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The “Three R’s” of Preparing for Your Next ALM Exam or Audit
Influenced by increased market volatility, higher levels of balance sheet
complexity, and greater uncertainty, examiners and auditors are paying
particular attention to ALCO decision-support tools and processes. Much
of the focus in recent exams and audits has been on evaluating the assumptions
that go into the ALM model, as well as reviewing the processes and controls
now in place to minimize the potential for making bad decisions based
upon bad information.
Whether your ALM modeling process is in-house or outsourced, it is important
that you understand the process that goes into building the model, developing
the assumptions, and verifying the accuracy of the results. Not all model
processes (in-house and outsourced) produce reliable results, and examiners
and auditors need to be convinced that your bank is doing what is necessary
to ensure that your process is reliable. Effective ALM modeling requires
ongoing fine-tuning as balance sheets grow in complexity and advances
in technology provide more information and tools to support the process.
There are a number of things you can do to be best prepared for your
next exam or audit—and it all boils down to the “Three Rs.”
Reading
Preparing for the next ALM exam or audit starts with knowing
what is expected of you and your institution. A good starting point is
to review the findings from your previous exam or ALM audit. This is one
of the first places we look when we perform independent reviews, as it
provides valuable insight into an institution’s risk-management
process—and where on the priority list this process resides. If
any recommendations were made, or your institution agreed to make specific
improvements, be sure that the recommendations were considered and that
the agreed upon changes were implemented. An effective ALM process requires
ongoing care and feeding to meet the challenges posed by the ever-increasing
complexity and volatility of the banking business and, most likely, your
balance sheet. A process that does not evolve over time can lead to risks
being overlooked or even to poor and costly decisions.
Review your ALM-related internal processes and controls documentation
to be certain that it reflects your current ALM process. Too often, we
find this documentation to be outdated or incomplete, putting the institution
at risk should the model manager leave without providing guidance to a
successor. We’ve encountered instances where model results were
significantly off due to a poorly documented model that was “inherited,”
and the original processes couldn’t be maintained.
Have your internal auditors obtain and review the most recent copy of
your ALM system vendor’s internal processes and controls. If you
outsource the ALM modeling function, as many of DCG’s clients do,
have them obtain and review the most recent copy of your provider’s
processes and controls along with the software they are using. Be certain
that you understand the processes and tools used to ensure you receive
accurate and reliable results. In the case of outsourcing, examiners and
auditors want to make sure the process is accurate, independent, sufficient
for your balance sheet’s complexity, and that you have not abdicated
your responsibility to validate the results. In addition, they are looking
for evidence that you are involved in the assumption development and management
process. The bottom line is that the model and process should not be a
black box upon which ALCO relies for decision-making.
Read your policies! Confirm that they still reflect your institution’s
current operating philosophies. They will also provide a good refresher
for you so that when questions arise, you are prepared to respond.
If you have never read a regulatory guide to ALM model validation, the
OCC’s Bulletin 2000-16 (available online at http://www.occ.treas.gov/ftp/bulletin/2000-16.doc)
is a good place to start. This nine-page bulletin will provide you with
a valuable perspective on the regulators’ expectations, including
the accurate use of data, assumption validation, and reporting. The FDIC
is currently in the process of writing a supplement to the Joint Policy
on Interest Rate Risk (SR96-13) called Risk Measurement System Validation
Guidance.
Writing
A well-documented ALM process can make or break an exam or audit. The
best processes can appear deficient if they are poorly communicated to
those outside ALCO. In addition, an otherwise adequate process can be
rendered inadequate if written communication within ALCO is insufficient.
As described earlier, your ALM modeling process and controls need to
be documented and reviewed each year. This documentation should include
descriptions of the sources of data used, the process of acquiring the
data, verifying its accuracy, inputting the data into the model, and validating
the results. In addition, a description of key assumptions, including
their sources and some form of periodic review and validation, is also
an important element. Finally, backup and restoration procedures should
be included in this documentation. While not critical to the survival
of your institution, loss of a model can be costly in terms of time—not
to mention the potential opportunity forgone because proper analysis couldn’t
be performed when needed. Outsourcing doesn’t excuse you from having
to provide this documentation to your examiner or auditor; such documentation
should be obtained and reviewed annually.
Other than through the interview process, the effectiveness of ALCO’s
decision-making process can only be gleaned from your ALCO meeting minutes
and any documentation you have for potential and implemented strategies.
Be certain that your minutes describe ALCO’s discussions regarding
your institution’s current position, including liquidity, interest
rate risk, and capital adequacy and their relation to your policy’s
guidelines or limits. In addition, confirm that discussions of both potential
strategies and the status of those previously implemented are well documented.
When decisions are made, record who is responsible for their execution
and for providing a status report at the next meeting. These straightforward
elements can go a long way to providing all the stakeholders, including
ALCO, the board, auditors, and your examiner, with confidence in ALCO’s
decision-making effectiveness.
Your polices should have been updated to reflect any changes to your
institution’s operating philosophies, risk measurement/monitoring
processes, and any new regulatory guidelines or accounting pronouncements.
For example, policies can be updated to include your institution’s
added liquidity options with the Federal Home Loan Bank’s acceptance
of non-residential loan collateral, or with FASB’s recent reversal
and approval of hedge accounting treatment for Prime-based interest rate
swaps.
Arithmetic
Accuracy, reliability, and timeliness are the cornerstones of
an effective ALM process. Without these elements, an ALCO’s effectiveness
as a decision-making body is not possible. While technology has strengthened
the ability to quickly measure, monitor, and validate risk, it also has
created more complexity and optionality in balance sheets. As a result,
more assumptions are now applied to our models, increasing the potential
for “model risk.” Potential model risk is a legitimate concern
of the regulators (and of practitioners as well), and banks need to have
processes in place to verify results and substantiate the assumptions
that are used as part of the ALM modeling process.
Verifying results generally begins with a simple “back-test”
that tracks model results to current interest income and expense levels.
The level of detail that’s analyzed depends on balance sheet complexity
and the extent of historical variances. When notable variances exist,
a more detailed analysis of the assumptions and an explanation is warranted.
Given the effect that faulty assumptions can have on projected earnings
and risk, the old cliché “close enough for government work”
no longer resides in the world of ALM. In terms of validating assumptions,
experience and gut instinct now need to be empirically supported with
historical evidence. This has encouraged a whole new generation of ALM
support services, since many institutions simply can’t obtain and
use the necessary information alone or in a cost-effective manner.
The key areas examiners and auditors have been focusing on include: complex
security cash flow and valuation modeling (callable agencies and CMOs),
loan and investment prepayment forecasting, core (non-maturity) deposit
retention and rate sensitivity, and new volume replacement rate/term assumptions.
The level of validation is a function of the size and complexity of your
balance sheet. Larger banks with more complex security structures, high
concentrations in fixed-rate mortgages, and a notable non-maturity deposit
base are expected to support the assumptions with more rigorous empirical
analysis than smaller institutions with potentially less optionality.
Finding direct sources for new loan/deposit activity is often possible,
but obtaining information related to actual prepayment experience (along
with historical non-maturity deposit retention/rate sensitivity) can be
more challenging. Turning this data into usable information often is even
more difficult!
It’s important to note that all assumptions are inherently flawed.
If we could accurately forecast rates, local market activity, and customer
behaviors, none of us would be doing what we are doing. Assumptions should
be periodically tested by running alternative scenarios that can illustrate
the relative effects each major assumption has on earnings and your risk
profile. In addition, examiners and auditors have been requesting that
institutions periodically perform alternative interest rate scenarios,
including more extreme rising rate scenarios, as well as non-parallel
yield curve scenarios.
On a concluding note, we all need to be mindful of the relative cost/benefit
of any strategy or process we underwrite. While expectations certainly
have increased and advances in information technology systems have greatly
enhanced our ability to analyze the trees within the forest, it is essential
to realize that ALM is still as much an art as it is a science—and
that having an effective ALM process doesn’t require that you also
analyze the leaves.
Michael Guglielmo is a Managing Director for Darling Consulting Group,
based in Newburyport, Massachusetts.
www.darlingconsulting.com
This article may not be reproduced or distributed without written permission
from DCG.
For more information, please contact John Biestman at the Seattle Bank.
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