Risk analytics infrastructure—and even how the banking industry thinks about analytical model risk—have evolved by leaps and bounds over the last decade. In this article, Steve Maglic and Jacob Kosoff discuss transformations within risk analytics.

1. The RMA Journal February 2020 | Copyright 2020 by RMA80
BY STEVAN MAGLIC AND JACOB KOSOFF
ANALYTICS AND RISK analytics infrastructure—and even how we think about analytical model
risk—have evolved by leaps and bounds at banks over the last decade. Following the financial
crisis, the Federal Reserve Board played a key role in setting new modeling requirements1
as
well as establishing new model validation standards.2
At the same time, the banking system
has continued to undergo significant structural changes, where many non-bank participants
have entered the market and various types of fund managers have made significant inroads
to what has been traditionally banking activity. Collateralized loan obligations (CLOs) and
other structures now hold significant exposures that banks historically held on their balance
sheets. As if that weren’t enough, fintech companies have recently emerged as a significant
RETHINKING ANALYTICS,
ANALYTICAL PROCESSES,
AND RISK ARCHITECTURE
ACROSS THE ENTERPRISE
ENTERPRISERISKMANAGEMENT
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2. February 2020 The RMA Journal 81
, disruptive force and big technology names
such as Google, Apple, Facebook, and Amazon
all have their own ideas about how to become
more active in financial services. These are all
formidable threats. Innovation and technology
are true strengths of these new competitors.
At the same time they are not burdened by
legacy processes and systems as most banks
are. Therefore, given that competition is only
expected to increase, traditional banks must
rethink innovation and distinguish themselves
through their keen ability to understand risk
and regulation effectively.
Much of banks’ understanding of risk and
regulation comes down to analytics and ana-
lytical processes. In this regard, the industry
has made enormous investments over the last
decade building stress testing and Current
Expected Credit Loss (CECL) methodolo-
gies, financial crime detection models, in ad-
dition to implementing artificial intelligence
and machine learning modeling techniques.
This all comes on top of an already sizable
model infrastructure that banks use to man-
age themselves. At this point, banks need to
think about how they can use analytics much
more efficiently: how to more effectively de-
velop and deploy models, how to standardize
model development and testing, how to utilize
modern software development practices, how
to rationalize redundant analytical processes,
and how to build the environment needed to
support these activities. With every area of
banking increasingly reliant on modeling and
analytics, model efficiency and effectiveness are
going to be of paramount importance. Perhaps
a helpful way to frame the opportunity is for
banks to think about what they do in the con-
text of how a fintech or a big technology firm
would approach the challenge.
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3. The RMA Journal February 2020 | Copyright 2020 by RMA82
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because the focus is on giving better
tools to modeling teams to bring about
standardization and efficiency.
A more challenging consideration
is the models themselves and how
they work—or in some cases, don’t
work—together within a firm. In fact,
most models were built for good rea-
son with a specific use in mind, but
over time that has created overlap
with different models seemingly doing
related things. To illustrate the chal-
lenge, one may ask: How many cash
flow engines does your firm have and
can the processes be rationalized in
some way? Continuing along this line
of thinking, prepayment models and
assumptions are embedded in mort-
gage servicing rights (MSR) valuation,
CCAR/CECL processes, asset liability
management (ALM), balance sheet
valuation activities, and elsewhere.
How can redundancy be reduced or
at least consistency improved? Addi-
tionally, most banks have a variety of
default estimation models in use for
different purposes. There is a genuine
opportunity for efficiency gains by in-
tegrating these models and processes
together to improve consistency.
Perhaps the best example of model
integration is how many banks have
In particular, banks have an oppor-
tunity to re-engineer the model devel-
opment cycle and how models can be
developed and validated more effec-
tively. This comprehensively includes
how models are developed, validated,
deployed, and monitored. Taken a step
further, one can easily imagine an en-
tire model lifecycle process in which
models move seamlessly from devel-
opment to validation to deployment
within flexible multi-purpose environ-
ments. Indeed, firms across multiple
industries have started to leverage
practices that were first developed by
software development companies to
effectively redesign the model devel-
opment and validation processes. For
example, Uber develops thousands of
internal and external facing models
in the React.js language. Rather than
have each modeling team reinvent the
wheel each time, Uber’s model devel-
opers leverage Web Base—a suite of
pre-built and standardized functions.
In the same vein, banks are develop-
ing a similar set of model features
for reuse in modern libraries such as
PySpark. With well over 1,000 model
features built on common deposit and
loan data sources, many institutions
have moved to this framework. In
doing so, both wealth management
and consumer banking can leverage
the same feature repository for their
specific business needs. This makes
not only model development easier,
but also model validation because the
validation team is already familiar with
the techniques used in a prior valida-
tion of a similar model. With so much
bespoke model development activity at
each institution, there really is a need
to standardize the process and make
this all much more effective. For in-
stance, how can model development
be partially automated and perhaps
even leverage economies of scale? An
example of such a scaling effort could
be to develop similar models at once,
with all the same standardized tests
in one framework. For example, it is
common for model development teams
to develop a central feature repository
and common analytical opportunities.
Not only are the same feature sets be-
ing used, but the same model frame-
works are being leveraged to jump
start the model development process
and decrease the time to deployment.
Bulk model development and valida-
tion of models could be applied to
all time series models or all logistic
regression-based models, for example.
Alternatively, efficiencies can be gained
through standardized components that
focus on specific tests such as out-of-
sample testing or ongoing monitoring.
For example, central feature sets can
have built-in automated testing, with
unit testing around every single func-
tion that generates a feature. If a model
risk team validates this feature set and
the unit test, the stored output that is
written back to the data lake could
be validated for other analytical uses.
While the system as a whole needs
to be validated, the core components
could be reviewed by model risk man-
agement from a prior validation and
periodically reviewed as part of the
governance process. This would make
model risk management more efficient.
These challenges could be considered
more straightforward to implement
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4. February 2020 The RMA Journal 83
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repurposed stress testing models that
were originally developed for CCAR
to support the new CECL account-
ing standard for setting reserves. This
was accomplished through only mod-
est incremental efforts, as opposed to
building up an entirely separate set
of models and processes. Although
each firm has a unique analytics in-
frastructure, additional integration
opportunities certainly exist within
each firm. In many cases, reducing
model redundancy is unlikely to be
as straightforward as eliminating one
model in favor of another. At some
firms, one integration opportunity
might be economic capital and stress
testing processes that both estimate
some type of tail loss, yet rely on dif-
fering methodologies and assump-
tions. At other institutions, trading
book and banking book processes
may be disconnected and may ben-
efit from integration. As models
are used for multiple purposes, the
overhead to develop, validate, and
maintain the models can be reduced
materially. Through this moderniza-
tion, there will be greater simplicity,
transparency, and efficiency. This also
means more optimal use of quantita-
tive talent, fewer handoffs, and lower
turnaround time to produce results.
Mostimportantly,modelandprocess
integration facilitates a more transpar-
ent, consistent, and comprehensive
understanding of risk. Through this
integration, assumptions become
more aligned and the quality of results
becomes higher. Using the CCAR and
CECL example, one modeling frame-
work intuitively drives both expected
as well as stressed losses. With an
integrated model and process frame-
work, it becomes more possible to
understand how different risks work
together in your firm’s exposures. For
example, while most banks are able to
effectivelyquantifymarket,credit,inter-
est rate, and liquidity risk in isolation,
most firms are challenged to effectively
quantify how all these risks work to-
gether, especially during a crisis. At
to be a risk rating model when mac-
roeconomic dependence is turned off.
Model and process integration in-
volves both organizational consider-
ations and defining a clear operating
model. Although integration would
suggest some level of process con-
solidation, it cannot compromise the
ownership of key processes and the
perspective of critical subject matter
expertise. For this reason, banks need
an operating model that can support
some level of centralization of analyti-
cal process without compromising key
processes within a firm. For example,
both finance and risk need to individu-
ally own and direct the processes they
uniquely understand, while at the same
time stay connected to the rest of the
firm. One possible arrangement could
be an open architecture environment
whereby individual groups own spe-
cific model components of a common
framework of models that are used
throughout the firm. For example,
risk may own the credit modules of
the framework, while treasury may
own the interest rate component, but
both divisions have the benefit of the
common platform. Other areas could
be responsible for data structure and
some firms, understanding how credit
risk and liquidity risk work together
requires coordination and phone calls
between treasury and risk. In another
example, if independent models quan-
tify counterparty credit risk and credit
risk in the banking book separately, it
becomes unclear that these risks may
manifest themselves at the same time,
leadingtoincreasedconcentrationrisk.
It should be noted that not all mod-
els or processes may be appropriate
for integration. For example, an ana-
lytical process that may be suitable
for risk management purposes may
not meet accounting standards for
allowance purposes. Or, perhaps a
sophisticated prepayment model may
be needed for ALM purposes, whereas
a much simpler model may be suf-
ficient for capital planning. In some
cases, reconciliation may be favored
over full integration. In other cases, it
makes the most sense to re-engineer
two processes into one more general-
ized model or process. An example
of this would be to subsume an early
warning model and risk rating model
into one more comprehensive model
framework. Similarly, a credit loss
stress testing model can be designed
MODEL AND PROCESS INTEGRATION
THROUGH CULTURE AND GOVERNANCE
A firmwide effort to accomplish model
and process integration is critical. To this
end, model development, model valida-
tion, process owners, and stakeholders
all play a role. For example, one of the
most common issues found by model
validation teams is inconsistent assump-
tions between upstream, downstream, or
related models. Model validation analysts
at banks must ask model developers to
explain the role of their model in terms
of other related models and processes
in the firm. Comparing models is one of
the most important activities within the
scope of validation to truly understand
aggregate model risk. In this regard,
model validation staff—with its firmwide
perspective—is well-positioned to iden-
tify model and process redundancies. Ad-
ditionally, process owners, stakeholders,
and users must regularly challenge the
models with questions such as, “Why are
the prepayment assumptions in the ALM
process different from that of CCAR?” or
“How are the correlation assumptions em-
bedded in CCAR different from correlation
used in the economic capital process?”
Depending on the institution, it may
make sense to formalize the discussion
of models, model use, coordinated model
development, and model integration.
However—whether in a formal setting
or not—banks need to nurture innova-
tion by increasing model efficiency. This
discussion ties back to the culture of
an institution and the need for banks to
foster innovation to match fintech and big
technology competitors.
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5. The RMA Journal February 2020 | Copyright 2020 by RMA84
not be attributed to Regions Financial Corpo-
ration or any of its subsidiaries or affiliates,
including Regions Bank. Any representation to
the contrary is expressly disclaimed.
Notes
1. Comprehensive Capital Analysis and Review (CCAR)
2. Federal Reserve Supervisory Letter SR 11-07: Guid-
ance on Model Risk Management
design of the computational environ-
ment. Under this arrangement, own-
ership would be shared by a few key
process owners and the bank is able to
fully leverage its collective subject mat-
ter expertise. Furthermore, modeling
teams will have a common underlying
framework to ensure collaboration. A
successful operating model thus en-
sures efficiency through clear owner-
ship, coordinated model development,
and coherent analytical processes.
Since the goal here is efficiency
and effectiveness, all projects must
result in measurable improvements.
One measure is the degree to which
results can be used to support business
decisions. For example, submitting an
informed bid on a portfolio acquisi-
tion requires timely and accurate
estimates of credit quality, allowance
usage, economic capital, stress capital
allocation, economic valuation, profit-
ability metrics, and concentration limit
considerations. Involving many groups
in this process introduces significant
delay and takes more people away
from their primary responsibilities
at the bank. A clear win for a bank
is where line of business partners can
easily access second-line risk models
and results to support timely portfo-
lio and single-name transaction deci-
sions. Other examples of measurable
improvement are reducing the number
of people or the time it takes to pro-
duce CCAR/CECL results or provide
updated customer information to the
line of business.
What is described here is a rede-
sign of the analytical risk architecture,
which is a significant endeavor at any
bank. Integration work of this type
requires a deep understanding of all
the processes and a breadth of experi-
ence to understand the appropriate
tradeoffs associated with integration.
Fortunately, ideas presented here
can be addressed with newly avail-
able technology and vendor solu-
tions to facilitate these transitions.
In this competitive and burgeoning
environment, there is no shortage of
good ideas and products to move the
industry forward.
In closing, it is perhaps worth mak-
ing a comparison between the finan-
cial services sector today and retail
sector during the late ’90s dotcom
bubble, when Amazon first emerged
as a viable competitor to traditional
brick-and-mortar businesses. It was
very clear back then that the world
had changed, but it was not clear
how it would all turn out. It is clear
that banking is changing very rapidly,
and that banking is not going away.
However, it is unclear which firms will
provide banking services to custom-
ers. In this context, traditional banks
have a unique opportunity to enhance
effectiveness through analytics and
innovation, while at the same time
continuing to leverage their expertise
and competitive advantages.
The opinions expressed in this article
are those of the authors, intended for
informational purposes only, and should
SIZING THE OPPORTUNITY AND DEFINING A PLAN
There are a few ways that one can size
up the opportunity and define a path
towards improved model efficiency and
integration. Firms must use their inventory
of models and processes more strategi-
cally. Sorting models and components of
models by model output helps to identify
related or potentially redundant activities.
In addition, it is also helpful to formulate
an end-state vision of the risk analytics
architecture, including the desired func-
tionality and characteristics. From this,
it becomes clearer what must be done
today to realize the end-state vision. For
example, data sources need to be rec-
onciled, integrated, and migrated to a
centrally available environment that can
support the required computational needs.
Models and processes that need to be
able to talk to one other may be written
in different languages and may need to be
modified accordingly. Highly involved or
bespoke models and processes need to
generalized and modularized—if possible
—to work alongside other models. Given
the number and complexity of models, the
integration can only take place piecemeal
over time. Finally, with so many integrated
models, processes, and users, a robust
governance structure is critical to ensure
all components work as designed and
interdependencies are clearly understood.
A key component to successfully
realizing this goal involves getting man-
agement on board with the plan. It is
especially important that senior execu-
tives understand that this is a multiyear
endeavor, and that their dedicated sup-
port is needed over this period. Further-
more, having a strong executive sponsor
ensures that obstacles can be overcome
and that the firm can accommodate the
needed changes. In exchange for this
support, model development teams must
commit to a steady stream of milestones
and measurable deliverables, so that
management has confidence that prog-
ress continues as promised.
JACOB KOSOFF is Senior
Vice President and Head of
Model Risk Management and
Validation at Regions Bank.
He can be reached at Jacob.
Kosoff@regions.com.
STEVAN MAGLIC is Senior
Vice President and Head of
Quantitative Risk Analytics at
Regions Bank. He can be reached
at Stevan.Maglic@regions.com.
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