Bill Hodash, DTCC Managing Director of Enterprise Data Management
Risk management is – and has always been - a core capability of DTCC. But in the decade since the financial crisis, it has emerged at the forefront of what we do. It will play an even larger role in the future, as we face greater interconnectedness in our financial markets, new fintech innovations and the ever-present threat of cyber risk, the No. 1 issue keeping us all up at night.
Innovative areas of risk management depend on a foundation of high quality data, with strong governance, housed and accessed via an advanced data architecture. Without that firm foundation, the benefits of new technologies, including advanced data analytics, machine learning and AI, will remain out of reach.
This is why Enterprise Data Management (EDM) has become mission critical at DTCC, as part of our overall approach to risk management, which is the bedrock of our firm.
At the recent ANNA Meets the Market conference in New York, I described how the financial crisis changed the game for financial market infrastructures (FMIs), bringing into clearer focus the importance of resilience in the face of crisis and change.
While FMIs dealt very well with the stresses of the crisis, including the insolvencies of some members, most famously Lehman Brothers, regulators around the world nevertheless started to ask what might have happened if the crisis played out differently and multiple large players were allowed to fail at the same time.
In short, how resilient would the FMIs have been under those very different circumstances? The public sector determined that they may not have been as resilient.
When you study the regulations and principles adopted in recent years in response to the crisis, they read like a call to arms. For Global Systemically Important Banks, the BCBS 239 Principles aim to raise their demonstrated capability to aggregate their risk exposures very quickly, especially during the highest market stress episodes. For FMIs especially systemically important ones, the CPMI-IOSCO Principles demand more resilience than ever before, under much more extreme scenarios than those we prepared for in the past.
With those sets of Principles as reference points, DTCC has implemented a formal, multi-year EDM Program focused on data quality transparency at source for critical data elements used in aggregating risk exposures for our three Systemically Important FMIs, as part of a multi-dimensional increase in all our risk management capabilities. In short, our work is about having clear evidence of the quality and transparency of our data, and a clear path to remediating any data quality issues, which in turn has a direct impact on increasing our overall resilience to detect and recover from disruptions as efficiently as possible.
Data Management Foundation
Increasing resiliency requires carefully assessing all the traditional major risk families as well as the potential risks presented by our highly interconnected financial markets, evolving fintech technologies and the heightened cyber risk environment. And while cyber risk mitigation still focuses on prevention and detection, it is moving on to ensuring the industry has the right practical preparations in place to respond and recover from a major disruption.
DTCC has made major investments in people and technology, upgrading our risk management capabilities and all our control functions, and in recognition that data management is foundational to all this risk management we are also investing in accelerating our data governance capabilities through our EDM Program.
Through EDM, we now can prove our data quality, especially for our critical data elements that are used by our three SIFMUs in scores of important risk management applications, and in reports that are essential for our aggregation of risk exposures and protection of the industry.
In practical terms, for each critical data element that we run through the program, we profile the data regularly at a single source, run data quality rules with very high thresholds and publish the results to the whole organization. Then, just like for any other risk-related issues, if we have a data quality issue, we assess its severity and remediate it, or if truly minor and low frequency and thus deemed low impact, we might continually monitor it.
All of this has full transparency to management, governance and regulators, showing that our efforts have evolved to meet today’s expectations. Operating without that precise evidence and transparency was acceptable before the financial crisis, but not after.
It All Comes Down to Building Resilience
A robust EDM program, featuring high quality data, with strong governance, housed and accessed via an advanced data architecture is not only a critical foundation for today’s risk management capabilities, it is also required to gain the benefits of new technologies that can play a critical role in building resilience. Big-data analytics, machine learning and artificial intelligence offer a variety of benefits that can help organizations act on their data more effectively. For example, machine learning could allow analysts to look at operational and incident data to discern trends and patterns that could be indicators of future disruptions and could allow them to prevent them from materializing.
Connecting all these new technologies will enable us to create an architecture to support what we call "intelligent resilience." The foundation of this is a data lake, which would be comprised of structured and unstructured data capable of capturing all aspects of risk, built in accordance with strong enterprise data management principles, and able to take in data from the broader network.
As the industry continues to strengthen its risk management practices in response to a fast-changing environment, firms must apply a broad, system-wide view of the risk landscape, build intelligent resilience into their frameworks and continually evaluate how technology can improve their capabilities. And foundational to achieving all of that is the successful implementation of an EDM program.
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