The Automation Bottleneck: Why Data Still Holds Back Digital Transformation

Despite the progress made in financial services over the last decade, accomplishing automation at scale remains out of reach for many firms. Though the industry has no shortage of ambition or investment, it lacks the data infrastructure needed to support real digital transformation.

Though essential, data readiness is often sidelined in favor of automation, which tends to take precedence in most strategy conversations. This misalignment is the crux of the problem. When firms try to automate workflows built on messy or piecemeal data, they typically end up with more manual work than they started with, and any hopes of meaningful transformation fall by the wayside.

Partial Digitization Creates New Manual Burdens

The assumption that any degree of automation represents progress must be challenged. In reality, partial digitization leads to fragmented processes, more copy-paste operations, and entrenched dependency on exception handling teams.

Take trade reconciliation as an example. A firm might automate the matching logic but still rely on manual data capture from PDFs or emails at the start of the process. As a result, the reconciliation engine spends more time flagging false breaks than resolving real ones. Meanwhile, the team becomes mired in cleaning up bad inputs instead of moving along the value chain.

It’s a failure for which data readiness, or rather the lack thereof, is primarily responsible. Automating the wrong part of the process, or doing it in the wrong order, exacerbates the very friction firms are trying to eradicate. And when teams are battling to recalibrate inconsistent formats and legacy inputs, efficiency isn’t the only thing that’s thwarted. Any ability to scale suffers the same fate.

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Legacy Integration Gaps Undermine Progress

Even in firms with well-funded digital agendas, legacy system sprawl is an ongoing headache. Data lives in silos, formats vary between regions and business units, and integration efforts can stall once it becomes clear just how much human intervention is involved in daily operations.

Elsewhere, the promise of straight-through processing clashes with manual workarounds, from email approvals and spreadsheet imports to ad hoc scripting. Rather than symptoms of technical debt, these gaps point to automation efforts that are being layered on top of brittle foundations.

Until firms confront the architectural and operational barriers that keep data locked in fragmented formats, automation will also remain fragmented. Yes, it will create efficiency in isolated functions, but not across end-to-end workflows. And that’s an unforgiving limitation in capital markets where high trade volumes, vast data flows, and regulatory precision are all critical.

Why Industry-Specific Platforms Matter

Recognition is spreading that generic data platforms can’t solve the complexity of financial services workflows. The logic underpinning margin calls, reference data validation, or corporate actions processing is specific to the individual business models within it. Retrofitting generic automation tools to these processes can easily lead to ballooning implementation timelines or fragile configurations that require constant support. Neither outcome aids transformation.

What does drive progress are purpose-built platforms that understand the shape and structure of industry data from day one, moving, enriching, validating, and reformatting it to support the firm’s logic.

Reinventing the wheel for every process isn’t necessary, but firms do need to acknowledge that, in financial services, data transformation isn’t some random back-office task. It’s a precondition for the type of smooth and reliable automation that prepares firms for the stark demands of a digital future.

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Thankfully, it’s a precondition that most capital markets firms are taking seriously. A recent report produced with CRISIL Coalition Greenwich found that nearly 60% of firms have already taken steps to improve data capture to unlock the full power of automation. However, the same report also revealed that 62% still process up to a quarter of their data manually, highlighting a clear significant gap between intent and execution.

Where Firms Are Seeing Real Wins

Perhaps surprisingly, the strongest digital transformation results aren’t driven by firms with the flashiest AI tools or biggest cloud budgets. Instead, they’re coming from firms investing in clean, consistent data capture at source, thereby eliminating manual bottlenecks at critical points.

For example, streamlining custodian data ingestion invariably results reductions in exception volumes and an easing of the burden on back-office teams. Simultaneously, improving data capture during client onboarding can dramatically cut downstream remediation work. While outcomes vary, these steps consistently deliver some of the biggest efficiency gains in data-intensive operations.

There are no shortcuts. Improving data quality can be challenging and standardizing legacy inputs takes time. Yet the payoff is fewer workarounds, faster process times, and systems that, rather than degrade over time, evolve and improve.

Transformation Starts with Data, Not Software

Modernization efforts often begin with cloud migrations or AI pilots. Yet without the foundation of clean, structured, and reliable data, such efforts are unlikely to deliver their full potential. Automation technologies are effective, but when implemented without first addressing data quality, they inevitably encounter operational bottlenecks.

About the author: Dan Reid is the Chief Technology Officer and an original founder of Xceptor. Based in the UK, he is responsible for driving the vision, architecture and development of the Xceptor platform.