AML supervisors oversee large, varied populations with small teams, from banks to accountants and estate agents. Line4 structures AML returns, builds risk-based views across the whole population, and keeps the evidence ready for a FATF mutual evaluation.
AI structures years of inconsistent AML returns so the whole population can be compared and risk-assessed.
Population-wide risk views that direct a small team’s attention to the firms that matter most.
Structured, queryable data and an evidence trail that show how supervision works and how risk is assessed.
Proven in production
AML supervision intelligence, enforcement case management, a prudential submissions portal with real-time validation, and an incumbent vendor platform replaced. Competitors quoted eighteen months.
Who it is for: Financial sector AML supervisors, DNFBP supervisors and self-regulatory bodies, Financial intelligence and supervision liaison, Enforcement teams.
Read the case study →Yes. The approach applies to any AML-supervised population, including lawyers, accountants, estate agents and dealers. We shape the data model to your returns with you.
Yes. Regulator-ready AML analytics and a supervisor chatbot built by Line4 were used for a FATF mutual evaluation at an international financial regulator.
That is the point. Our engineers work inside your team, so you get the capability without hiring a SupTech department first.