As Sardine scaled its fraud and risk platform, transaction data became a critical input to real-time assessments. But inconsistent merchant information and noisy transaction descriptors made it difficult to accurately evaluate risk in the moment, until Sardine integrated Spade.
Impact at a glance:
- Improved merchant identification for fraud and risk evaluation
- Enabled higher approval rates by reducing false positives tied to unclear merchant data
- Reduced reliance on brittle rules and manual heuristics
The Opportunity
Sardine provides fraud, risk, and compliance infrastructure for banks and fintechs that need to evaluate activity in real time. Today, the platform helps secure more than $150B in transactions and has helped stop over $21.3B in attempted fraud — a scale where signal quality and speed are critical.
For Sardine, merchant identity and transaction context are essential inputs. Without a clear, consistent view of who is on the other side of a transaction, even the most advanced models are forced to compensate with complexity.
Before Spade
Like many risk platforms, Sardine initially worked with the merchant data available in standard transaction streams. Merchant Category Codes were often too broad to be useful, and transaction descriptors varied widely across networks and processors.
To fill in the gaps, Sardine relied more heavily on rules, heuristics, and downstream analysis. That approach added operational overhead and made it harder to distinguish genuine fraud from normal behavior, particularly as new merchants and attack patterns emerged.
With Spade
Sardine integrated Spade to enrich transactions with verified merchant identity and standardized merchant attributes in real time at authorization. Instead of inferring merchant behavior from noisy descriptors, Sardine could anchor risk assessments to a consistent, structured view of the merchant behind each transaction.
This improved the quality of signals available before decisions were made — allowing Sardine to approve more legitimate transactions while still identifying true risk. Clearer merchant context reduced false positives tied to ambiguous transactions and lowered the need for ongoing rule tuning and manual merchant mapping.
As transaction volume and customer complexity grew, Spade became a reliable part of how Sardine supported consistent risk evaluation without increasing operational overhead.
Applications beyond Sardine
Sardine’s experience highlights a broader lesson for fintechs building real-time risk systems: when transaction context is unclear, teams may be forced to compensate with complexity. Integrating transaction enrichment improves signal quality at the outset — helping fraud platforms reduce noise, improve precision, and scale protection without multiplying tools or workflows.
To hear directly from Sardine on how real-time merchant data improves authorization decisions, read their blog on this topic.