Reevaluating Risk Management: Addressing the Challenges of Unreliable Data

By Adam Quirke, Financial Services Presales Consultant, InterSystems

In financial services, firms must perpetually assess risk, so they can seize opportunities while diminishing the potential for adverse outcomes.

Yet with risk management encompassing a broad spectrum, from credit and market risks to data governance and technology, the task is formidable. Chief risk officers and their teams must ensure their organisation maximises profitability while remaining secure, innovative, and compliant with complex regulation. It is hardly surprising that they experience high levels of frustration at the inadequacies of the systems and data they use.

The pressure is heightened in the current climate, as institutions grapple with compressed margins, intensifying competition, and the unpredictability of global political and economic events.

To move risk management forward and eliminate these frustrations, leadership teams in the financial sector must adopt more forward-thinking strategies in data management to refine risk management processes and sidestep regulatory fines.

The context is one in which regulators now possess enhanced capabilities to scrutinise the entire spectrum of data, from reporting to capital adequacy calculations. For instance, in January 2023, the FCA imposed a £7.6 million fine on Guaranty Trust Bank (UK) for lapses in anti-money laundering risk assessments. Similarly, in the previous June, ED&F Man Capital Markets faced a £17.2 million penalty for insufficient oversight and compliance in a trading operation. While the fines themselves may not be astronomical, the potential damage to reputation is significant.

A new approach

The financial industry must embrace a new approach to alleviate the problems teams experience when relying on outdated or unverified data. This dependence necessitates extensive manual rectification and verification, resulting in a lack of trust in the data across various business functions.

In the contemporary marketplace, effective risk management is critically dependent on the availability of accurate and timely data. The industry is confronted with an urgent requirement for swift insights from a variety of data sources, both internal and external, to navigate the unpredictable global landscape. Risks can arise abruptly from any business domain, customer activities, or new regulations. And yet numerous banks and financial sector firms continue to put their faith in outdated and unclean data.

The necessity for superior data quality extends across all functions, from front-office operations to settlements, compliance, and client relations, all of which require immediate data access without the intervention of specialised data scientists. The same should apply to CFOs and boards as they seek a comprehensive perspective on risk, encompassing threats, opportunities, assets, and liabilities.

Too often, data quality is addressed on a tactical level, cleaned, and harmonised for one purpose only, then re-optimised by another team for a different application. This leads to inefficiencies, added costs, delays, and risks associated with inconsistent decision-making data.

The real challenge lies in the velocity of data acquisition and its quality. Financial institutions strive to out-perform competitors by leveraging clean, standardised, and curated data for rapid and informed decision-making. They require high-quality, real-time data for risk management just as much as for investment decisions.

Conventional, manual, or ad-hoc IT frameworks are inadequate for these more demanding requirements. They fail to effectively handle the volume and complexity of data necessary for advanced analytics. The data volume is ever-growing, as are the sources and formats. Standardising such diverse data with systems that have been in use for many years is near-impossible.

Herein lies the significance of the smart data fabric concept. This cutting-edge IT architecture amalgamates various data sources into a cohesive, reliable stream, accessible almost instantaneously. This methodology not only streamlines data management but also boosts performance and eradicates redundancies by eliminating the need for multiple distinct data repositories catering to different data consumers. Concurrently, embedded analytics facilitate real-time advanced analytic processing without relocating the data.

By integrating a smart data fabric, financial institutions empower risk managers with essential information precisely when they require it. As we transition into an era characterised by open banking, open finance, and regulatory proactivity, banks, financial services firms, and asset-management businesses require timely and reliable data to safeguard compliance and risk management, ensuring they remain competitive and avoid substantial penalties.


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