Interactive synthetic AML demonstration · IBM research dataset · Human review required

$3.1 Trillion
in Illicit Funds.
One Deterministic
Evidence Trail.

The Axiomyx Platform builds the evidence ledger before AI writes a word — canonical payload, β/Df/Dh signature, Zone v2 placement, graph path, and reproducibility hash. Generated narrative is shown separately from those deterministic outputs and remains subject to human compliance review. This demonstration does not establish production detection performance.

25
Synthetic transactions displayed in this bounded demonstration
24
Synthetic records routed for review in the displayed sample
100%
Displayed records with a demonstration evidence path
5
Deterministic platform modules — Canonical Pipeline to Graph Provenance
The problem today

Three review risks this demonstration makes visible

Many AML AI workflows depend on probabilistic scoring, embeddings, or free-form narrative generation. That creates governance pressure: investigators need to know why a transaction was flagged, what evidence was linked, and whether the result can be reproduced.

🎭

Generated Claims

Free-form narrative can introduce relationships that are not present in source evidence. Reviewers need a clear boundary between generated prose and recorded evidence.

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Variable Interpretation

Model wording and interpretation can vary. Reproducible evidence fields make it possible to compare the generated narrative with the same recorded inputs.

🕳️

Missing Evidence Route

An alert without a source and transformation path is harder to examine. Graph Provenance demonstrates an inspectable route for this synthetic sample.

The Axiomyx solution

Five modules. One deterministic money trail.

Click a synthetic transaction and follow it from raw record to deterministic evidence outputs and a separately labelled AI-generated investigation narrative. The result remains a research demonstration, not a regulatory decision.

IBM AML Dataset — Live Sample
Processing

Dataset: IBM public synthetic AML transaction data (IBM/AML-Data). CSV financial transactions with laundering tags used for benchmark evaluation. Repository licence: Apache-2.0. Data licence: CDLA-Sharing-1.0. Independent Axiomyx benchmark — no IBM endorsement implied.

Working synthetic output

What the bounded demonstration produces

Displayed transactions pass through the five-component pipeline. Canonical fields, geometric placement, relationship outputs and provenance are deterministic demo evidence; the AI investigation narrative is generated prose that requires review.

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Platform Metrics
Live processing stats — IBM dataset
25
Processed
24
Flagged
24
For Review
7B-S2
Hot Zone
100%
Reproducibility
12ms
Avg Pipeline
Geometry Core — Zone v2 Map (Dh × β)

🔴 Suspicious cluster   🟡 Under review   🔵 Normal pattern

🚨
Live Alert Feed
AI-written SAR-style investigation narratives · Full audit trail
Full audit trail

What a reviewer can inspect in this demonstration

Each displayed transaction and deterministic component output is timestamped beside the generated narrative. The evidence fields can be replayed; the model prose is not represented as deterministic truth or a regulatory filing.

Timestamp Transaction ID Amount β / Df / Dh Zone Entity Match Risk SAR Narrative
Next step

The dataset is synthetic.
The evidence path is inspectable.
The decision remains human.

This working reference shows five deterministic components processing synthetic transactions into inspectable evidence before AI writes a word. It does not prove live detection performance, eliminate governance work or replace a qualified compliance decision.

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