A surprising amount of AI strategy is still sitting on top of spreadsheet-era data foundations
Financial services in EMEA still has a data problem...AI is simply making it harder to hide.
The EBA’s reporting framework 4.2 includes new and amended requirements, including instant payments reporting. Their ‘Pillar 3’ data hub went live on 23 January 2026, reflecting the broader push towards more centralised, comparable and accessible prudential disclosure under the CRR3/CRD6 package.
Meanwhile, EIOPA opened a March 2026 consultation on integrated data collection aimed at streamlining and harmonising reporting. None of that is just a ‘reporting’ story. It is an ‘operating model’ story. The more firms are asked to evidence resilience, risk, prudential strength and customer outcomes, the more the burden shifts upstream to ingestion, classification, validation and traceability.
The problem is rarely a lack of dashboards. It is poor inputs and too much manual normalisation before anyone can trust the output.
That is why this is one of the cleanest Tungsten plays on the board.
Turning fragmented, document-heavy inputs into structured, auditable, decision-grade data is not glamorous. But right now, it is also one of the most commercially useful forms of modernisation.
Where could we reposition document-heavy pain as a data quality and decision-readiness problem?
Frequently Asked Questions
Why is AI exposing data quality problems in financial services?
AI depends on accurate, structured, and traceable information. Weak data foundations, fragmented document inputs, and manual normalization make it difficult for financial institutions to trust AI-generated outputs.
Why is regulatory reporting also an operating model issue?
Regulatory reporting depends on the upstream processes used to ingest, classify, validate, normalize, and trace information. Weak operational processes reduce the quality and reliability of the final report.
How does document intelligence improve data quality?
Document intelligence converts fragmented and unstructured document inputs into structured, validated, and auditable data that can support regulatory reporting, analytics, and enterprise decision-making.
What is decision-ready data?
Decision-ready data is accurate, structured, validated, traceable, and available in a form that business systems, analysts, and AI models can use with confidence.
Glossary
| Term |
Definition |
| Data Quality |
The accuracy, completeness, consistency, timeliness, and reliability of information used by business processes, reporting systems, and AI models. |
| Decision-Ready Data |
Structured, validated, and traceable information that can be used confidently for analysis, automation, regulatory reporting, and business decisions. |
| Document Intelligence |
AI-powered technology that classifies documents and extracts, validates, and structures the information they contain. |
| Data Ingestion |
The process of collecting and importing information from documents, applications, databases, and other sources into a business system. |
| Data Normalisation |
The process of converting information from different formats and sources into a consistent structure that can be compared and processed reliably. |
| Data Traceability |
The ability to track where information originated, how it was processed, and how it was used throughout a workflow or reporting process. |
| Pillar 3 |
Prudential disclosure requirements intended to improve transparency and comparability across regulated financial institutions. |
| CRR3/CRD6 |
The latest EU banking regulatory package updating capital requirements, supervisory standards, and prudential reporting obligations. |
