Regulatory reporting is moving beyond the submission of standardised templates, with supervisors increasingly demanding granular data that can be traced, audited and explained. Across Asia-Pacific (APAC), banks face growing pressure to demonstrate the accuracy and completeness of their submissions, making data lineage and governance central to regulatory compliance.
Regnology, a provider of regulatory reporting and compliance technology, explored these challenges at its RegInsights event in Singapore earlier this summer. Discussions spanning regulatory developments, data management and technology innovation highlighted the need for banks to move away from fragmented reporting processes towards integrated data and technology frameworks. The company will continue the conversation at the 33rd RegTech Convention in November, under the theme “From Control to Intelligence”, examining how data can support greater insight across reporting, risk and finance.
The changing requirements reflect several regulatory developments arriving at once. Prudential frameworks are becoming more interconnected, requiring institutions to assess the implications of new rules across capital, risk and reporting. In Singapore, the Basel III output floor under MAS Notice 637, revised large exposure rules under MAS Notice 656 and new crypto-asset frameworks demonstrate the expanding scope of regulatory data requirements.
While these measures address different areas of financial risk, they share a demand for more detailed information, faster recalculations and greater transparency. Banks must be able to provide supporting evidence and explain how reported figures have been generated when supervisors request it.
The challenge becomes more complicated for institutions operating across multiple jurisdictions. Although regulatory objectives are increasingly aligned, individual markets continue to differ in their implementation timelines, reporting requirements and supervisory expectations. This creates difficulties for banks attempting to maintain consistent data definitions, controls and governance across their operations.
Supervisory technology (SupTech) and advanced analytics are also changing how regulators examine submissions. Greater use of automated analysis means inconsistencies and gaps in reporting data can be more readily identified. As a result, institutions need robust data quality controls, end-to-end lineage and explainable reporting processes.
Traditional reporting architectures are poorly equipped to meet these expectations. Many banks have responded to regulatory changes by introducing separate systems and tactical fixes for individual requirements. Over time, these additions have created fragmented data environments, with manual workarounds and duplicated spreadsheets making reporting more difficult to maintain and audit.
One regional banking executive recalled the significant remapping exercise required under MAS Notice 610. The executive’s concern was that the issue is no longer simply the volume of information banks need to process, but the assurance required to prove that data is accurate, complete and timely.
This puts pressure on reporting teams that still rely on extracting information into reporting engines shortly before submission deadlines. Without clear visibility of where data originates and how it has been transformed, institutions may struggle to demonstrate the reliability of their figures.
One approach gaining traction is Regnology’s “map once, report many” principle. It separates the sourcing and standardisation of data from the reporting rules applied downstream, allowing multiple regulatory and internal functions to draw on a consistent data foundation.
Under this model, banks can respond to new reporting requirements by configuring rules rather than repeatedly rebuilding data pipelines. The approach is designed to reduce duplication, strengthen consistency and make regulatory changes easier to manage.
Regulators in Singapore, Hong Kong, Malaysia and Australia are moving towards more granular, transaction-level data. This increases the importance of data models capable of supporting detailed reporting across jurisdictions. Regnology’s cloud-native Reporting Hub, built on its Granular Data Model, was presented as one solution for putting this architecture into practice.
Artificial intelligence is introducing further opportunities to improve reporting and compliance processes, but industry expectations are increasingly centred on human oversight. Rather than handing regulatory decisions entirely to automated systems, banks are exploring AI as a co-pilot that can identify anomalies, explain variances in liquidity metrics and suggest potential actions.
Human teams remain responsible for reviewing outputs and making decisions, while audit trails provide a record of the process. Regnology’s Ascend platform and Governance Intelligence Layer apply this approach to agent-driven workflows, combining automation with governance and accountability.
Regnology general manager APAC Philipp Hühne said, “Regulatory reporting is the only area where institutions don’t want to be special. You want to move in a pack.”
Regnology’s central message is that regulatory reporting must be supported by a connected data architecture rather than a collection of individual compliance processes. As supervisors demand increasingly granular submissions, banks will need to demonstrate not only that their reporting is accurate, but that every figure can be traced back to its source and supported by reliable controls. Templates will remain the visible output, but data lineage, governance and technology will determine whether institutions can meet the next generation of regulatory expectations.
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Copyright © 2026 RegTech Analyst
