FinBank catches 2× more fraud with no-code ML

{ TODO: replace with real customer story. This is a Phase 2 mock. }

The challenge

FinBank was losing to fraud rings that exploited the gaps between their rules-based detection system and their overnight batch scoring. They needed real-time predictions grounded in the graph of accounts, transactions, and devices — but didn’t want to hire an in-house ML team.

The Langsat approach

• Ingested 12 months of transactions, account metadata, and device fingerprints
• Trained a relational model that learns from fraud rings, not isolated signals
• Integrated via REST API for real-time transaction scoring

Outcomes after 3 weeks

• 2.1× more confirmed fraud caught at the same false-positive rate
• < 50ms p99 inference latency
• 0 lines of ML code written internally

{ TODO: add compliance/SOC2 notes and CFO quote. }