Open Finance data becomes valuable only when people and systems can understand it. Data enrichment and categorization turn inconsistent transaction strings into governed financial context.
Raw data is not yet intelligence
Transaction descriptions vary across institutions, channels, merchants, and markets. The same counterparty may appear under several labels, while salaries, rent, subscriptions, repayments, and transfers can be difficult to distinguish reliably.
A strong enrichment layer normalizes those descriptions, resolves merchants and counterparties, assigns governed categories, and identifies recurring financial patterns.
Explainability matters
Institutions need to understand why a transaction received a category or why a financial signal was produced. Observable and versioned rules make the result testable, correctable, and suitable for use inside regulated journeys.
A controlled enrichment journey
- Source specific transaction fields are normalized.
- Known merchants, counterparties, and categories are resolved.
- Recurring income, commitments, and spending patterns are detected.
- Governed rules produce explainable financial signals.
- Missing cases create controlled rule improvement tasks.
- The enriched context is used only inside an approved journey.
The goal is not to decorate raw data. It is to create financial context that remains explainable, governed, and useful.
AI can improve the rulebook without seeing the customer record
Production rules can execute inside the regulated environment. External AI can assist with proposing new rules from controlled examples and context that excludes PII, then pass those rules through testing and governance before production use.
A reusable foundation for many experiences
Once transaction data is understandable, the same enrichment capability can support personal financial management, SME cash visibility, verification, affordability, risk, service, and payment experiences without rebuilding the interpretation layer for every journey.