Banks restrict transaction data, hampering fintechs

Banks are limiting access to detailed, standardized transaction records by restricting APIs, delivering inconsistent fields and charging for enriched feeds, industry participants say. Those practices reduce commercial incentives for third parties to build services that rely on reliable transaction information.

Fintechs, accounting software vendors, credit assessors and merchant analytics firms use transaction-level data-merchant names, timestamps, category codes and line-item detail-for budgeting tools, underwriting models and expense management. Companies report that banks provide API access that is slow, subject to tight rate limits or stripped of metadata, forcing vendors to spend time on data cleaning and reconciliation.

In markets with legal access frameworks, such as the EU under PSD2 and the UK under open banking rules, banks are technically compliant but often deliver records that lack granularity or arrive with delays of hours or days. Firms that rely on near-real-time feeds report that delays affect the timeliness of financial products.

Technical limits in legacy banking systems are cited as a key reason for low data quality. Core banking platforms and card networks were built for clearing and settlement rather than for delivering enriched customer-facing fields. Industry developers report truncated transaction strings, inconsistent merchant identifiers across issuers and uneven application of merchant category codes. Tokenization and privacy measures can remove contextual fields, prompting developers to build reconciliation layers to produce usable records.

Commercial motives also affect data sharing. Some banks offer proprietary data products or charge for premium API access and enriched feeds. Banks cite anti-money-laundering rules and customer privacy as reasons to restrict certain fields or to require stricter onboarding for data recipients.

The effects are visible across multiple sectors. Small-business accounting platforms report significant engineering effort spent normalizing merchant names and matching payments to invoices. Personal finance apps report higher rates of failed categorization and increased manual corrections from users. Lenders that use transaction histories to assess cash flow report lower-confidence inputs and larger margins of error in underwriting models when bank-provided data is incomplete. Payment processors and merchant analytics firms report that gaps in transaction narratives make it harder to attribute spend by product or campaign.

Regulatory responses differ by jurisdiction. PSD2 and the UK open banking regime set legal and technical pathways for third-party access. The United States lacks a single federal open-banking mandate, leaving market agreements and bilateral arrangements to govern access. Where standards exist, firms say compliance has increased availability but not always the consistency or richness of records.

Industry groups are working on common API specifications and richer data models to reduce variation in field delivery and labeling. Some banks offer sandbox environments or partner programs to ease integration. Third-party aggregators that normalize feeds across multiple banks have grown; these services charge fees and apply mapping logic, which shifts costs onto fintechs and end customers.

Operational priorities inside banks further affect outputs. Engineering resources allocated to fraud detection, compliance work or system migrations can delay improvements to transaction data APIs. Vendors report delays in feature requests that would preserve richer merchant descriptors or timestamps.

Transaction data refers to the digital records generated when payments occur. Typical fields include merchant name, merchant category, transaction amount, currency, timestamp, authorization code and descriptive text from the merchant. Standards such as merchant category codes and initiatives under PSD2 and open banking aim to create common expectations for data sharing, while technical implementation, commercial terms and compliance interpretations determine how useful the delivered data is in practice.