The countdown has started. On May 28th, 2024, the US will undergo a major transformation in its securities market with the implementation of T+1 settlements. This means instead of the current two-day (T+2) settlement cycle, trades will be finalised and funds exchanged just one business day after execution. While seemingly simple, this change has significant implications for investors, institutions, and the entire financial ecosystem.
Understanding the shift
Previously, settling a trade involved exchanging cash for securities across two business days. This time lag carried inherent risks, creating the potential for defaults and exposing participants to financial volatility. The move to T+1 aims to reduce these risks by speeding up the process, freeing up capital faster and enhancing overall market efficiency.
With the T+1 settlement countdown, the pressure is mounting for financial institutions to adapt. While the benefits of faster settlements are undeniable, the operational hurdles are substantial. This is where automation steps in, offering a powerful solution to navigate the complexities of T+1 with increased efficiency, accuracy, and agility.
Understanding the landscape
- Identify bottlenecks:Â Analyse your current trade lifecycle, pinpointing areas prone to delays, errors, and manual intervention. These bottlenecks become prime candidates for automation.
- Compliance is key:Â Familiarise yourself with the DTCC’s “Hitting 90% Affirmation by 9:00 PM ET on Trade Date” report, outlining key benchmarks for achieving T+1 success. This report sets the bar for trade affirmation and allocation deadlines, crucial for timely settlement.
Automation recommendations
Pre-trade and trade allocation
- Leverage electronic communication channels:Â Utilise platforms like TradeSuite ID to automate trade confirmations and affirmations, facilitating faster data exchange and minimising human error.
- AI-powered matching engines:Â Implement AI-driven solutions to expedite the matching of trade details and allocations, ensuring accuracy and eliminating the need for manual reconciliation.
Corporate actions and settlement instructions
- Automated corporate action processing:Â Integrate tools that automatically identify and process corporate actions, reducing manual intervention and potential delays.
- Standardised settlement instructions:Â Adopt industry-standard formats for settlement instructions (e.g., FIXML) to facilitate seamless communication and minimise errors.
Real-time monitoring and exception handling
- Automated exception management systems:Â Deploy automated systems to flag and manage settlement exceptions promptly, enabling faster resolution and improved efficiency.
- Predictive analytics:Â Utilise machine learning to anticipate potential exceptions based on historical data, allowing for proactive intervention and smoother settlements.
Additional considerations
- Data quality:Â Ensuring accurate and timely data availability is crucial for successful automation. Invest in data governance initiatives and data cleansing processes.
- Change management:Â Prepare your team for the shift towards automation. Invest in training, communication, and support to ensure smooth adoption and user buy-in.
AI and RPA: The perfect partners
While automation offers a broad framework, specific technologies like AI and RPA provide the muscle for T+1 success:
- Artificial Intelligence (AI):Â Machine learning algorithms can identify patterns and anomalies in data, automate complex tasks like exception handling, and even predict potential issues before they arise.
- Robotic Process Automation (RPA):Â RPA bots can automate repetitive tasks like data entry, reconciliation, and trade confirmation, freeing up human resources for more complex activities.
The combined power of AI and RPA can deliver a comprehensive automation solution for T+1:
- AI-powered exception handling:Â AI can analyse trade data and identify potential exceptions, automatically triggering corrective actions or routing them to human reviewers for faster resolution.
- Automated corporate action processing:Â RPA bots can automate tasks like dividend distribution, stock splits, and mergers & acquisitions, ensuring timely and accurate execution.
- Predictive analytics:Â AI can analyse historical data and market trends to predict potential settlement delays, allowing proactive measures to mitigate risks.
Manually managing these tasks in a T+1 environment is not only inefficient but also prone to errors. Automation, however, offers a compelling solution:
- Increased efficiency:Â Automating repetitive tasks frees up human resources for more strategic tasks, boosting overall productivity.
- Enhanced accuracy:Â Automation reduces human error and ensures data consistency across the entire process.
- Improved speed:Â Automation speeds up processes significantly, enabling faster settlements and quicker access to capital.
- Reduced costs:Â By minimising errors and manual work, automation can lead to significant cost savings in the long run.
Navigating the implementation
While the benefits are clear, implementing automation for T+1 requires careful planning and execution:
- Identify automation opportunities:Â Analyse current processes and workflows to identify tasks that can be effectively automated.
- Choose the right technology:Â Select AI and RPA solutions that align with your specific needs and infrastructure.
- Change management:Â Prepare employees for the changes automation brings and ensure their understanding of the new workflows.
- Data governance:Â Establish robust data governance practices to ensure data integrity and security throughout the automation process.
Automating T+1 settlements is not just a trend; it’s a necessity. By leveraging AI and RPA, financial institutions can navigate the T+1 transition smoothly, reduce risks, improve efficiency, and ultimately unlock the true potential of faster settlements. In fact, as the T+1 deadline nears, we should explore automation solutions, start planning our implementation roadmap, and embrace the future of faster, more efficient, and automated settlements.
