Fraud detection
[ TODO: real-time transaction scoring with sub-100ms latency. Graph of accounts, transactions, devices, and merchants. ]
SOLUTIONS · FINANCE
ML that catches fraud rings — not just isolated signals
Langsat's relational ML learns from the graph of accounts, transactions, devices, and counterparties. Deployed in your perimeter for compliance.
USE CASES
What finance teams build
Credit scoring
[ TODO: complement (or replace) bureau scores with signals from your own transaction history. ]
Transaction categorization
[ TODO: label raw transaction descriptors with canonical categories for PFM and tax products. ]
AML signals
[ TODO: anomaly detection on the transaction graph to surface structured-transfer risk. ]
CUSTOMER
FinBank: 2× fraud caught at the same false-positive rate
A digital bank replaced rules-based fraud detection in 3 weeks.
[ TODO: one-paragraph summary. ]
Read the full story →Talk to the fraud team
See how Langsat plugs into your real-time decisioning stack.