Ask a chief data officer (CDO) at any major bank what keeps them up at night, and the answer is rarely the sophistication of their AI models. It is the data underneath them, and specifically, knowing where it comes from, what has happened to it along the way, and whether they can prove all of that to a regulator on demand.
Data lineage, the ability to trace every data asset through its full lifecycle, from origin to transformation to consumption has long been recognised as foundational to risk management, compliance and reporting. The problem has never been understanding its importance. The problem has been building it fast enough, at the scale modern banks require, to actually be useful.
That is the problem agentic AI is now uniquely positioned to solve.
The lineage gap is a regulatory liability
Banks have never faced more pressure to demonstrate data traceability. BCBS 239, the global standard requiring banks to improve the accuracy and timeliness of risk data and reporting, has been in place since 2016. However, supervisory reviews consistently find that large institutions are still struggling to meet its requirements in full.
DORA, the EU’s digital operational resilience regulation now in force, adds further obligations around documenting and evidencing data flows across critical systems. And the EU AI Act introduces new requirements for organisations using AI in high-risk contexts to trace decisions back to authoritative, auditable data sources.
The common thread across all of these frameworks is lineage as in, who owns the data, where it came from, how it was transformed, and whether it can be trusted. Regulators want the full chain of evidence. The challenge is that building that chain manually is a slow, expensive and ultimately losing battle. Complex bank data estates span hundreds of systems, thousands of data flows, and decades of accumulated technical debt. Manual lineage mapping takes months per project. Documentation becomes outdated almost as soon as it is written.
Compliance gaps are not exceptional events, they are structurally inevitable when the pace of change in the data estate outstrips the pace of documentation.
Where agentic AI changes the equation
Agentic AI is not simply a faster version of existing automation. Unlike rule-based tools or earlier generations of machine learning, agentic systems can reason across tasks. They can plan sequences of actions, and operate autonomously across complex environments while maintaining the human oversight that regulated industries require.
Applied to data lineage, this changes what is possible in three concrete ways.
First, it shortens the time required to build lineage. An agentic system can work simultaneously across technical and business domains, stitching lineage across systems, mapping physical data to business terms, and enriching metadata at a speed no manual team can match. A process that previously took months can be reduced to days or weeks and crucially, it remains current, because the agent can continuously monitor and update lineage as the data estate evolves.
Second, it extends the reach of lineage into territory that has historically been impossible to cover systematically. Legacy PDFs, spreadsheets, images, and undocumented institutional knowledge hold enormous amounts of critical data context that never makes it into formal lineage maps. Agentic systems can ingest and interpret this material, creating queryable, interactive lineage that reflects the full reality of how data flows through the organisation.
Third, and perhaps most importantly for regulated institutions, it does this while generating a complete, explainable audit trail. Every action the agent proposes is visible to human reviewers before it moves to production. Teams interact through natural language, tracing upstream dependencies and sensitive data flows without needing to write code or navigate complex interfaces.
What this looks like in practice
Consider a bank preparing for a BCBS 239 review. Historically, this would mean months of manual effort with data owners across the business tracing reporting chains, reconciling conflicting documentation, and trying to produce a coherent picture of data flows across systems that were never designed to be connected. The result is often incomplete and the moment the review is over the documentation begins drifting out of date.
With agentic AI handling lineage, that same bank can instead maintain a continuously updated, queryable map of its entire data estate. When a regulator asks how a specific risk metric was derived, the answer is not a manually assembled document, it is a live, traceable lineage chain that shows every transformation the underlying data has undergone, with timestamps, and full audit history. The compliance burden shifts from a painful exercise to an embedded, ongoing capability.
Or consider a bank navigating the EU AI Act’s requirements for high-risk AI systems. Any AI model used to make credit decisions, assess insurance risk, or flag suspicious transactions must be able to demonstrate provenance back to authoritative data sources. Without complete lineage, that demonstration is impossible. With agentic lineage running continuously across the data estate, provenance tracing is not an afterthought, it can be built in from the start.
Human oversight is non-negotiable
The ‘human-in-the-loop’ model that is emerging as best practice across regulated AI deployments applies just as much here. The agent proposes, enriches and flags while the human reviews, approves and takes responsibility. This is not a compromise; it is the most sensible design for any system operating in an environment where accountability cannot be delegated to a machine.
As IDC’s Stewart Bond recently noted, regulators and boards increasingly expect organisations to demonstrate not just what decisions were made, but exactly what data underpinned them, and whether it can be trusted. That level of accountability requires human sign-off, and well-designed agentic systems make that sign-off faster and better-informed, not slower or more uncertain.
The leading approaches also allow banks to deploy agentic lineage within their own governance boundaries, using their own enterprise AI infrastructure. This keeps sensitive metadata sovereign which is critical for institutions cautious about where their most valuable data assets are sitting.
The window for action is now
Data lineage has always mattered but what has changed is that the technology now exists to build and maintain it at the speed and scale that modern banks actually need and that regulators increasingly demand. The gap between institutions that have invested in this capability and those still relying on manual processes is already visible, and it will only widen.
For banks, the question is no longer whether to take data lineage seriously. It is whether to keep addressing it with tools and processes designed for a slower, simpler era or to deploy the agentic capabilities that can finally make lineage a genuine, real-time operational asset.
The data problem that has frustrated CDOs for a decade has a credible answer and the institutions that move now will spend less time on compliance fire fighting and more time on the things that create competitive advantage.
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- Artificial Intelligence in FinTech