Financial Technology

7 Key Actions for the Data-Driven Chief Risk Officer

 



Data is increasingly important to the risk function

COVID-19 has accelerated the need to ensure that risk functions are flexible, agile and adaptable. While this trend was well under way before the crisis began, the need to understand how we use data and analytics to inform decision making is, in a rapidly evolving environment, an operational imperative. Digital and data analytics are fundamental in allowing organizations to react in uncertain circumstances and more stringent regulatory environments. Chief risk officers need to be sufficiently “digitally fluent” to understand the options available to them and provide the necessary insights and advice to their organizations.

However, data and analytics are not “silver bullets.” Their application requires careful thought and planning: asking the right questions is often harder than getting the right answers. As is the case for many senior leaders, knowing where to start when approaching the topic of data and analytics in the risk function can be daunting, especially with COVID-19 placing more constraints on time and resources.

Through many conversations with senior leaders across risk and data, Russell Reynolds Associates has created a set of seven key actions for chief risk officers who are considering their organization’s use of data. These actions are by no means exhaustive, but they form a solid platform from which a chief risk officer can build a more rigorous data strategy—creating significant impact across their organization and the wider risk ecosystem.

Actions for data-driven chief risk officers

  1. Understanding data challenges – “sweating the small stuff”
  2. Challenge data sources – look beyond the model
  3. Metadata – uncover its many uses in risk
  4. Data standards – get to grips with the challenges
  5. Boardroom – manage data expectations in decision making
  6. Risk ecosystem – take the lead in data convergence
  7. Regulatory factors – engage with the regulator on risk data
  1. Understanding data challenges – “sweating the small stuff”

    For senior executives with broad and complex portfolios, it is important to understand how their organization deals with simple data tasks. This allows them to learn where biases may arise during work, or how incorrect data interpretation can negatively influence decision making.

    Something as straightforward as quantifying a customer base comes with several questions. For instance, it’s easy to assume that deceased clients should not be counted, but for some purposes (e.g. KYC) their estates will continue to be materially important. The reason behind a query can have a significant impact on the final answer.

    Internally, basic questions like company headcount highlight major challenges. For example - does an organization count contractors? How are temporary staff accounted for?

    Understanding the “simple” data challenges provides the foundation for a chief risk officer looking to tackle more complex issues. Data capability and risk data strategy need to be constantly reviewed, taking a back-to-basics approach rather than getting caught up solely in sophisticated or exciting new modelling techniques.

  2. Challenge data sources – look beyond the model

    As digital acceleration increases and the world continues to move to online channels, businesses have been inundated with additional data, creating new opportunities. However, this also comes with added risk as more complex models are built.

    This is compounded when novel, often unvalidated data sources are used. During the COVID-19 crisis, businesses have sought additional information such as R-numbers per country or hospital bed vacancy rates. The blending of new and old methodologies in these cases can result in serious distortions, which—left unchecked—can critically damage modelling efforts and the advice management receives.

    It is therefore critical to constantly challenge data sources—to ask the tough questions about data’s provenance, including the assumptions made when cleaning, refining, and augmenting the data. This is especially the case where data has been provided by an external partner and is not immediately available in a raw format.

    Stripping back convoluted models to understand the data behind output reports can reveal “hidden simplicity”: ways in which redundant or untrustworthy data can be eliminated, leaving fewer, more manageable sources.

  3. Metadata – uncover its many uses in risk

    One of the most often-overlooked sources of information is metadata—that is, the information sitting on top of data which records its origin, including for example author notes and time-stamps.

    In the fields of risk, compliance and financial crime, elements as simple as timestamps on accounting software entries can be critical in determining trustworthiness. This is true even for sets of data that might not be used as a primary sources. Credit decisions can be greatly augmented by, for example, the longevity of a business’s social media presence versus its claimed pedigree—even if data from the social media presence itself is not used.

  4. Data standards – get to grips with the challenges

    Significant portions of the data ecosystem, including the applications of advanced data techniques—such as AI/ML— are severely hampered in many areas by a lack of clear standards.

    In some fields, this problem has been exacerbated in recent years. There are now, for example, thousands of separate ESG indices, many giving wildly different views on the same businesses. Without clear data standards, consistent definitions, metadata taxonomy and validation rules organizations will have to exercise caution in how they use and interpret this data.

    In many cases, the absence of this standardization can lead to accidental inaccuracy or even deliberate manipulation. In credit decisioning, for example, a lack of clarity around the definition of key indicators—such as marital status— can lead to inaccuracy or “gaming of the system”.

    This issue is further complicated by the increased deployment of AI and ML, where the use of badly standardized data can jeopardize the validity and application of these techniques in decision making.

    There is light at the end of the tunnel, though—we may only be a few years away from the end of the “wild west era of data.” As bodies such as (in ESG) the Sustainability Accounting Standards Board create more rigorous methodologies, we will begin to see a more systematic approach to the way in which emerging data sources are generated and classified.

  5. Boardroom – manage data expectations in decision making

    Boards are increasingly engaged in the ways in which organizations collect and use data—particularly as it relates to risk management and the uncertainty of the environment.

    This can lead to potential confusion, frustration and in some cases misunderstanding with regards to novel data sources: this has been heightened during COVID-19 when methodologies are constantly evaluated and re-evaluated, and generated data can appear to fluctuate significantly on an almost daily basis.

    Familiarizing boards with these challenges—helping them understand how data is gathered and used—is critical to allowing them to cut through the noise and fulfil their oversight and governance function.

    As a key executive in the long-term growth and stability of the business, the chief risk officer has a critical part to play in ensuring that boards understand the ways in which threats are mitigated and informed decisions are taken; this is even more critical in the often misunderstood area of data strategy.

  6. Risk ecosystem – take the lead in data convergence

    Data is the bedrock from which decisions are made. Creating a single source of truth is essential for effective decision making, while minimizing the creation of inconsistent data sets.

    It is entirely plausible, for instance, that an organization’s risk, compliance and financial crime functions might use overlapping sources of data—and yet generate, refine and use them independently from each other, with different outcomes for business units.

    This is not only inefficient, but it can create varying views of reality across a business that can lead to inconsistent decision-making. It is therefore imperative that this work is unified as far as possible, allowing functions to add value to one another’s data sets and take consistent action based on the same inputs. As a general rule, data sources should be centralized, allowing different business units and functions to conduct their own analytics using consistent information.

    As a business owner of the “risk ecosystem”, the chief risk officer can be a decisive force for unifying company-wide efforts and creating better, more efficient outcomes.

  7. Regulatory factors – engage with the regulator on risk data

    As with any method by which companies make decisions, regulators take a deep interest in data collected, stored and used by businesses.

    Tensions can be created when the data that regulators seek do not align with the data used by businesses in a practical sense. For example, many financial regulators require firms to provide historical data on recovery costs, sometimes stretching back multiple decades, into territory where data may be recorded in completely different formats and with different metrics.

    This apparently unnecessary burden creates multiple frustrations, not least of which is the difficulty in convincing commercial business units of the need to provide seemingly useless data to regulatory bodies. Worse still, these frustrations can bleed over into other areas of data strategy.

    As a key element of any company’s engagement with relevant regulatory bodies, the chief risk officer should be a powerful advocate for advising and lobbying regulators on a more productive approach - a role which also plays a critical part in aligning “data-skeptical” internal functions with the value of a consistent commercial data strategy.

The chief risk officer has the potential to be a key data strategy asset

As data becomes more and more fundamental to the role of the risk function, the pressure on the chief risk officer to play their part will only increase.

By taking the decisive steps outlined above, chief risk officers can be better prepared to add more value to the protective, mitigating and reputational aspects of their role, as well as to the commercial growth of their firm, as we continue to navigate the uncertainties of a global pandemic, emerging recession and high unemployment.

As with any change in the way that businesses operate, the proliferation of data into risk presents a myriad of dangers as well as opportunities. A chief risk officer that can adeptly grasp both will be an invaluable asset to any organization for the foreseeable future.

AUTHORS

  • MINA AMES co-leads Russell Reynolds Associates’ FinTech practice and is a member of the Financial Services practice. She is based in London.
  • JAKE STRONG is a member of Russell Reynolds Associates’ Financial Services knowledge team. He is based in London.
  • ELLEN YAFFE is a member of Russell Reynolds Associates’ Financial Services practice. She is based in New York.
  • BEIJING ZHU is a member of Russell Reynolds Associates’ Financial Services knowledge team. She is based in New York.

SPECIAL THANKS

JP RANGASWAMI is an expert in the fields of technology and data. With a background in economics and financial journalism, he is a former Chief Data Officer of Deutsche Bank and has undertaken C-Suite technology and data roles at Salesforce, BT, and Dresdner Kleinwort. JP is Chairman of the Web Science Trust, and a Non-Executive Director at Admiral, DMGT, and Allfunds.

OTHER CONTRIBUTORS

  • Group CRO, Global Bank, Europe-based ɳ Group CRO, Global Bank, UK-based
  • Group CRO, European Bank ɳ CRO, UK Challenger Bank
  • CRO, UK Digital Bank
  • CRO, UK Consumer Bank
  • CRO, Challenger Bank
  • CRO, US Consumer Bank ɳ VP Risk, US Insurance
  • Director of Technology and Risk, US Wealth Management
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7 Key Actions for the Data-Driven Chief Risk Officer