Process Automation in Regulatory Reporting

The best candidates are recurring, rule-based processes with structured inputs and high manual effort. In practice this typically includes data collection and consolidation from source systems, data quality checks and plausibility validations, filling and reconciling COREP/FINREP templates, XBRL conversion and submission, and the documentation of processing steps. Less suitable for full automation are judgement-heavy activities such as interpreting new regulatory requirements or analysing unusual movements — here automation supports rather than replaces the expert. We recommend starting with a structured potential analysis: map the end-to-end reporting process, quantify manual effort and error frequency per step, and prioritise automation candidates by benefit and implementation effort. This ensures the first initiatives deliver visible results quickly and build momentum for a broader automation programme.

The benefits fall into four categories. First, efficiency: automating recurring processing steps frees capacity that reporting teams can redirect to analysis and quality assurance rather than data mechanics. Second, quality: automated validations catch errors systematically and consistently, reducing correction cycles and resubmissions. Third, timeliness and resilience: automated processes handle deadline pressure and peak loads better and are less dependent on individual key persons. Fourth, auditability: every processing step is logged, which strengthens your position in internal and supervisory audits. The realistic magnitude depends heavily on your starting point — institutions with highly manual, spreadsheet-driven processes see the largest gains. A potential analysis at the start of the initiative gives you a concrete, defensible business case instead of generic promises.

Each technology addresses a different layer of the reporting process, and they are most effective in combination.

🔍 Technology building blocks:

The right mix depends on your system landscape. RPA often delivers quick wins, while ETL pipelines and workflow integration create sustainable, maintainable automation over the long term.

Automation strengthens auditability when it is designed for it. Key elements include complete logging of every processing step, version control for transformation rules and report logic, documented test and release procedures for changes, and clear human accountability: automation executes, but responsibility for the submitted report remains with named process owners and defined sign-off steps. Data lineage is particularly important — you must be able to trace every reported figure back to its sources, which aligns directly with BCBS 239 principles. We design automation solutions with these requirements built in from the start, including the documentation internal audit and supervisors expect. Done properly, an automated process is easier to audit than a manual one, because nothing depends on undocumented individual knowledge.

Usually not. Most automation potential lies in the processes around your reporting engine — data sourcing, quality assurance, reconciliations, adjustments and submission handling — rather than in the engine itself. RPA and workflow solutions integrate with your existing landscape without invasive changes, and ETL pipelines can be built incrementally alongside current processes. We deliberately design solutions that are vendor-neutral and work with established reporting platforms. That said, an automation initiative is a good moment to review your architecture: if your reporting software requires extensive manual workarounds or is approaching end-of-life, we assess whether targeted automation or a platform change delivers the better long-term business case. The decision is always based on your specific landscape, not a predetermined tool preference.

We recommend starting with a compact potential analysis: within a few weeks, we map your reporting processes, quantify manual effort and error sources, and deliver a prioritised automation roadmap with a business case per initiative. Implementation then proceeds iteratively — first quick wins such as RPA for repetitive tasks or targeted data quality automation, then structural improvements such as ETL pipelines and workflow orchestration. A full transformation towards straight-through processing is a longer journey, usually phased over several release cycles so that ongoing reporting operations continue undisturbed. Throughout the initiative, we build knowledge in your team through training and joint implementation, so you can operate and extend the solutions independently. The phased approach means each stage delivers value before the next begins.