Rethinking transaction reporting with knowledge graphs

Firms have built strong transaction reporting foundations, navigating complex and often divergent regulatory texts while contending with fragmented architectures and siloed data flows. However, as regulatory expectations continue to evolve and with the adoption of ISO 20022, the limitations of legacy operating models are becoming increasingly exposed. Reporting logic is often opaque, data lineage is incomplete, and change management can be resource heavy.

Meeting the future demands of transaction reporting will require a framework shift in how firms model, interrogate, and adapt reporting logic. This is where knowledge graphs offer real promise – enabling structured, transparent, and scalable approaches to regulatory change.

Reframing the problem

In today’s reporting environments, firms continue to face persistent challenges, including:

• heavy reliance on manual processes and key-person expertise
• limited traceability of data lineage and transformation logic
• high cost and effort associated with impact assessment.

These issues don’t represent a failure of prior design decisions per se, but rather reflect the growing complexity and demands of the reporting landscape.

With regulatory initiatives like SEC Rule 10c-1a and the HKMA Reporting Rewrite on the horizon, the pace and complexity of regulatory change is accelerating. Institutions need tools that not only meet technical requirements, but also enhance collaboration, governance and adaptability in response to evolving expectations.

This urgency is paired with a clear opportunity. Advances in data standards (including ISO 20022), improved cross-regime interoperability and the growing maturity of semantic technologies make now the optimal time to scale knowledge graph adoption.

A connected approach to lineage and logic

Knowledge graphs offer a semantic layer that connects data, transformation logic, controls, and regulatory requirements. Instead of viewing these elements in isolation, a knowledge graph models them as interconnected entities, creating a dynamic, explorable web of relationships. With the right design, firms can:

• visualise end-to-end lineage from trade capture to report submission
• map rules, validations, and exception handling to specific fields
• trace control failures or reporting issues across systems and regulatory regimes.

Through natural language interfaces, these insights become accessible beyond technical users. Queries like “What rules apply to Notional Amount under EMIR and MiFID?” or “How is Execution Venue derived?” no longer require bespoke SQL or deep SME support. 

From Prototype to Platform

At Capco, we have developed a working prototype of a Regulatory Lineage Graph focused on transaction reporting. It features a robust ontology to model data flows and reporting logic, a natural language interface, and a graph-RAG [retrieval-augmented generation] pipeline that enables intuitive, plain-language querying. This allows users – from compliance teams to business leads – to explore complex lineage and rules with ease.

This is not just about metadata, it is about transforming the reporting experience. By integrating the graph into existing or new interfaces, reporting logic becomes interactive, explainable, and resilient. 

Our approach is shaped by delivery of graph-based solutions for tier-1 institutions, particularly in regulatory reporting and data lineage. That experience has informed a scalable, adaptable architecture built for evolving requirements.

We’re expanding functionality, integrating data quality signals to trace validation issues to the instrument level and up through the data lineage. With structured regulatory data fed into the graph, we’re also enabling generative AI use cases. RAG techniques enhance explainability and support automated, compliant content generation.

The solution offers strong explainability through end-to-end traceability, enhancing confidence across risk, compliance, and audit functions. It significantly accelerates speed to insight – reducing query times from days to seconds – while also increasing change resilience by making regulatory simulations faster and more cost-effective. A shared semantic layer fosters cross-functional clarity, enabling business, data, and compliance teams to align more easily.

Achieve results, today

Knowledge graphs won’t replace your transaction reporting engine – but they will transform how you understand, govern, and evolve it. In an era of heightened regulatory scrutiny and increasing operational complexity, the ability to interrogate and interpret your reporting infrastructure – rather than simply document it will be a defining capability. We view this as a practical and achievable strategic step, one that is ready to be implemented today.