AI in Regulatory Reporting

Introduction

At JC Advisory Luxembourg, we use AI as an accelerator for Business Analysis in regulatory reporting, never as a replacement for human expertise.
Here is our perspective on this hybrid operating model, from use cases to testing and data quality.

How AI enhances Business Analysis in regulatory reporting

Regulatory reporting remains one of the most demanding areas of banking transformation.
Frameworks such as COREP, FINREP, IRRBB and EMIR require precise regulatory interpretation, accurate process modelling and reliable data integration across complex IT landscapes.

At JC Advisory Luxembourg, we view artificial intelligence not as a substitute for human expertise, but as a support tool for our Business Analysts, covering the full delivery lifecycle — from use-case definition to testing and data-quality controls.

Concretely, AI is leveraged to:

• support regulatory analysis and monitoring (text comparison, synthesis, identification of regulatory impacts);
• assist in structuring business use cases and scenarios (data flows, regulatory scenarios, edge cases);
• accelerate the production of functional specifications and project documentation with enhanced consistency;
• contribute to the creation and structuring of testing assets (test scenarios, UAT cases, test data sets, boundary conditions);
• reinforce data quality frameworks, by supporting the identification of control rules (completeness, consistency, thresholds, reconciliations) and assisting in variance analysis;
• improve traceability, document coherence, and knowledge capitalization (templates, rule libraries, regulatory repositories).

Combined with the hands-on expertise of our consultants, this hybrid approach allows our teams to focus on higher-value activities:

• facilitation of business workshops across Risk, Finance, IT and Data teams;
• prioritization and arbitration of use cases aligned with regulatory expectations and operational constraints;
• design and validation of data-quality rules across the full regulatory reporting chain;
• quality assurance of delivery artifacts (review of mappings, controls, reconciliations, regulatory outputs);
• end-to-end validation of data and processes from source systems to regulatory returns.

This operating model, combining human expertise, strong governance and intelligent tooling — enhances both the operational efficiency of transformation programs and the reliability of regulatory compliance frameworks, while preserving essential human accountability in regulated environments.

At JC Advisory, we strongly believe that the future of regulatory reporting lies in this synergy between business expertise, data governance and intelligent technologies, serving pragmatic, robust and sustainable banking transformations.