The Role of Artificial Intelligence in Automating DORA RoI Updates

AI transforms DORA compliance—automate your Register of Information with NLP & validation to cut costs & boost accuracy for financial institutions.

Since the Digital Operational Resilience Act (DORA) took effect in January 2025, every in‑scope financial institution—banks, insurers, payment processors, asset managers, rating agencies and others—must maintain a formal Register of Information (RoI). The RoI records all contractual arrangements with ICT third‑party service providers at entity, sub‑consolidated and consolidated levels. Supervisors treat the register as a near‑real‑time reflection of an organisation’s technology supply chain, not a once‑a‑year compliance file. Validation rules built into the official data taxonomy reject incomplete or inconsistent submissions outright.

Why manual upkeep collapses under its own weight

A typical RoI covers 17 tightly connected tables, hundreds of mandatory codes, legal‑entity identifiers and xBRL‑CSV formatting rules. Finding the latest contract version, tagging critical services, mapping ownership structures and re‑keying data can consume weeks of analyst time—and still produce errors. Meanwhile, contract changes occur continuously: new cloud workloads, amended service‑level agreements, mergers of subcontractors. A purely human process cannot keep pace.

How AI closes the gap

Artificial intelligence offers targeted solutions at each choke point:

• Document and e‑mail ingestion – Optical character recognition combined with large‑language‑model (LLM) extraction scans contracts, annexes and even email threads, pulling out relevant clauses in seconds.

• Semantic contract parsing – Fine‑tuned transformer models label services as “critical” or “important,” identify geographic locations and map sub‑outsourcing chains in line with DORA taxonomies.

• Entity resolution – Graph‑based matching engines spot missing or duplicate legal‑entity identifiers and merge fragmented supplier records.

• Automated mapping and validation – Rule engines populate the 17 RoI tables directly in xBRL‑CSV and run validation checks before submission.

• Continuous monitoring – Event‑driven ingestion watches procurement and vendor‑management systems so every new or amended contract is queued for processing automatically.

Early results from live deployments

Organisations that have rolled out AI‑enabled RoI pipelines report a reduction in preparation time from multiple weeks to a few hours, sometimes minutes. Error rates fall sharply because validation happens up‑front, not after the file is lodged. Compliance teams re‑allocate staff from repetitive data entry to higher‑value resilience analysis, and the risk of supervisory fines drops materially.

Quantifying the return on automation

A mid‑tier bank that previously spent around €180 000 a year on manual RoI maintenance—roughly 1.5 full‑time employees plus remediation costs—typically saves €90 000 to €110 000 in direct outlay during the first year of automation. Indirect benefits include faster insight into concentration risk, more agile contract negotiation and a reputational boost with regulators and customers alike.

Designing an AI‑first RoI architecture

A robust pipeline is usually built as a set of micro‑services:

Contract Sources → NLP + OCR → Entity‑Resolution Graph → Taxonomy Mapper → Real‑time Validator → xBRL‑CSV Generator → Secure Submission Gateway ↑ │ Change‑Event Listener

Key design principles:

• Modularity so the organisation can pilot one capability at a time.

• Human‑in‑the‑loop checkpoints for critical/important service flags.

• Explainability layers that log every AI decision and support model‑risk governance.

Implementation roadmap

1. Data readiness sprint – Clean vendor master data and obtain legal‑entity identifiers for every supplier.

2. Proof‑of‑concept – Run a small set of high‑risk contracts through the parser and compare to the manual baseline.

3. Integration and scale‑up – Connect the contract repository, procurement platform and vendor‑risk modules.

4. Governance and controls – Register models, set drift thresholds and align with internal model‑risk policies.

5. Continuous optimisation – Feed regulator feedback into the training data and refine mappings as taxonomies evolve.

Risks and safeguards

AI brings its own challenges: hallucination or misclassification, data‑privacy exposure and regulatory change. Dual‑model consensus, token masking, API isolation and automated taxonomy updates help mitigate these issues, while periodic human review remains essential.

Looking ahead

By 2027, conversational RoI assistants will answer supervisory questions on demand (“Show all cloud contracts over €1 million with sub‑outsourcing in Asia‑Pacific”). Knowledge graphs will detect concentration‑risk thresholds and trigger renegotiation workflows, and a single AI layer will populate DORA, NIS 2 and other EU reporting regimes from one authoritative data source.

Conclusion

Artificial intelligence is quickly becoming the only practical way to keep the DORA Register of Information accurate, validated and submission‑ready in real time. Firms that adopt AI‑driven pipelines now are already seeing double‑digit savings, smoother supervisory interactions and richer resilience insights. Those still wrestling with spreadsheets can start by experimenting with the freely available Dora register of information template and build upward from there. By embedding AI at the heart of RoI upkeep, organisations turn compliance maintenance into a strategic asset for operational resilience.