Artificial intelligence can help organisations prepare more accurate, consistent and timely regulatory reports. But its value depends on reliable source data, appropriate controls and accountable human review.
A new opportunity for regulatory reporting
Regulatory reporting is essential to corporate accountability, yet preparing it can be resource-intensive. Finance and compliance teams often work across accounting records, transaction data, regulatory templates, policies and supporting documentation. Even when the underlying information is available, extracting, reconciling and presenting it correctly requires considerable effort.
Artificial intelligence (AI) offers an opportunity to improve this process—not by removing professional responsibility, but by helping experienced teams concentrate on matters that genuinely require judgement.
The Malta Financial Services Authority (MFSA) has recognised the growing role of AI in financial services. Its April 2026 sector survey reported that 39% of respondents were testing or deploying AI in production. The findings also highlighted continuing challenges around formal governance, data protection, reliability and third-party dependencies.
For Malta-based businesses, the important question is therefore not simply whether AI can produce a report. It is whether AI can support a reporting process that remains accurate, explainable, secure and defensible.
Where AI can make a practical difference
1. Collecting and organising information
A regulatory return may draw information from trial balances, general ledgers, bank statements, portfolio records, client-money reconciliations and internal registers.
With appropriate access controls, AI-enabled systems can assist by identifying relevant documents, extracting information, classifying records and identifying incomplete submissions.
For example, an AI-assisted workflow might highlight missing supporting statements or identify differences between reporting-period data and the records used to prepare a draft return.
This reduces time spent locating information, while giving the reporting team a clearer view of the evidence available.
2. Supporting reconciliations and exception reviews
Regulatory reporting relies on consistency between different sources.
AI can help identify unusual movements, missing fields, apparent inconsistencies and items requiring investigation. It can also assist with explaining changes between reporting periods.
However, regulatory calculations and mandatory validation rules should remain within controlled, tested calculation processes. A generative AI model should not be relied upon to invent formulas, estimate missing regulatory figures or determine capital requirements without independently verifiable logic.
The European Banking Authority’s reporting frameworks illustrate the importance of structured data definitions, technical specifications and validation rules.
The appropriate model is therefore AI-assisted exception detection combined with deterministic calculations and independently checked data.
3. Preparing draft explanations and management commentary
Many regulatory and statutory reports contain narrative elements.
AI can help produce initial drafts of management commentary, financial-variance explanations, compliance summaries, board reporting and regulatory correspondence using approved source materials.
An effective workflow should connect every material statement to supporting evidence and clearly identify information requiring confirmation.
This can substantially reduce repetitive drafting. Nevertheless, the resulting text must be reviewed to ensure that it accurately represents the entity’s circumstances and does not contain invented facts, unsupported conclusions or misleading assurances.
4. Monitoring regulatory changes
Reporting requirements evolve. Templates, taxonomies, validation rules and supervisory instructions may change between reporting periods.
AI can help teams monitor official publications, compare document versions and summarise changes that may affect internal reporting procedures.
For example, Malta investment firms may need to consider updates to MiFID Firms reporting requirements and applicable EBA reporting frameworks.
AI can provide a useful change summary, but the reporting owner must confirm the applicable rules, effective dates, reporting population and prescribed templates directly against authoritative sources.
An AI-generated summary must never substitute for reading the relevant regulatory instrument or supervisory instruction.
A practical example: preparing an MFSA regulatory return
Consider an investment firm preparing a periodic regulatory return.
A controlled AI-assisted process could begin with approved financial data and supporting records. Software would map the information into the applicable reporting structure, perform prescribed calculations and run formal validation checks.
AI could then assist with explaining significant period-on-period changes, identifying gaps in supporting documentation and preparing an exception report for the finance and compliance teams.
The draft return would progress through an established Maker → Checker → Approver process, with each reviewer examining the matters appropriate to their responsibilities.
Only after the required checks and approvals would the authorised person proceed with submission through the relevant regulatory channel.
This example demonstrates an important distinction: AI can improve preparation and review efficiency without replacing the control framework surrounding the final regulatory submission.
Five safeguards that should never be overlooked
Data confidentiality. Corporate, client and regulatory information should be processed only through appropriately assessed and authorised systems. Organisations must consider confidentiality, GDPR obligations, service-provider arrangements, access restrictions and data retention before using external AI platforms.
Accuracy and source verification. AI-generated numbers, legal references and narrative statements must be checked against underlying evidence. A convincing explanation is not necessarily a correct explanation.
Clear accountability. Responsibility for regulatory submissions remains with the relevant firm and its authorised officers. AI-generated content should not bypass existing approval responsibilities.
Auditability. The process should preserve appropriate source references, data versions, validation outcomes, reviewer decisions and submission evidence. An organisation should be able to explain how a reported figure or conclusion was established.
Ongoing monitoring. AI systems and their third-party providers require proportionate oversight. Performance, access, reliability, model changes and operational dependencies should be reviewed over time.
These principles also align with the direction of the EU AI Act, whose obligations depend on the nature and intended use of the AI system. Not every reporting assistant is automatically a high-risk AI system, but relevant legal requirements still need to be assessed.
The MFSA’s 2026 message: governance comes first
In June 2026, the MFSA issued a Dear CEO Letter concerning AI governance, risk and prudential expectations.
The Authority emphasised board and senior-management accountability, appropriate oversight, third-party dependencies, model validation, data governance and operational resilience.
This is an important message for firms considering AI-assisted regulatory reporting.
Introducing a sophisticated tool does not automatically produce a sound control environment. Organisations need to determine what the tool is permitted to do, which information it can access, how its outputs are tested and who is responsible for accepting or rejecting its conclusions.
The more significant the reporting consequence, the stronger the justification should be for independent verification and documented approval.
Start with a controlled use case
The most sustainable approach is to introduce AI where its benefits can be measured and its risks remain manageable.
A business might begin with document classification, reporting checklists, variance explanations or internal management-report drafts.
Before expanding the use case, it should evaluate accuracy against known examples, assess data-protection implications, establish reviewer responsibilities and document the appropriate controls.
A successful pilot is not simply one that produces a report faster. It is one that can demonstrate comparable or better quality, reliable evidence, controlled exceptions and less unnecessary manual effort.
The future is AI-assisted, not accountability-free
Benjamin Franklin’s observation—“An ounce of prevention is worth a pound of cure”—remains relevant to regulatory reporting.
The greatest benefit of AI may not be the speed with which it produces a first draft, but the opportunity it creates to identify problems earlier, improve consistency and support better-informed professional review.
For Malta’s corporate and regulated sectors, the strongest reporting model combines technology-enabled efficiency with reliable source data, sound governance and accountable human decisions.
AI should support the reporting professional—not replace the responsibility behind the report.
Geren Corporate supports businesses with corporate governance, regulatory reporting coordination, compliance processes and structured reporting controls. The objective is to help organisations maintain clear responsibilities, reliable records and well-governed reporting procedures.
This article is provided for general information only. It does not constitute legal, regulatory, accounting, tax or other professional advice. Reporting and AI governance requirements must be assessed against applicable law, supervisory expectations and the circumstances of each organisation.
Primary references
MFSA — AI Adoption in Malta’s Financial Services Sector: 2025 Survey Findings
MFSA — AI Governance and Prudential Risk Expectations, June 2026
EBA — Regulatory Reporting Frameworks
EU — General Data Protection Regulation