Bank Transaction Data Quality: Why Enrichment Is Now Essential for Nordic Banks
Stockholm, Sweden – Open Banking's first phase unlocked access to European financial accounts. But for banks, lenders, and fintechs, a harder problem remains: bank transaction data quality. As the sector moves toward Open Banking 2.0 and prepares for the Anti-Money Laundering Regulation (AMLR) and the incoming PSD3/PSR payments package, access to raw data is no longer the constraint — usability is.
Transaction enrichment, the process of turning raw, unstructured bank data into clean, categorised, machine-readable intelligence, is fast becoming mandatory infrastructure. Due to legacy banking standards, data loss during clearing, and cryptic merchant references, raw transaction data arrives as an unintelligible mess. For Nordic institutions trying to automate credit underwriting, improve customer experience, or run AI-driven financial crime prevention (FCP), poor data quality is the bottleneck.
Executive summary: Legacy clearing systems and inconsistent payment networks strip context from transactions, leaving cryptic strings such as 'Bankgirot SBL FIN' or '984*ENLABS NO'. Poor data quality drives high false-positive rates in AML monitoring, manual bottlenecks in credit risk, and unreliable AI outputs, and industry-wide it costs the average organisation an estimated $12.9 million a year (Gartner). With AMLR taking direct effect from July 2027, the PSD3/PSR package close to adoption, and the EU AI Act imposing data-governance duties on high-risk models, robust and explainable data is now a regulatory requirement. Transaction enrichment layers that cleanse, categorise, and structure raw data are becoming standard infrastructure for modern financial services.
Why Is Raw Bank Transaction Data So Difficult to Work With?
Transaction data was designed for accounting and settlement, not for consumer insight or algorithmic risk scoring. When a customer pays, the data passes through multiple intermediaries: acquiring banks, payment processors, card networks, and issuing banks. At each hop, character limits, formatting shifts, and ageing standards degrade the information.
By the time it reaches an Open Banking API or an internal ledger, a transaction that should read as a simple subscription or salary payout often looks like a chaotic alphanumeric string. A legitimate salary payment or a Nordic tax return can surface as an obscure reference, making it impossible for rule-based systems to reliably categorise income, identify loan repayments, or flag organised-crime patterns such as manipulated accounts linked to Bolagsverket or Skatteverket.
The Cost of Unstructured Data in AML and Financial Crime Prevention
Financial crime prevention depends on detecting anomalous behaviour in real time. When the underlying transaction data quality is poor, the effectiveness of any AML system drops sharply.
Nordic banks have invested heavily in automated transaction monitoring, yet unstructured data triggers a cascade of false positives. When a system cannot distinguish a legitimate B2B invoice payment from a high-risk offshore transfer because of a truncated merchant name, compliance teams are forced into manual review, and in legacy rule-based monitoring the large majority of alerts turn out to be false positives, consuming analyst time on cases that were never risky.
The efficiency gain from cleaner data is measurable. In some Nordic deployments, enriched transaction data has cut case-handling times (handläggningstider) in half, effectively doubling the throughput of manual review teams. Enrichment does not just reduce noise; it directly reduces the operational cost of compliance.
The governance bar is also rising. When the EU's AMLR takes direct effect from July 2027, institutions will need to prove to the new EU-level supervisor (AMLA) that their risk models are explainable and defensible. A model trained on messy, unstandardised data cannot meet that standard. Structured, enriched transaction data is the prerequisite for the explainable AI that tomorrow's compliance landscape demands.
Improving Credit Underwriting for B2B and B2C Lenders
Credit risk scoring is shifting from static credit-bureau snapshots towards real-time cash flow underwriting. By analysing up to 12 months of transaction history, lenders can assess true affordability, spot undisclosed liabilities, and identify risky behaviour. But evaluating an applicant on raw Open Banking data is nearly impossible without a robust data-quality layer.
For consumer credit (B2C), lenders must isolate salary deposits from one-off transfers and identify recurring subscriptions or gambling activity, for example recognising 'Ninja Casino' inside a string like '984*ENLABS NO'.
For business credit (B2B), lenders must verify a counterparty's operational health through invoice payments, tax liabilities, and payroll, data points that are hard to fake when pulled directly from enriched bank feeds.
Why Enrichment Is Becoming a Prerequisite for Financial AI
The financial sector's AI ambitions are increasingly bottlenecked not by the models, but by the data feeding them. Gartner predicts that through 2026, organisations will abandon 60% of AI projects unsupported by AI-ready data, and 63% of organisations say they either do not have, or are not sure they have, the data-management practices AI requires. Poor data quality already costs the average organisation an estimated $12.9 million a year, with MIT Sloan research putting the revenue impact at 15-25%. In transaction-heavy institutions, most of that unready data is raw banking data: fragmented, truncated, and uncategorised.
This is where enrichment stops being a nice-to-have. AI models, whether they score credit, detect financial crime, or power customer-facing agents, inherit the quality of their inputs. A model fed '984*ENLABS NO' learns noise; a model fed a clean, categorised 'Gambling - Ninja Casino' event learns signal. Enrichment converts unstructured transaction strings into the standardised, labelled features that machine learning depends on, which is why it directly determines model accuracy, explainability, and auditability.
The point is now written into EU law. Article 10 of the EU AI Act requires high-risk AI systems to be built on training, validation, and testing data that is relevant, representative, and, as far as possible, free of errors, and it explicitly lists enrichment among the data-preparation operations providers must govern. Because AI systems that evaluate the creditworthiness of individuals or set their credit score are classified as high-risk under Annex III, point 5(b), any lender running an AI credit model on Nordic bank data is, in practice, accountable for the quality of the enriched data beneath it. Combined with the explainability bar AMLR and AMLA set for financial-crime models, this makes enrichment less a performance optimisation than a compliance foundation.
For Nordic institutions the takeaway is direct: you cannot build defensible, high-performing financial AI on raw Open Banking data. The enrichment layer is what makes the data AI-ready, in both the technical and the regulatory sense.
Gokind: Elevating Data Quality for the Nordic Market
Bridging the gap between raw data and actionable intelligence requires specialised infrastructure. Gokind is the Nordics' leading company in bank transaction data quality and enrichment.
Instead of relying on internal engineering teams to build and maintain endless parsing rules, institutions use Gokind to automatically cleanse, structure, and enrich their transaction data. Gokind processes chaotic data strings and enriches them with clear merchant names, logos, precise categorisation, payment types, and specific event markers. By transforming a string like 'Marie semester: 24220' into a verified, categorised 'Salary' event, Gokind provides the intelligence layer that lets AI agents, credit models, and AML systems consume banking data efficiently and accurately.
Conclusion
As the European sector moves from Open Banking to Open Finance, the volume of accessible data will only grow. But access without clarity is a liability. For Nordic banks and fintechs, treating bank transaction data quality as a strategic priority is no longer optional. With advanced transaction enrichment, institutions can unlock faster credit decisioning, cut compliance costs, and build the resilient infrastructure the next generation of financial services requires.
Frequently Asked Questions
What is transaction enrichment? Transaction enrichment is the process of taking raw, unstructured bank data and turning it into clean, actionable intelligence. It involves identifying the true merchant, categorising the spend (for example Groceries, Salary, or B2B Invoice), and appending metadata such as logos, risk markers, and payment types.
Why is raw transaction data so difficult to work with? Because of ageing banking standards, data loss during clearing, and complex payment references, transaction data is highly fragmented. Strings are often truncated, mixed with internal reference numbers, or mislabelled, making manual or basic rule-based parsing highly inaccurate.
Is transaction enrichment required under the EU AI Act? The EU AI Act does not mandate a specific vendor, but Article 10 requires high-risk AI systems to be built on well-governed, high-quality data and explicitly names enrichment among the data-preparation steps providers must govern. Since credit scoring of individuals is high-risk (Annex III, 5(b)), enriched, structured data is effectively a prerequisite for compliant AI credit models.
What is the difference between PSD3 and PSR? They are two parts of the same EU payments package. PSD3 is a directive (updating PSD2) that member states transpose into national law, mainly covering licensing and supervision. The PSR (Payment Services Regulation) applies directly across the EU and covers operational rules such as fraud, access, and data. Together they raise the bar for payment data handling.
Why does the structured versus unstructured distinction matter so much for AI models? AI models need clean, standardised inputs to produce reliable, explainable outputs. Enriched data acts as the foundation for the AI layer, letting machine-learning models in credit underwriting and AML find patterns without being confused by formatting noise.
Can AI models just learn to interpret raw transaction data themselves? While large language models are powerful, applying them directly to raw, unstructured financial transactions is computationally expensive, prone to hallucination, and difficult to audit. A dedicated enrichment layer delivers deterministic, verifiable categorisation that meets strict financial regulatory standards.
References
Gokind: AMLR explained — what changes for Nordic banks' financial crime workflows in practice in 2027. https://gokind.co/blog/amlr-explained-nordic-banks-2027/
Gokind: Current State of Open Banking — From Regulatory Access to Financial Infrastructure. https://gokind.co/blog/current-state-of-open-banking/
Gartner: Lack of AI-Ready Data Puts AI Projects at Risk (February 2025). https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk
Gartner: Magic Quadrant for Data Quality Solutions (2020) — source of the $12.9 million per year figure.
MIT Sloan Management Review / Cork University Business School — 15–25% revenue impact of poor data quality.
EU AI Act, Article 10 — Data and Data Governance. https://artificialintelligenceact.eu/article/10/
EU AI Act, Annex III, point 5(b) — high-risk classification of creditworthiness and credit-scoring systems. https://artificialintelligenceact.eu/annex/3/
Regulation (EU) 2024/1624 (AMLR) — on preventing the use of the financial system for money laundering or terrorist financing. Available on EUR-Lex.
European Commission: Proposal for a framework for Financial Data Access (FiDA) and the modernisation of payment services (PSD3/PSR).
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