AMLNet

AMLNet is a synthetic anti-money laundering benchmark dataset created for machine learning evaluation.

This Version 2.0 release is associated with the paper:

“AMLNet: A Knowledge-Guided Synthetic Benchmark for Machine Learning Evaluation in Anti-Money Laundering”

The dataset contains a fixed synthetic benchmark instance generated using the AMLNet framework. It includes approximately 1.09 million transactions over a 195-day simulation period, from 13 October 2025 to 27 April 2026. The benchmark contains 1,411 suspicious transactions, corresponding to a suspicious rate of approximately 0.13%.

The dataset is fully synthetic. The accounts, transactions, timestamps, locations, balances, metadata, labels, and customer activity patterns do not correspond to real customers, real institutions, or real banking activity.

AMLNet was designed to support machine learning experiments under rare-event anti-money laundering conditions. The dataset includes ordinary transactions and suspicious transaction sequences representing structuring, layering, and integration patterns. Suspicious activity is embedded within ordinary account activity to make the detection task more realistic and non-trivial.

CONTENTS

This release includes:

– the fixed AMLNet Version 2.0 synthetic transaction dataset;
– transaction labels;
– laundering typology labels;
– transaction metadata;
– dataset documentation and column descriptions.

Evaluation scripts are not included in the current release, but can be made available to reviewers on request for verification. They will be added to the Zenodo record in a subsequent version after publication.

The associated manuscript provides the generator pseudocode, main configuration details, demographic assumptions, regulatory constraints, laundering typologies, timing rules, routing rules, split protocol, and evaluation protocol.

The AMLNet generator source code is not included in this release due to security concerns.  Requests for access to the generator source code can be considered for legitimate research purposes, subject to identity verification, institutional affiliation, and appropriate use conditions. 

DATA FORMAT

The main dataset is provided as a CSV file with the following columns:

– step: Sequential simulation step or transaction index.
– type: Transaction type, such as BPAY, CASH_OUT, DEBIT, EFTPOS, NPP, OSKO, PAYMENT, or TRANSFER.
– amount: Transaction amount in Australian dollars.
– category: Transaction category, such as housing, food, transport, recreation, healthcare, education, utilities, shell company, property investment, cryptocurrency, or other.
– nameOrig: Originating account or customer identifier.
– nameDest: Destination account, customer, or merchant identifier.
– oldbalanceOrg: Originating account b