽𝐿 + 𝑋 𝐺 𝛽 𝐺 + 𝑋𝐼 𝛽𝐼 + 𝑋 𝑀 𝛽 𝑀 +
𝜀1
𝜀2
𝜀3
EAD ratio
(e.g. CCF,
LEQ etc.)
Three segments of
data (e.g. term
loans, overdrafts,
guarantees)
Segment-specific
variables e.g. term
loan maturity
Variables common across segments:
legal entity level, customer group level,
industry level, macroeconomic level.
SUR
error
terms

12. Downturn EAD estimation techniques:
EAD CALIBRATION
APS 113 requires calibration of EAD to downturn levels if they are more conservative than through-
the-cycle estimates:
• EAD is often lower during recessions than at other times.
• Differences between downturn EAD and through-the-cycle EAD vary by product. Guarantees are
more likely to be called during recessions. However banks are less keen to lend during
recessions, so limits are more likely to be restricted.
Cohort methods will typically yield
LEQ/EADF/CCF ratios closer to zero
than fixed-horizon methods because
cohort observation dates are typically
closer to the day of default.
Macroeconomic correlations
External data, e.g.
GCDC/PECDC data
Conservative percentiles of
existing data
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13. IFRS 9 IMPAIRMENT METHODOLOGY
Purpose of the Regulation:
The main purpose of introducing IFRS 9 is to replace IAS 39 by addressing historic issues and to harmonise with US
GAAP. Particularly, giving more timely loss recognition and more accurate information.
Phase II
Impairment methodology
Phase I
Classification and
Measurement
Phase III
General Hedge
Accounting
IAS39
• Impairment only
held against
assets which have
experienced a
loss event.
• Impairment
calculated as
Identified and
Unidentified
impairment.
• Impairment to be
held at ‘current
valuation’.
IFRS9
• Impairment held against all
assets. (Must be forward
looking.)
• Bucket 1: 12-month EAD
recognised for all loans
• Bucket 2: increase in credit
risk since initial recognition,
Lifetime EAD recognised
• Bucket 3: loans which
have experienced a loss
event, Lifetime EAD
recognised
Impaired
Not
Impaired
Bucket 3
Bucket 1
Bucket 2
IAS39 IFRS 9
• Non-Impaired Loans to be classified into Bucket 1 and Bucket 2
based on whether there is ‘Significant Increase in Credit Risk’
‘Significant Increase’
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14. Identify
Portfolio and Data
− Identify portfolio,
exposures and
sub-segments
− Gather internal
data on the
portfolio is
compiled (e.g.
performance data)
− Analyze bank
master scale with
historical data
− Analyze existing
process, models
and
methodologies,
e.g., point-in-time
adjustments,
existing rating
distributions, model
inventory, etc.
Classify
Exposure
− Classify exposure
into three Buckets:
• Bucket 1:
performing assets
• Bucket 2: Credit
Risk is increased
significantly
• Bucket 3: Credit
Losses are
incurred
− Define ‘significant
increase in credit
risk’
• Quantify PD
deterioration, if
available
• Internal watch list
• Delinquency info
• Consider forward
looking factors
Develop Dual
Calibration
− Select appropriate
calibration
methodology
based on portfolio,
model type, data
availability, etc.
− TTC and PiT
methodologies:
• Default Distance
(DD) method
• Adjustment
scalar factor
method
− Test convergence
to central tendency
Quantify
Expected Loss
Monitoring and
Reporting
− Define and design
method for
Expected Credit
Loss aggregation
− Identify KPIs for
reporting
dashboards for
business, audit,
and regulators
− Develop executive
dashboards for
IFRS 9 reporting
− Automate model
monitoring to re-
classify the
exposures on
frequent basis
Banks are challenged to comply with both IFRS9 and Basel norms given the differences in the underlying
rating philosophy implied – PiT for IFRS 9 and TTC for Basel.
Activities 1 2 3 4 5
− Lifetime EL
Methodologies
• Migration
Matrices
approach or roll
rate models
• Survival Analysis
• Regression
Model Based
• LGD x EAD
methods
− Impact by
exposure
classification,
discount rates,
modeling choice
− Review with
stakeholders
− Socialize within
bank to get buy-in
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IFRS 9 IMPAIRMENT METHODOLOGY

15. THANK YOU
Questions?
14
David Ong
Analytics Manager
Accenture Digital
david.ong@accenture.com
Melbourne, Australia
Curtis Eriksson
Analytics Senior Manager
Accenture Digital
curtis.eriksson@accenture.com
Sydney, Australia
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