How to cut settlement failures ahead of EU’s 2027 changes

<p><b>With T+1 on the horizon, our 3-step process will help banks ensure their post-trade operations are ready.</b></p>

<p>European capital markets are racing toward single-day settlement for stocks and bonds in 2027. For many market participants, meeting the shorter cycle times—officially called T+1 (one business day after trade date)—requires dramatically improving their ability to detect and resolve settlement failures.</p> <p>Yet within many firms, post-trade operations aren’t ready for the challenge. Decades of mergers and quick tech fixes have left them juggling legacy systems and manual processes that worked for T+2 but could become costly liabilities under T+1. To better predict settlement failures, they’ll need a trio of tools: real-time data extraction, a centralized repository and, of course, artificial intelligence.</p> <h4>The catch-22 of post-trade modernization</h4> <p>When it comes to modernization, the post-trade process regularly takes a back seat to higher priority initiatives. Heads of operations are under constant pressure to reduce operational expenses and justify budget for tech upgrades. They face a classic catch-22: The high staffing costs required to maintain legacy systems are the very reason there’s no budget for modernization initiatives. It’s no wonder that aging post-trade systems are typically viewed as a barrier to banks’ digital transformation.</p> <p>But legacy modernization is core to banks’ IT strategies. So to make their post-trade systems more efficient and compatible, market participants are pursuing a variety of routes. Some are rehosting legacy applications on the cloud, while others re-write for a modern programming language or opt for off-the-shelf replacements.</p> <p>In the context of the European Union changes, the upgrades’ real payoff is the door they open to AI/ML solutions for predicting trade settlement failure. The AI-driven solutions use predictive analytics to provide real-time visibility into settlements and facilitate early corrective action. By proactively alerting clients and middle and back-office staff to issues like insufficient securities, these solutions prevent costly interventions such as manual file uploads, email exchanges, and upkeep of supporting satellite systems.</p> <p>The AI/machine learning solution can also help prevent failures from cascading through the trade settlement value chain. For example, an asset manager using the solution might detect a potential failure early and take action to avoid downstream disruptions at entities such as a custodian, depository or payment bank.&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>We’re already developing AI-driven solutions to predict trade settlement failures. Cognizant’s Meritsoft <a></a><a href="http://euroclear.com/newsandinsights/en/press/2025/mr-16-meritsoft-and-taskize-to-launch-next-generation-ai-service.html" target="_blank" rel="noopener noreferrer">partnered with Euroclear</a>&nbsp;on a real-time platform. Euroclear, one of the world’s largest central securities depositories (CSDs), operates a network of European CSDs. The cloud-based platform will provide real-time data and resolution across the network to support the EU T+1 settlement requirement.</p> <h4>Three steps for predicting settlement failure</h4> <p>By taking the following steps, operations leaders can prepare to reduce settlement failures—and costs—and be ready to meet the EU’s October 11, 2027, date for T+1 compliance:</p> <p><b>1. Enable real-time data extraction from core settlement and satellite systems. </b>This first step is critical, regardless of the legacy modernization approach taken. It provides the foundation for making settlement systems work together effectively. Settlement processing is driven by the central core transaction processing engine, which is in turn supported by peripheral systems such as cash management, securities lending/borrowing and repo trading. Because the systems have grown siloed over time, data sharing and extraction has become a cumbersome, labor-intensive process. Successful settlement processing depends on two things: seamless integration among systems, and extraction of relevant data from connected systems on a near real-time basis. Building data transformation “wrappers” around post-trade systems will facilitate smoother flow of data among applications and enable the creation of a data foundation for integrating with any modern technology like AI/ML.<br> </p> <p><b>2. Establish a settlement data lake. </b>To store the voluminous amounts of structured data that are collected on trade settlements, data sourcing from multiple systems needs to flow into a data repository like a data lake. The data lake is key for addressing data quality issues. It’s where data is cleansed—correcting inconsistencies and inaccuracies—and stored in logical data sets for use by modern tools. <br> </p> <p><b>3. Implement an AI/ML solution</b>. Logical data sets containing settlement data can be fed into predictive analytics software. We recommend a two-phase approach:</p> <ul> <li>Single pane of glass view for analysis of causes of settlement failures.<br> <br> </li> <li>Market participants can use settlement datasets fed into an analytics tool to holistically understand the underlying reasons for settlement failures. Repetitive and avoidable causes of failures can be viewed on a dashboard for corrective actions.</li> </ul> <p>Trade settlement failure can be predicted based on the data sets fed into the AI/ML tool. Initially, predicting the probability of trade settlement failure will be based on historic settlement failure trends, which when fed into the ML algorithm will predict the baseline probability of a failure. A feedback loop, such as pattern analysis, will feed data, such as settlement fails and their associated reasons, to refine the algorithm. With iterations, the predictions will better reflect reality. </p> <p>Global capital markets are inherently complex with varied practices, laws, systems and local regulations. With greater collaboration among regulators and market associations, there is a push toward more standardization and transparency in the capital market value chain. These actions lay a strong foundation for achieving greater economies of scale among participants. As the benefits of applying AI and machine learning become clearer, we anticipate that financial institutions will more aggressively adopt these solutions to mitigate risks and improve operational efficiency.<br> &nbsp;</p>