Regulatory scrutiny has significantly increased and has prompted banks to develop complex models at the lowest level of granularity to capture the impact of economic cycles. Segmentation is one of the first steps in establishing a quantitative basis for the enterprisewide scenario analysis of stress testing.

1. CCAR Stress-Testing
Segmentation Insights:
Account-Level Risk Segmentation and
Correlations to Macroeconomic Factors

2. 3 CCAR and Stress-Testing Segmentation Insights: Account-Level Risk Segmentation and Correlations to Macroeconomic Factors
Dodd-Frank Act Stress Testing (DFAST) requires bank
holding companies (BHCs) with assets over $10 billion to run
supervisory stress-testing scenarios to provide a forward-looking
assessment of the capital adequacy under different economic
cycles. Comprehensive Capital Analysis and Review (CCAR) is
a broader supervisory program that includes supervisory stress
testing but also assesses a BHC’s unique stress-testing scenarios.
The purpose of CCAR is to assess whether the largest BHC’s
operating in the United States have sufficient capital to continue
operations during times of economic and financial stress and to
ensure that they have robust, forward-looking capital-planning
processes that can account for their unique risks.
CCAR regulatory guidance requires financial institutions to
establish a quantitative basis for enterprise-wide scenario analysis.
However, the guidance does not prescribe the use of particular
quantitative approaches to estimate losses, revenues and expenses
across the industry, leaving BHCs to select the approach that best
fits their type of portfolio and level of complexity.
On the other hand, CCAR guidance provides clear expectations
and a common ground for how losses, revenues and expenses
are calculated. The guidance states: “While the Federal Reserve
does not require BHCs to use a specific estimation method,
there is the expectation that each BHC should estimate its losses,
revenues and expenses at sufficient level of granularity so that it
can identify common, key risk drivers and capture the effect of
changing conditions and environments.1
”
Segmentation plays a critical role in determining the right level of
granularity for stress modeling. Practically speaking, the objective
of segmentation is to define a set of sub-populations that,
when modeled individually and then combined, rank risk more
effectively than a single model tested on the overall population.
Stress-testing modelers understand that finding the right level of
granularity to segment the bank’s portfolio represents one of the
first steps in establishing a quantitative basis for the enterprise-
wide scenario analysis of stress testing.
1
Board of Governors of the Federal Reserve System. Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice, p. 19, August 2013.
Regulatory scrutiny has significantly increased following the financial crisis. The previous
lack of attention to low-probability, high-impact events that could strain a firm’s capital
adequacy and spread to other financial institutions prompted banks to develop complex
models at the lowest level of granularity to capture the impact of economic cycles.

3. What is segmentation?
Segmentation was originally a marketing strategy used to
aggregate prospective buyers into groups — or segments — that
have or are perceived to have common needs, interests, priorities,
or buying behaviors, and designing and implementing strategies
to target them. In other words, they are homogeneous. Market
segmentation enables companies to target categories of consumers
who perceive the value of certain products and services differently
from one another. This allows organizations to design customized
marketing campaigns and customer treatments.
Segmentation approaches have also been successfully applied in
many different fields including economics, business, finance and
risk management.
Granular segmentation and its application to stress tests
Granular segmentation is a key requirement for meeting the
expectation that BHCs are able to identify risk and forecast
change under different economic scenarios. To accomplish this,
modelers will need to identify relevant homogeneous segments
or sub-portfolios that are sensitive to similar risk drivers and are
consistently correlated to comparable macroeconomic factors
over time. When effectively selected, these smaller segments, or
cohorts, should be sensitive to material changes in risk exposure
or portfolio characteristics over the planning horizon.
Good segmentation yields greater modeling granularity and
captures risk drivers with more precision. Segmentation
provides the foundation for banks to develop models that meet
regulatory expectations to estimate revenues and expenses at a
sufficient level of granularity so they can identify common risk
drivers and capture the effects of dynamic economic conditions
and environments.

4. 5 CCAR and Stress-Testing Segmentation Insights: Account-Level Risk Segmentation and Correlations to Macroeconomic Factors
Models used for stress-testing purposes should account for
portfolio changes under different credit cycles by considering
correlations with macroeconomic variables. An example of
using the right level of aggregation to determine correlation
is the application of loss-estimation methodologies to retail
and commercial portfolios. Typically, bank’s retail portfolio is
composed of credit cards, personal loans, home equity lines of
credit and consumer mortgages.
When different sub-portfolios are aggregated together, the
assumption is that they will create a homogeneous segment, while
in reality each sub-portfolio exhibits a different probability of
default (PD). As a result, the combined retail portfolio will show
correlations to macroeconomic variables that are less intuitive
than when each sub-portfolio is modeled separately.
Granularity is also a requirement for a bank’s commercial
portfolio. For example, commercial real estate — one of the
largest material components of a commercial portfolio — should
use granular segmentation schemes that capture specific risk
drivers, such as geography and property type, and risk measures,
such as loan-to-value and coverage ratios, debt service and net
operating income.
The correlations with macroeconomic variables are stronger
at the granular level, where the sub-portfolios exhibit similar
loss rates under the same credit cycle. Banks often develop
their models at a more aggregated level and thus get less
reliable results. These models typically represent the first
generation of stress-testing models, with weaker correlations to
macroeconomic variables.
Multifactor approach to granular segmentation
An effective approach to identifying homogeneous sub-segments
with a higher level of predictability is to use multifactor
segmentation. This method differentiates risk better than the
traditional segmentation scheme using a single risk characteristic.
Multifactor segmentation approaches are based on a combination
of quantitative and qualitative characteristics. For example, once
a risk score is assigned to an account (e.g., PD), a multifactor
segmentation methodology will first group accounts based on
similar risk scoring and then apply a second level of segmentation
using qualitative characteristics such as geography, industry,
products, past due date, etc.
Granular segmentation challenges
BHCs adopt multifactor segmentation approaches to forecast
losses, revenues and expenses that correspond with the size and
diversity of their portfolios. In doing this, institutions of different
sizes confront different technical challenges.
The development of a dynamic multifactor segmentation
approach involves data-intensive modeling work that starts
with scoring the lowest level of granularity (e.g., account level).
However, for most institutions, developing account-level models
is a difficult undertaking for three reasons: (1) a lack of access
to granular account-level data, (2) the need to use sophisticated
modeling software and data processing technology, and (3) the
need for more advanced modeling techniques.
Typically, CCAR BHCs — with heterogeneous customer
bases and product portfolios — have many potential segment
combinations. This makes it very difficult to identify the right
number of segments to account for the heterogeneity of
their portfolio.
While most of the large CCAR banks have achieved a high
level of sophistication in their modeling approach, some have
developed complex quantitative models using a large number of
predictive parameters without sufficient data to support the level
of granularity of the modeling framework.

5. On the other hand, the vast majority of DFAST banks tend to
identify a limited number of segments. Many of these institutions
use top-down methodologies to identify the correlation with
macroeconomic factors at the portfolio level. Even those
DFAST institutions that use a segment-level approach tend to
use segments that are not granular enough to identify portfolio
changes. These organizations find themselves managing at the
portfolio level as they are unable to identify the relevant cohorts
that drive the behavior of the entire portfolio.
Thus, many CCAR and DFAST banks struggle with determining
the right level of segmentation to achieve sufficient granularity in
their models. To determine the right level of segmentation, banks
should consider:
• The ideal level of segmentation is achieved when the
strength of the correlation of a sub-segment with the
macroeconomic variables is strong enough to drive results
for the entire portfolio.
• The level of granularity of the segmentation is determined by
the granularity of the data.
Multifactor segmentation for a credit card portfolio
Many CCAR institutions that have adopted advanced account-
level modeling face different segmentation challenges because they
need to aggregate accounts into relevant, homogenous segments.
Multiple statistical techniques are available for segmenting
a population. Traditional techniques rely on using a single
characteristic to define a segment. However, the use of multiple
characteristics yields more predictive models. For example,
credit card companies segment their portfolios based on vintages,
paying behaviors (i.e., transactors vs. revolvers) and type of credit
card product.
Leading credit card companies have developed account-
level models at the lowest level of granularity by applying
the following segmentation design as the foundation of their
modeling process.
1. Model PD at the account level using competing hazard
survival models.2
Examples of hazards include contractual at
180 days, bankruptcy, deceased and attrition.
2. Aggregate accounts into relevant segments based on common
PD scores or a similar type of risk score.
3. Apply a second segmentation logic approach using qualitative
factors such as ZIP code, age and education.
4. Apply other aggregation criteria, such as buying behaviors
or vintages.
Finding the right number of relevant segments is an art and requires
trial and error combined with sophisticated modeling and data
mining techniques. Each segment’s level of granularity will depend
on the business objective. This hybrid approach can be illustrated
using the example of a hypothetical credit card portfolio.
2
Hosmer, David W.; Lemeshow, Stanley; and May, Susanne. Applied Survival Analysis: Regression Modeling of Time to Event Data, 2nd edition, John Wiley & Sons Inc., March 2008.

6. 7 CCAR and Stress-Testing Segmentation Insights: Account-Level Risk Segmentation and Correlations to Macroeconomic Factors
Conclusion — takeaways
1. Granular segmentation is a key element for meeting the
CCAR/DFAST regulations that require banks to identify
relevant segments or sub-portfolios that are sensitive to
similar risk drivers and are consistently correlated to similar
macroeconomic factors over time.
2. Many CCAR and DFAST banks struggle with how to
determine the right level of segmentation to enable sufficient
granularity for their models.
3. For most institutions, developing account-level models is a
difficult undertaking that is negatively affected by three
main factors:
– The lack of access to granular account level data
– The need to use sophisticated modeling software and data
processing technology
– The need for more advanced modeling techniques
4. Multifactor segmentation approaches based on a combination
of both qualitative and quantitative risk characteristics produce
better segmentation and a higher correlation to risk drivers.
5. To determine the right level of segmentation, banks
should consider:
– The right level of segmentation is achieved when
the correlation between a sub-segment and the
macroeconomic variables is strong enough to drive the
results for the entire portfolio.
– The level of granularity of the segmentation is determined
by the data’s granularity.
• The adoption of more granular segmentation carries a set of
implications to multiple areas including:
– Data management
– Reporting
– Model validation
– Audit traceability
– Scenario building
Multidimensionalsegmentation
Vintage
Segment-level PD
Midmarket
FICO
PD PD PDPD PD PD PDPD PD PDPD PD PD PD
Vintage
Product
segmentation
ZIP code
Transactors
Account-level PD modeling
Vintage
Product
segmentation
Partner
Revolvers
Vintage
Product
segmentation
Macroeconomic
modeling...........
...........
Segment-level PD Segment-level PD Segment-level PD

7. Contacts
Ilieva Ageenko
Managing Director
Financial Services Advisory
Leader, Model Risk Management
T +1 704 632 6820
E Ilieva.ageenko@us.gt.com
Paul Makowski
Managing Director
Financial Services Advisory
T +1 240 463 0550
E paul.makowski@us.gt.com
Nigel Smith
National Leader
Financial Services Advisory
T +1 212 542 9920
E nigel.smith@us.gt.com
Segmentation and the Pareto rule
The Pareto rule, also known as the 80/20 rule, can be applied in risk
management for portfolio management, where 20% of the cohorts
usually generate the highest concentration of risk exposure under
stress. The identification of relevant sub-populations that are sensitive
to macroeconomic factors — and subsequently have a higher
propensity to generate exposure — becomes one of the most critical
tasks of the risk identification process. Many questions need to be
answered: What sub-segments/sub-portfolios are driving the largest
levels of exposure in the portfolio? What sub-segments are more
sensitive to macroeconomic factors under different economic cycles?
Banks should take a closer look at their segmentation approaches.
The Pareto rule applies to most portfolios where the correlation
between certain sub-segments and macroeconomic variables is
strong enough to drive the correlations for the entire portfolio.
Identifying these smaller sub-populations can have a major impact,
as management actions can become more effective by targeting the
sub-segment with appropriate pricing actions.
How Grant Thornton can help
Grant Thornton’s Model Risk Management Center of Excellence
has extensive industry experience in model development,
implementation, validation and use. We have professionals with
business and risk/regulatory acumen and advanced quantitative
modeling knowledge to address the risks that arise from model
use. We can also provide deep insights into analytical solutions
pertaining to risk modeling (credit, market, operational, AML,
etc) and CCAR, DFAST, Basel I, II, and III, Solvency II.

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