AML supervision software for financial regulators
Line4 turns AML supervision data, often years of it in inconsistent spreadsheets, into structured data supervisors can analyse and query. AI does the structuring and reading; deterministic rules and an evidence trail keep findings explainable.
The problem today
- Years of AML returns sit in inconsistent Excel templates nobody can analyse together.
- Risk assessment of the supervised population depends on manual review.
- Preparing evidence for a FATF mutual evaluation means weeks of pulling data by hand.
How Line4 does it
- 01
Structure the history
An AI pipeline infers structure from messy templates, so past and present returns can be analysed together.
- 02
Analytics supervisors use
Risk views across the supervised population, with drill-down to the firm and the underlying return.
- 03
Ask in plain language
Supervisors query their own data conversationally, with answers that link back to the source.
- 04
Explainable by design
Rules make the calls that must be repeatable, and every finding traces to its data and rule.
Proven in production
At an international financial regulator
- Years of AML supervision data at an international financial regulator structured from messy Excel templates by an AI pipeline.
- Regulator-ready analytics and a supervisor chatbot used for the FATF mutual evaluation.
- Now live, with self-serve insight for analysts.
Questions
Does this work for DNFBP supervision?
The approach applies to any AML-supervised population, including designated non-financial businesses and professions. We shape the data model to your returns and sectors with you.
Can it help with a FATF mutual evaluation?
Yes. Structured, queryable AML supervision data makes it far faster to evidence how supervision works and how risk is assessed. It was used this way ahead of a FATF mutual evaluation.
Where does the data live?
Inside your environment and security perimeter. Our engineers work under your governance.