Balancing risk in banking is a continuous endeavour, with institutions striving to optimise opportunities while mitigating potential downsides.
In such an intensely competitive and highly regulated arena, effective risk management relies on the availability of precise, verified, and timely data. Risk management teams need quick insights from a broad array of data sources, both internal and external.
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Risks to profitability, compliance, or new products can suddenly arise from any number of business areas, from customer actions, or from new regulatory measures. To make the right calls every time, banks should not be relying on outdated, inaccurate data – but all too often they are. They are hampered by data and systems that are barely up to the job.
And the problem is that standardising and validating data is becoming much tougher. The scope of risk management has expanded to cover capital regulations, credit, market, data governance, and technology. The information sources are diverse, the data is in different formats and constantly growing in volume. Neglecting any of this information is no longer an option. It must be cleaned up, verified, and harmonised so banks can extract insights from it in near real-time.
Systems and processes that drag down data quality
Yet much of this preparatory work is beyond the capabilities of the veteran systems so many institutions use. In the absence of a better approach, preparation is extremely challenging, demanding significant investment in the time of highly skilled people.
What happens is that teams in banks conduct data quality management at the tactical level. Data is cleaned and harmonised for one purpose before being reworked by another team for a different use. This is inefficient, increases costs and delays, and injects risk into decision-making, rather than reducing it. The upshot is banks run risk management operations using out-of-date data.
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By GlobalDataLoss of innovation
This jeopardises compliance with the complex web of financial regulation. But it also inhibits agility. An important element in a chief risk officer’s role is to ensure their bank is fully able to innovate, can adapt to changing conditions, and outpace the competition. Chief risk officers and their teams must guide their organisations along the path to increased profitability while remaining fully compliant.
The rapid growth of embedded finance is a good example of developments that are changing the risk landscape. Embedded finance presents risks relating to credit, liquidity, interest rates, and operations. The applications may run on technology built by fintech companies that have limited experience in risk management. Banks still clearly need to protect customer data, facilitate authentication and authorisation, and create visible audit trails. They need to monitor all these risks effectively so they can build revenues without jeopardy.
The necessity for a better approach to data management
The task of managing all these proliferating factors continues to be ever-more challenging in a volatile world.
The time has come for the financial sector to adopt innovations, such as the smart data fabric architecture, so they can streamline their operations and boost compliance. Regulators now have unprecedented authority to examine all facets of data, from financial reporting to capital adequacy assessments. For example, the UK FCA imposed a £7.6 million fine against Guaranty Trust Bank (UK) in January 2023 for anti-money laundering assessment failures. This followed the £17.2 million fine against ED&F Man Capital Markets in June of the previous year for compliance shortcomings. Though these fines are modest for financial institutions, the impact on a firm’s reputation can be profound.
The challenge lies in the speed and quality of data acquisition. All areas of a financial organisation need fast access to high-quality data, from front-office to settlements, compliance, and client relationships. CFOs and board members alike demand a detailed view of risk, encompassing threats, opportunities, assets, and liabilities.
Unifying and validating risk management data
This is where the concept of the smart data fabric becomes invaluable. This innovative IT architecture integrates disparate data sources into a unified, dependable stream, accessible in real-time. Data management is far more efficient, improving performance and eliminating the need for maintenance of separate data repositories for different users. Additionally, embedded analytics allow for real-time, advanced analytic processing directly within the data environment.
The adoption of smart data fabric technology enables financial institutions to provide risk managers with the necessary information at the right time. It is the most effective of the new approaches and the least disruptive.
As the era of open banking, open finance, and proactive regulation dawns, financial institutions must rethink how they support their risk management operations. The smart data fabric is the one sure way to access timely and accurate data, to achieve compliance, and give the organisation the confidence to innovate and excel in all areas.
Adam Quirke is Financial Services Presales Consultant, InterSystems
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