The data model maze: Are you lost?

In the intricate ecosystem of modern finance, where every transaction is governed by a complex web of legal agreements and entity structures, data models emerge as the silent architects shaping our digital landscape.

From everyday technologies in the apps we use on our phones, to sophisticated financial systems, the significance of data models permeates every aspect of our lives, dictating how information is structured, processed, utilised and queried. The interconnected derivative agreements used today, particularly in the domain of Capital Markets, highlight the challenges of managing data models, often reaching dizzying heights of complexity, in my personal experience…

Specific derivative agreements, such as CSAs, ISDA MAs, PBAs, and GMRAs, form the backbone of the financial transactions they govern, enabling complex risk management strategies and the facilitation of liquidity across Global Markets. In the eyes of a ‘Data Modeler’ these agreements are formed of layers of intricate data structures, each bearing the weight of legal obligations and financial transactions. At the heart of these agreements lie data models, meticulously crafted frameworks that govern how data is captured, stored, and interpreted.

One of the paramount challenges faced in this domain is the daunting task of standardising data models across a myriad of legacy contracts. Unlike modern, regulatory compliant contracts designed with interoperability in mind, legacy agreements often lack uniformity, with each agreement presenting its own unique set of data complexities. From varying definitions of eligible collateral to nuanced umbrella structures containing fund-specific data, the maze of complex legal clauses (sometimes negotiated beyond recognition) poses a formidable obstacle in the quest for standardisation.

At the forefront of these challenges is the dilemma of determining the level of granularity needed to satisfy a particular requirement. Recently, it has been a ‘Dear CEO’ letter that has accelerated this requirement for both European and US based financial institutions. At Likezero, we not only enable this requirement to be met, we work with our clients to pave the way for a trusted, rich, granular data set that can be queried immediately in a market crisis event by starting with the high level structures, almost like planting a flag on a clause to come back in the future and mine for more data when you’re good and ready.

Now we enter the three dimensions of complexity;

Tables: Capturing data from tables is often the most complex aspect of these agreements, especially in the legacy world. Not only the ability to represent each row of collateral accurately, but data that sits outside an eligible collateral schedule (such as a definition for a Corporate Bond) needs to be captured. Likezero covers both.
Funds: Umbrella agreements represent another pain point in the market today. Some agreements can have over a thousand different funds, each with their own thresholds, base currencies and eligible collateral. At Likezero we are able to capture data at the lowest entity level, covering all variable parameters that will be unique for each fund.
Amendments: Legacy is… legacy. The need to move forward is absolutely critical. Likezero is not a one stop shop for capturing data from old contracts, we do this as an initial necessary exercise to pave the way for Business As Usual (BAU). Not only does this help shine a light on potential data issues from legacy contracts, it enables us to work towards an automated BAU world, where you execute a new contract, upload to Likezero and data is automatically inherited, enabling a straight-through process.

Amidst these challenges, the call for greater configurability in data models resonates louder than ever before. In a world where agility is paramount and adaptability is the key to survival, the ability to configure data models with ease is an absolute must. By empowering institutions with the tools to tailor data structures to their unique needs, the path to standardisation becomes less arduous, paving the way for greater interoperability and efficiency in Capital Markets.

As we navigate the intricate landscape of legal contracts, the role of data models cannot be overstated. From standardising legacy agreements to harmonising complex data structures, the challenges faced are as diverse as they are daunting. Yet, amidst the complexity lies an opportunity. Only by acknowledging the complexities inherent in data models can we hope to unlock their full potential and usher in a new era of innovation in Capital Markets.