The demand for more sophisticated data analytics for financial risk, liquidity, and trade quality within capital markets is at an all-time high. Firms are increasingly recognizing the critical need for data explainability, especially in the realm of traded risk, where the stakes are exceptionally high.

This demand encompasses the need to not only process vast, rapidly changing datasets, but also to extract meaningful insights about market risk and liquidity constraints.

The challenge lies in the traditional data management infrastructure prevalent in so many financial institutions. Central IT departments, often the custodians of data analytics, face a daunting task. They are required to deliver real-time, high-quality, and comprehensive data analyses, which involve complex OLAP operations on large datasets with constant refresh rates.

This has led to a bottleneck, where the quest for data explainability is hampered by limitations in technological agility and resource availability. As a result, analysts and data consumers frequently grapple with compromises in data quality, freshness, and the depth of analysis – factors that are non-negotiable in financial risk management.

The ideal of achieving actionable data explainability – where data can be not just accessed but also deeply understood and leveraged for backtesting, scenario analysis, and intraday decision-making – remains a critical yet challenging goal. In such a landscape, the question arises: how can financial institutions break through these barriers to harness the full power of their data?

To tackle these challenges head on, Opensee has launched a LinkedIn Live series focused on helping financial institutions understand how to harness the power of Explainability for their risk, trade, and finance use cases.

Data explainability applied to Value at Risk

For our launch episode, our risk data experts dive into why explainability is a must-have for Value at Risk (VaR). At a high level, VaR is just a single number, but the quality or accuracy of that number is dependent on many underlying components. To assess the accuracy of VaR and know where it’s coming from, you need to be able assess the different components of VaR, across different dimensions, on the fly.

Check out the full discussion here:

Data explainability applied more broadly to risk, trade, and finance use cases

Explainability is a core challenge for financial institutions. You are dealing with hundreds of dimensions and millions of trades that need to be aggregated at different levels. You must be able to drill into an individual component in real-time. To do this, you need a system that can manage both incredibly high volume and volatility of data.

In our next episode, we’ll dive into the problems preventing financial institutions from achieving explainability.

Don’t miss: Data Management 2.0: conquering data lake and quality challenges

Explaining risk calculations and capital exposures should be easy, but the current approach by many financial institutions is incredibly complex. The main challenge comes from poor data quality and a resistance to empower users to build data models within unified data stores that bust silos and allow for real-time, synchronous calculations on huge volumes and velocity of data.

Luckily, there is a growing base of data citizens who are looking at data consumption and explainability through this new paradigm, achieving fast time and cost to value.

Join us on February 22nd where we’ll discuss why this needless complexity exists and the transformative potential of a unified data approach.

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About the author: Emmanuel Richard is a Data and Analytics expert with over 25 years of experience in the technology industry. His extensive background includes leadership roles at industry giants and startups across the US and Europe.