For most large retail and commercial banks, the regulatory requirements underpinning FATCA and CRS are no longer the primary concern. The rules are well understood. What is now emerging as the central challenge is something far more difficult to solve: the operational model used to deliver compliance at scale is fundamentally broken.
According Label, customer tax data is not static. It is collected across multiple touchpoints, updated through ongoing account activity and affected by a continuous stream of lifecycle events.
Label recently discussed why FATCA and CRS compliance is becoming a structural problem for banks.
In banking, the sheer volume and frequency of these changes creates a data management challenge that periodic review processes simply cannot keep pace with. Maintaining accuracy requires near real-time consistency, not annual reconciliation.
Yet many FATCA and CRS compliance frameworks have evolved around legacy processes rather than addressing the underlying problem. Data collection remains incremental, validation is frequently delayed and changes in customer circumstances are typically caught through scheduled reviews rather than as they occur. These approaches remain functional, but the inefficiencies they introduce become increasingly visible as customer volumes grow.
The assumption at many institutions is that the root cause is incomplete data collection. In practice, the issue is more structural. Banks are generally collecting the right data. The problem is that it is gathered across disparate interactions, validated inconsistently and not maintained in line with the rate at which it changes. At scale, even a one or two per cent error rate across a large customer base can generate thousands of exceptions, each requiring individual follow-up, validation and resolution. Over time, operational effort migrates away from maintaining data quality and towards managing the fallout of issues that have already materialised.
This dynamic has given rise to a compliance model where remediation is no longer an exception — it has become an embedded fixture of the annual reporting cycle. Large-scale outreach campaigns, documentation requests and data reconciliation exercises are routinely deployed to close gaps before submission deadlines, often with the support of external providers. While these efforts are effective in ensuring that reports are completed, they do not address the underlying model. Instead, they create a recurring cycle in which problems are identified late, resolved in bulk and then resurface the following year. Rather than reducing over time, the cost and effort associated with remediation stabilise at a persistently high level.
What makes this particularly acute is that the model does not scale efficiently. As customer volumes increase, so do exception volumes, while response rates from outreach activity tend to decline and operational costs continue to climb. Customer experience also suffers. Mass mailings and static documentation forms are misaligned with how banking customers typically interact with their institutions, leading to low engagement and repeated follow-up contact.
The pressure is unlikely to ease. As reporting frameworks continue to evolve — including the ongoing expansion of CRS and the introduction of new regimes such as CARF — the volume and complexity of data that banks are required to manage will only grow. For institutions where current operating models already struggle to maintain consistency at scale, additional requirements are more likely to amplify existing inefficiencies than resolve them.
What is now emerging among leading institutions is not simply an upgrade in tooling, but a more fundamental shift in how FATCA and CRS compliance is delivered. Rather than relying on periodic remediation cycles, forward-looking banks are beginning to validate data at the point of interaction, monitor changes as they occur and collect information through digital, integrated customer journeys. In these environments, compliance becomes continuous rather than cyclical, and reporting becomes a direct output of well-maintained underlying data rather than an exercise in retrospective correction.
For banks still operating remediation-based models, the path forward is unlikely to yield materially different outcomes regardless of how much resource is applied. The opportunity lies not in scaling the existing approach, but in rethinking how customer tax data is collected, validated and maintained across the full customer lifecycle.
Read the full Label post here.
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