No stitching together a screening tool, a spreadsheet of clients, and a folder of PDFs. Averholm runs the whole anti-money-laundering loop as one connected workbench, on the registers you actually answer to.
Add the companies you're responsible for and Averholm re-reads the official registers every night. A new director, a change of status, a moved seat, a filed insolvency notice — each becomes a typed change event with the before and after, attributed to its source, and severity-ranked so the critical ones surface first.
Companies and their people are matched against the EU consolidated, UN Security Council, OFAC and UK sanctions lists, plus politically-exposed-person data. Matching is tuned for the languages you work in — accents and diacritics folded, name variants and legal-form suffixes handled — so real hits aren't missed and noise is kept down. Nothing is auto-decided: candidates land in a review queue where a person confirms or dismisses, with the reason recorded.
Instead of a calendar reminder every few years, a file reopens the moment something material happens — a critical change event, a new sanctions match — and otherwise on a cadence set by the client's risk tier. The review queue is a live worklist: it tells you who needs attention today and why, and each completed review is written to the audit trail. It's the shape of due diligence AMLR is pushing everyone toward.
Look up any company and Averholm assembles a sourced report: the ownership chain and officers, an A–F financial-health grade from the annual accounts, the screening result, and the risk signals it found — from sanctions exposure to insolvency and adverse indicators. Export it as a PDF for the client file, with every figure attributed to where it came from.
Open a customer-due-diligence case with the standard measures, and an enhanced-diligence block that appears when the risk rating is high. Record your decision, and — when a case warrants it — Averholm assembles a suspicious-activity report in goAML format as a draft. It maps your risk signals to the report's indicators and shapes the XML to the schema; you review the narrative and you file. Averholm never files on its own.
Averholm watches the official gazette and EU legislation and turns each relevant publication into a plain-language impact brief: what changed, and whether it touches an obliged entity like you. A deterministic core detects and classifies; language models only summarise, at the edge, and every brief is marked and human-reviewed before it counts. It's how you keep up with an AMLR-shaped wave of change without reading every issue yourself.
Affects obliged entities filing cash-transaction reports. Action: review your reporting trigger. · NN · human-reviewed
Everything Averholm surfaces is attributed to where it came from, so a finding can always be traced back to a source you can stand behind.
Beneficial ownership is handled through your own lawful access to the register — Averholm orchestrates and monitors the result rather than scraping a gated register on your behalf. It's the defensible model, and it keeps your obligation where it belongs.
Every vendor in this market now says “AI-powered”. Ask them which part. In an AML tool the answer matters, because a supervisor will eventually ask you the same question about the file you signed.
A language model writes the plain-language impact brief on the regulatory radar, and the narrative section of a suspicious-activity report. Both arrive labelled as drafts. Both are worthless until a named person signs them off, and the sign-off goes in the audit trail with their name on it.
The parts that decide anything are deterministic and unit-tested: the diff that produces a change event, the fuzzy matcher that raises a sanctions candidate, the risk logic behind a rating. No model is in that path. A screening hit is reproducible, explainable and identical every time you run it — which is the only kind of hit you can defend.
Why draw the line there. A model that summarises a published law is doing something it is good at, and the worst case is a clumsy sentence a reviewer rewrites. A model that decides whether a person is on a sanctions list is doing something it is bad at, and the worst case is a missed hit you cannot explain to the CSSF. The line is not caution for its own sake — it is drawn where the failure modes change.
We'd rather you trust the tool than be surprised by it. So plainly:
Book a 30-minute demo — we'll show the full loop on live RCS data.