Transforming Financial Institutions for Quicker Settlement Cycles

On the 28th of May, 2024, the Securities and Exchange Commission (SEC) in the United States will implement a pivotal change in the securities trading sector: moving to a T+1 settlement cycle. This shift – reducing the settlement period from two business days after the trade date to just one – is a strategic move to mitigate market volatility and diminish credit and settlement risks. However, the transition presents significant challenges for financial institutions, particularly in their back-office operations. So, what are they and how can they be overcome?

What are T+1 and T+2 Settlements?

The T+1 settlement cycle is a financial practice where transactions executed before 4:30 pm are settled on the next trading day. For instance, a trade made on Monday before 4:30 pm will be settled by Tuesday. This process entails transferring securities and/or funds from the seller's to the buyer's account, differing from the T+2 settlement, which settles trades two business days after the transaction. And, according to SEC Chair Gary Gensler, “T+1 is designed to benefit investors and reduce the credit, market, and liquidity risks in securities transactions faced by market participants.”

And so, the move to a T+1 settlement cycle is placing immense pressure on existing back-office processes for several reasons. First, the settlement cycle is a predominantly manual process, relying on batch data processing. To shift to a T+1 settlement cycle a more efficient and real-time data management approach is required, as well as access to accurate and up-to-date information to facilitate timely reconciliation and reporting. 

Secondly, with a shorter settlement cycle, there is less time for error correction, which increases the risk of settlement failures. Financial institutions must enhance their risk management practices to promptly identify and address any discrepancies in trade details. Ultimately, the existing methods are rapidly becoming obsolete and inefficient in the face of this new, expedited settlement cycle. To adapt, firms must urgently automate these manual processes and shift towards event-based, real-time processing to enable these quicker settlement cycles.

Overcoming T+1 Transition Challenges 

Financial institutions require a new approach to assist in this transition. One of the most important assets here will be tools that enable a smooth and efficient automation of existing batch processes in an Operational Trade Store (ODS) – a database that integrates data from multiple sources for operational reporting. 

At the same time, legacy settlement systems often involve manual tasks that are time-consuming and error-prone, but recent innovations in modern developer data platforms could present a solution with several advantages. 

For example, using flexible data models at the development stage can allow for a more intuitive data storage approach, accelerating development processes by reducing the need for complex data transformations or Object-Relational Mapping (ORM). At the same time, the growth of user-friendly developer platforms have also come a long way in reducing the learning curve for developers, facilitating quicker adoption. Additionally, with a rich query language, complex queries can be simplified, minimising the need for extensive coding, while a highly scalable format means platforms can manage larger volumes of trade data and high concurrency levels. 

Due to overly-complex legacy batch processing methods, the process of consolidating transaction data in back-office systems is fraught with challenges. Though they have long been the industry standard, limitations such as rigid schema, difficulty in horizontal scaling, and slow performance mean this process is no longer optimal for post-trade management. 

However, with a real-time operational trade data store (ODS) as a solution, this approach enables financial firms to consolidate transaction data in real-time, which streamlines back-office operations and enhances decision-making efficiency. For instance, trade desk data can be incorporated into an ODS in real-time through Change Data Capture (CDC). This then creates a centralised trade store that serves as a primary source for downstream trade settlement and compliance systems, fostering faster settlement times and improved data accuracy.

The potential of Artificial Intelligence and Machine Learning for Managing Trade Settlement Risks

Adopting advanced technologies like artificial intelligence (AI) and machine learning (ML), financial institutions often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. Building an ODS with a flexible schema enables them to effectively integrate AI/ML models into their trade platform to efficiently handle large volumes of trade data in real-time. This flexibility facilitates seamless integration with different AI/ML platforms, allowing organisations to adapt to changes in the AI landscape without extensive modifications to the infrastructure. Additionally, with a flexible data schema capable of accommodating any data structure, format, or source, institutions will future-proof themselves with the adaptability and agility required to face evolving technologies and regulations.

Integrating with AI/ML platforms is crucial in managing trade settlement risks efficiently, and facilitating the development of AI/ML models for more effective management of potential trade settlement failures, both in terms of cost and time. Predictive analytics further enable firms to forecast availability and demand, thus optimising inventories for lending and borrowing.

Towards Flexibility and Adaptability

As financial institutions grapple with the challenge of reducing settlement duration from T+2 to T+1, there remain viable solutions to ease a potentially bumpy transition. By automating manual processes and adopting real-time data store repositories, institutions can attain operational excellence and meet the SEC's T+1 settlement deadline. 

In the eventuality of T+0 settlement cycles, institutions need to be equipped with flexible data platforms to ensure they are better prepared to adapt to new regulations. It’s encouraging to see that many leading banks are beginning to modernise their infrastructure, leading to reduced time-to-market, lower total cost of ownership, and enhanced developer productivity.