Spade raises $40m to expand transaction data platform for banks
Spade has closed a $40 million Series B funding round led by Oak HC/FT, with backing from Andreessen Horowitz, Flourish, Gradient, NAventures, and the National Bank of Canada's venture arm. The company processes 1.9 billion transactions daily for financial institutions and fintech firms seeking to make sense of raw transaction data.
Transaction strings from card programs, ACH transfers, and wire payments are often unreadable without enrichment. Banks struggle with high dispute volumes, poor customer experiences, and inaccurate reward attribution as a result. Spade matches raw data against a proprietary database of verified businesses to identify exactly where and with whom each transaction occurred.
The platform covers 99.9% of US and Canadian merchants with accuracy above 99%. Its API infrastructure delivers responses in under 40 milliseconds at the 99th percentile, making it among the fastest solutions available.
Spade has grown 470% year-over-year and counts Stripe, Bilt, and Mercury among its clients. More than 85% of customers use the platform across multiple functions, from authorization to attribution to analytics.
How the technology works
A proprietary matching engine powered by AI agents continuously scans external data sources to fill gaps and remove duplicates. The system improves with each transaction processed, generating attributes like precise geolocation and verified merchant categories independent of standard merchant category codes.
Oban MacTavish, Spade's co-founder and CEO, said the funding positions the company to become "the default data and intelligence layer for financial services." He added that banks moving toward fully automated workflows need "detailed, verified, and structured consumer behavior powered by enriched transaction data."
Next steps
Spade plans to expand beyond transaction enrichment into a full payments intelligence platform. The company will enable clients to build, manage, and automate workflows for personalized rewards attribution, real-time customer notifications, and behavioral user segmentation.
For finance professionals evaluating AI for Finance solutions, transaction data quality remains a foundational requirement for any automated system.
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