By: Dilip Kumar and Yang Shim,
principals, and Kiet Pham, executive director in the Financial Services Office
of Ernst & Young LLP
So it begins. Over the next 18
to 24 months, large global banks face another wave of deadlines for compliance
with a host of new regulations governing a wide range of business areas that
will take effect in stages through 2015 and beyond. The new rules cover such
areas as proprietary trading, OTC derivatives, capital management, resolution
and recovery planning, and consumer protection, requiring banks to report a
vast array of information that most are not currently equipped to provide. To
comply, banks must locate, verify, aggregate, format, and report data from all
parts of the organization, a challenge for their currently fragmented systems
and corporate structures.
For most banks, pulling
together all this data in time to meet the deadlines will take a great deal of
work, in part because many institutions are organized as a patchwork of
business units, subsidiaries, and separate legal entities. Communication among
these units is often complicated by issues of confidentiality and conflicts of
interest; some maintain arm's-length relationships with one another. As a
result, many of these units maintain their own information systems and
operating protocols.
To meet new mandates, however,
banks will have to rethink their corporate structures and make IT investments
to bridge these divisions, a move that will require hard choices in the current
turbulent economic climate. Forward-thinking institutions, however, will take a
positive approach. Instead of seeing a revamp of their IT architecture as a
regulatory exercise, banks can seize the opportunity to create efficiencies and
gain new business insights by enabling a horizontal rather than a vertical
approach to information management. This information convergence strategy can
break down data, technology, and organizational silos to achieve transparency
and consistency in reporting for regulatory, risk management, and business
performance purposes.
Add to
the bottom line
By streamlining and
standardizing the way they produce, handle, and store their data, banks can
rationalize IT departments and operations to reduce costs. For example, one
system might serve multiple functions, such as risk management, treasury, and
finance. Reorganizing not only data structure, but also the people, resources,
and processes that support them, will help executives get a more comprehensive,
global view of positions, exposures, and capital allocation, which can help
reduce risk and strengthen business decision-making.
Moreover, banks can use this
aggregated, horizontal data to gain insight into their client bases firmwide,
potentially spurring growth and new business development. In breaking down the
barriers among their business silos to enable the flow of data, banks will, at
the same time, enhance their analytical capabilities, driving innovation and
creation of new products, cross-sector fertilization of new ideas, and
increased cross-selling. Using data mining techniques to improve customer
service and segmentation can also improve profitability.
Traditionally, for example,
various lines of business might have used different data identifiers or
definitions for the same client. Standardizing these identifiers makes it
possible to aggregate information on that client on a global and
enterprise-wide basis. Management can see total risk exposures to a single client,
how much regulatory capital its business with that client really consumes, and
what return it gets from that risk and capital.
As they rush to meet rapidly
approaching deadlines, banks may be tempted to patch together quick fixes that
address each new regulation separately, in the process creating new silos of
information. Unfortunately, these short-term expedients do not make for a good
long-term solution, especially when addressing overlapping regulatory demands.
But the opposite approach-overhauling
the firm's information architecture completely to build a centralized data
warehouse-may also be unfeasible for most banks. While a data warehouse might
seem ideal, few institutions will have the appetite to invest the resources and
the effort to make it a reality. For firms with multiple lines of business, a
one-size-fits-all approach may not even be possible. Moreover, the sheer size
and complexity of such a project may doom it to many delays.
Divide
and conquer
Finding the middle ground is
likely the best strategy for most institutions: creating a new data
architecture using a modular approach. This approach decouples the major
components of the data architecture project-data sourcing and cleaning, data
modeling and integration, data governance, data reporting and data storage-so
that progress on one does not depend on completion of another. While this
solution still requires considerable investment, it allows banks to make the
best use of the systems and data they already have.
Modular, however, does not mean
piecemeal. While based on components, this solution requires a global
assessment of an entire institution's current state and lays out a roadmap
toward the ultimate strategic goal. It combines shorter-term tactical steps
with holistic, long-term strategic solutions.
This approach enables an
institution to leverage its existing infrastructure and reporting tools,
building them into the new architecture. It also helps create an assembly line
model of sorts for the overall project. As a group responsible for a component
task like data cleansing figures out how to accomplish this within a business
line or division, it creates, in effect, a repeatable process that it can use
again elsewhere-or that a group working on a different component can apply to that
same division.
Dividing the project into these
more manageable chunks should show tangible benefits in the near term. It also
permits particular people or groups to take ownership of each component and
have real accountability for it, which are crucial to making such a modular
approach work. Ownership and accountability, in fact, must start at the top.
Develop
an enterprise information strategy
Across the industry there has
been a significant increase in C-level data management roles to drive greater
responsibility and accountability for enterprise data programs. Regardless of
the title, an enterprise information strategy is critical to achieving a
horizontal view of data. A well-defined strategy should enable banks to
prioritize near- and long-term investments and help break down organizational
silos.
To maximize the return of
regulatory-driven investments, banks must analyze each new regulation, along
with business growth objectives, laying out timelines for implementation and
the status of individual rulemaking for each piece of regulation. This will
make it possible to set priorities and identify overlapping data requirements
across regulatory reforms. Next, they must assess the institution's current
state which should catalog existing data resources and systems, identify gaps
and weaknesses-including manual procedures-and evaluate data quality and risk
control. This assessment will show where the bank must make changes in order to
meet the new requirements and where these changes are most urgent.
The current state assessment
will provide the information necessary to develop an enterprise data management
target operating model that addresses governance, management, quality,
architecture, and usage. The model should also identify key support partners
including information security, infrastructure, change management, risk, audit,
and compliance. The target architecture should be flexible enough to adapt to
future regulatory reforms and business expansion, while storing data in an
efficient way that enables users throughout the organization to access it
easily.
Finally, banks must create a
roadmap toward the target architecture that prioritizes investments according
to not only regulatory deadlines, but also on the importance of, and the impact
on, each business. Under this plan, the bank can begin implementing tactical
plans for near-term compliance concurrently with strategic plans for long-term
optimization of its global data management. The benefits will extend to
business performance and management reporting. If the firm does not embed
efficient data management into the way it operates as a whole, it will not be
able to remain compliant-or to realize the bottom-line benefits.
Look
forward
While it may be a daunting task
for a bank to prepare itself to meet so many overlapping demands from
regulators for accurate, comprehensive and standardized information, in the
long run the institution will benefit tremendously from overhauling its data
architecture. A bank can analyze its data more quickly and
more deeply if it is centrally stored and logically structured.
The results will give bank
executives a much better handle on overall risk and where the bank's costs and
profits really come from, enabling them to make better decisions. Improving
management of global banks and financial institutions is a goal important not
only to regulators, but to shareholders and stakeholders throughout the
financial system.
Six
action items
· Develop an enterprise data management target
operating model to break down organizational silos and prioritize near-and
long-term investments.
· Rethink organizational structure to support this
more horizontal view of the business and its data.
· Create an information management framework to
converge existing data platforms and make certain that all future information
management design takes into account the need for unified data.
· Consider not only regulatory demands, but also
business objectives and benefits in constructing a new data architecture.
· Divide the data architecture project into
component tasks that can proceed semi-independently and make the best use of
current IT and organizational assets.
· Create a strategic roadmap for implementing the
overall project that prioritizes these component tasks to meet regulatory
deadlines and optimize business value.
###
The views expressed herein are
those of the authors and do not necessarily reflect the views of Ernst &
Young LLP.
[This article was posted on November 20, 2012, on the website of ABA
Banking Journal, www.ababj.com.]
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