27. March 2025
Revolutionizing Trade Efficiency: Harnessing Ai To Streamline Global Transactions
The Trade Lifecycle Process: Harnessing Gold-Copy Data and AI for Enhanced Efficiency
Accuracy and efficiency are paramount in today’s fast-paced and interconnected trade lifecycle. The current end-to-end process relies heavily on accurate data at each stage, with the Investment Book of Records (IBOR) system playing a crucial role in ensuring that trade, position, and cash data match the custodian and accounting book of records (ABOR) system. However, this dependence on manual processes can lead to inefficiencies, noise, and uncertainty for front-office teams.
The current addressal and resolution of non-STP executions remain highly manual, with stakeholders comparing data points across multiple systems, beginning the arduous process of identifying discrepancies and rectifying them. This manual process is not only time-consuming but also prone to errors, which can have significant consequences in the trade lifecycle.
The advent of AI and event-driven updates offers a beacon of hope for enhancing efficiency in the trade lifecycle process. By leveraging gold-copy data and AI, we can automate many of the tedious tasks involved in data scrubbing, validation, and updates, thereby reducing the likelihood of errors and increasing the speed of processing.
Creating a Gold-Copy Event: The Foundation of Efficiency
In most cases, stakeholders create a “gold-copy” event once they’ve “scrubbed” data from multiple sources and created an accurate, up-to-date copy of the event that will occur. This process is ideal in many ways – scrubbing multiple sources ensures there’s less chance of an incorrect feed from a single vendor, creating process gaps. However, this manual process can be time-consuming and prone to errors.
To address this challenge, we need AI to undertake this process continuously. IBOR systems should, at minimum, be subscribed to two or more vendors from whom data should be retrieved. Any change to the dataset should be continually updated through a push or pull API mechanism.
A New Paradigm for Data Sharing and Validation
In this new paradigm, data vendors supplying feeds to IBOR systems will feed this information automatically once the required minimum data point details are populated. IBOR systems will create the security within their data systems, ensuring that any mismatches across vendors are reviewed by a user, and appropriate values chosen (if deemed necessary).
As updates occur in the market, these changes should be captured and updated in the IBOR system. Downstream applications that leverage this application will automatically flag a security market update and the impending event-driven update, informing users that the dataset they’re seeing may be stale compared to external processes that may be receiving up-to-date data.
To protect against inaccurate data from a single vendor, only datasets consistent across all vendors should be updated automatically. Data updates from a single vendor only will prompt a user to review and approve. Once underlying securities are updated, this will be considered an ’event’, driving event-driven updates that greatly reduce the number of manual touches downstream users need to make for inaccuracies identified upstream.
Mitigating Concerns: Materiality/Tolerance, Timing Differences, and Data Capacity
While harnessing AI and event-driven updates offers numerous benefits, there are several concerns worth discussing:
Materiality/Tolerance: Securities can undergo immaterial changes from time to time that may have little to no impact on upstream and downstream processes in the trade lifecycle. A set of fields and tolerances should be identified to flag in case of market updates (core dataset). If updates occur on these specific fields and they’re outside of the existing tolerance, IBOR systems should consume the updates provided by vendors.
Timing Differences: While IBOR systems may have up-to-date data, external participants (e.g., banks or fund accounting systems) may continue to leverage stale or outdated datasets. There should be an audit history available of the core dataset’s historical data; in other words, if a bank/fund accounting system refers to any of these audit datasets, an automatic note should be sent to the external participant informing them of stale data and to recheck against external market vendors.
Data Capacity: The use of AI will undoubtedly increase data consumption and storage costs, particularly when maintaining audit history for at least five years as required by law in many jurisdictions. Expanding core dataset updates with tolerance should help manage some of this capacity requirement.
The Future of Trade Lifecycle Efficiency
Despite the concerns highlighted, harnessing AI is valuable to design and implement across the trade lifecycle process and would be substantially more valuable than the costs that would likely be incurred. The universe extends far beyond public securities to private securities with much less high-quality data, making leveraging AI increasingly essential for both universes.
As the investing world transitions to increased investments in private securities, we can expect this trend to continue across both universes. With AI at its core, the trade lifecycle process is poised for significant improvements in efficiency, accuracy, and speed, paving the way for a brighter future in global finance.