Across capital markets, data modernisation has become a boardroom-level priority. With pressures like the shift to T+1 settlements, the demands of EMIR REFIT and the exponential growth in data volumes and complexity, firms are struggling to consolidate fragmented systems, manage diverse data sources, and keep pace with rising regulatory and client expectations. At the same time, ageing legacy infrastructure constrains their ability to increase operational resilience, streamline processes, and deliver timely, accurate insights at scale.
For too long, the financial services industry has operated on a foundation of systems built for a different era – a time of lower data volumes and less complex regulatory demands. Today, this aging infrastructure isn’t just inefficient; it’s a significant commercial and compliance liability.
Reconciliation legacy systems were designed for a single, specific purpose, like cash or securities reconciliations. As new regulations have come into force and new asset classes have emerged, firms have been forced to bolt on manual processes and workarounds. These fixes are costly, time-consuming, and introduce operational risk.
Consider a typical reconciliation system from 15 years ago. It was built to handle a specific format of data and were designed on simple debit to a credit for cash, and a buy to a sell for security principles. Now, if you need to reconcile new, non-traditional data streams for a regulatory report, the legacy system simply can’t handle it as the architecture is too rigid. This isn’t a minor issue; it’s an automatic blocker to innovation and, in some cases, to basic compliance.
The behavioural challenge of change
Modernisation in the financial industry isn’t just about upgrading outdated hardware and software, but rather it’s a structural and cultural challenge. Many institutions assume they can sustain current operations without overhauling their data infrastructure, overlooking the escalating costs of legacy environments. These include perpetual licensing fees, high maintenance overheads, and reliance on niche technical expertise. More critically, outdated systems constrain firms’ ability to manage rising data volumes, comply with evolving regulatory obligations, and support mission-critical functions across trading, risk, finance, and client servicing.
The most successful modernisation projects begin with a clear understanding of the total cost of ownership (TCO) of the legacy system. By demonstrating the full cost, including manual workarounds, compliance penalties, and missed opportunities, firms can build a compelling business case for change. A clear TCO analysis often reveals that the potential savings from migrating to a modern, automated platform are too significant to ignore.
A proven way to secure stakeholder buy-in is to address the most urgent pain points first, particularly where legacy systems fall short, such as adapting to new regulatory reporting requirements or handling surging data volumes and complexity of the new asset classes. By targeting these areas, firms can deliver immediate, measurable results while demonstrating how modern solutions streamline compliance, reduce operational risk, and eliminate manual, resource-intensive processes that erode margins.
Unlocking broader organisational gains
The true value of data modernisation lies in the broader organisational gains it unlocks. When done right, a modernisation project can deliver several key benefits:
- Faster time to market: With a single, flexible platform that can ingest any data format, financial institutions can launch new products and services faster without being constrained by their technology.
- Improved auditability: Automated, AI-powered solutions provide a clear, auditable trail, reducing compliance risk and easing the burden of reporting.
- Reduced dependency: A modern, user-friendly system reduces reliance on a team of technical experts, empowering business users to take ownership of their data.
While the adoption of AI and machine learning is powerful, their true value in financial services lies beyond surface-level use cases. For example, in the world of reconciliations, the biggest efficiency gains are not at the front of the process, while attempting to configure a reconciliation, but in automating the resolution of breaks and exceptions, where the bulk of manual effort, cost, and operational risk resides. By applying AI to analyse transaction patterns, generate intelligent data-matching rules and be able to resolve the identified breaks quicker, firms can drastically reduce repetitive manual steps, accelerate reconciliation processes, and redeploy skilled staff toward complex investigations, risk management, and higher-value client service.
From obligation to opportunity
Looking ahead, the industry has the ability to unlock a new level of efficiency. By modernising, firms can create a future-proof architecture that handles new data types, adapts to evolving regulations, and supports emerging technologies like generative AI.
To achieve this, we need to shift the conversation from simply managing legacy risk to using data modernisation as a catalyst for growth. By embracing this change, firms can turn a compliance obligation into a powerful engine for success.
