By Will Thomey, Co-Head of Business Development, Acadia
In today’s financial markets, organizations face the daunting task of managing vast amounts of trading documentation, a complex undertaking which underscores the urgent need for a more streamlined approach. Legal documentation forms the backbone of financial markets, essential towards promoting transparency, reducing risk, and protecting the rights of the involved parties.
Trading documentation, which includes both legal and operational data, significantly impacts various internal and external entities, such as counterparties, custodians, and Central Counterparties. All these participants have a vested interest in maintaining clean and accurate data. However, the industry commonly fails to capture and operationalize this data properly, necessitating change.
Moreover, many firms have maintained relationships and corresponding documents for years, if not decades, evolving significantly over time. The combination of the breadth of legal data, its fluidity over time, and the duration of these relationships results in a large and complex surface area to manage.
RISKS OF DISPARATE LEGAL DATA
Financial firms are complex in structure, and data is a key component in improving and simplifying the operating environment. Organizations should not store legal data in disparate systems for multiple reasons, but often, this is the case, and there is no golden source of legal data. What are some of the key reasons to avoid falling into this trap?
- Economic: Trading often requires data from various systems and infrastructures, therefore ensuring that all are updated with the latest and most relevant information becomes challenging. Data which is not captured, inaccurate, amended, or expired can lead to errors in trade pricing and discounting, directly impacting the trading desk’s P&L.
- Risk Management and Decision Making: Disparate systems make it challenging to obtain a unified view of agreement data across the organization. This lack of visibility limits the ability to generate accurate and comprehensive reports, hindering critical insights and strategic decision-making. Manual data transfer and synchronization efforts increase the risk of errors and inconsistencies.
- Operational Integration: Operations should work from the same legal data used within trading, risk, and other departments within the firm. Inaccurate legal data in the operating environment is likely to increase the volume of disputes and discrepancies, potentially leading to capital impacts. Relying on multiple legal data sources increases operational risk and the potential for loss.
- Governance: Ensuring consistent data governance practices is challenging without a centralized system. Without a golden source of legal data, enforcing data standards and definitions can be difficult, and document organization is likely to be haphazard. Tracking amendments and maintaining a clear audit trail for data modifications remains difficult and elusive without digitization.
Modernizing trading markets through a centralized and integrated data storage system can help mitigate these issues and streamline agreement management processes, addressing key business and regulatory concerns while improving decision-making. Having a golden source repository is essential for both legacy documents and new documents and amendments. It is crucial to understand everything from simple operational parameters to the complex optionality embedded in documentation to adequately mitigate and manage risk within your business.
What does this mean for the buy-side?
As is often the case, the sell-side may well have a more complicated time when it comes to regulatory oversight and the sheer volume of documentation that they contend with. Despite that, given the inter-connected nature of financial markets, spillover is inevitable whenever there are issues. In the time since the global financial crisis, the evolution of margin and collateral across the market has required the buy-side to become more sophisticated in terms of how they think about collateral.
Buy-side firms often benefit from agility when utilizing and improving data. Where alpha generation is concerned, being fast and first has always been the case. The metrics that lead to cost inefficiency are on the radar and more critical than ever.
Buy-side firms must consider:
- Data Consolidation: Consolidating legal data from various sources and systems to ensure accurate pricing, risk, reporting and decision-making.
- Data Analysis and Insights: Leveraging advanced data analytics to deliver alpha opportunities. Extending this approach will enable them to enhance returns further, especially in relation to margin costs.
- Data Management: Establishing robust data governance frameworks to ensure data integrity, accuracy, and compliance with regulatory requirements.
Maintaining the status quo hampers efficiency and agility within and across business functions and the wider industry. Buy-side firms can benefit from centralizing legal agreement data and improving the data quality to enhance profitability, risk management, operational capabilities, and decision-making. Even if the buy-side has a smaller challenge relative to a Global Systemically Important Bank, they are by no means immune to the challenges of managing legal agreement data themselves while also ensuring that the banks/brokers they transact with are correctly representing and using this shared legal agreement data (minimizing the aforementioned risks of incorrect pricing, valuation, disputes, etc.).
WHAT ARE FIRMS DOING TO TACKLE THE ISSUE?
Progressive, forward-looking firms are tackling the legal data issue head-on. Many have recognized that having a golden source of legal data across trading businesses is essential for pricing, risk management, and collateral management. While this has long been understood, we are now witnessing the evolution of legal data being gold-plated, further enhancing its value and reliability.
As with all data projects, building a robust data model is a critical starting point. Over the past decade, most organizations have adopted a higher degree of common standards, such as standard legal agreement templates and standard codes and identifiers. However, these standards are often not fully adopted or comprehensive.
Most data projects can be categorized into digitizing old legacy documentation or executing new documentation within a more digital framework. For legacy documentation, digitization providers with varying degrees of Optical Character Recognition sophistication are working to build capabilities that use scanned copies to translate documentation into a data model. These tools represent a significant advance from fully manual digitization but still often struggle with language nuances, bespoke documentation, and complex tables. Many firms are now exploring the use of Machine Learning and inserting aspects of Large Language Models (LLMs) to further improve digitization accuracy. Thus far, the technology is promising, but still cannot be fully entrusted and thus requires manual review and/or manual digitization to plug known limitations.
For new documentation, tools are available to enable the negotiation and execution of documents within a more natively digital framework. These tools are expected to grow in use, but the legal profession has been resistant to changing the way documentation is written, negotiated, and executed. Regardless of the type of data project, all require consistency across data libraries and definitions, including the data capture process, coverage model, and data accuracy.
As we look towards the future of financial markets, a unified approach to managing legal data is not just a competitive advantage but a necessity. Regulatory bodies increasingly emphasize the need for firms to streamline their data management processes to ensure sound risk and liquidity management practices across the organization. For buy-side firms, the benefits of consolidating legal agreement data, deriving actionable insights, and establishing robust data governance frameworks cannot be overstated.
The time to act is now to improve data integrity and accuracy, which will ultimately lead to enhanced profitability and operational capabilities within the industry.
