DOI: 10.5281/zenodo.16482143
title: AMLNet - Synthetic Anti-Money Laundering Transaction Dataset

description: **DEPRECATED (Superseded): This record (mislabelled as Version 1.0) is superseded by the corrected release: DOI https://doi.org/10.5281/zenodo.16736515. Do not use this deprecated version for analysis; it contains incomplete configuration/content.

AMLNet - Synthetic Anti-Money Laundering Transaction Dataset

DESCRIPTION:This dataset contains over 1 million synthetic financial transactions (1,048,575) generated using the AMLNet framework for anti-money laundering research.

CONTENTS:- 1,047,028 legitimate transactions across 5 categories- 1,547 labeled money laundering transactions (0.15%)- 192-day simulation period- AUSTRAC-compliant suspicious patterns

DATA FORMAT:CSV file with 16 columns containing transaction details:

CORE TRANSACTION DATA:- step: Sequential transaction step/ID- type: Payment method (TRANSFER, OSKO, BPAY, EFTPOS, DEBIT, NPP)- amount: Transaction amount in AUD (positive/negative values)- category: Transaction category (Housing, Food, Transport, Recreation, Other)- nameOrig: Originating customer ID (e.g., C3511)- nameDest: Destination customer/merchant ID (e.g., C4945, M558)- oldbalanceOrg: Account balance before transaction- newbalanceOrig: Account balance after transaction

LABELS:- isFraud: Binary fraud indicator (0=legitimate, 1=fraudulent)- isMoneyLaundering: Binary AML label (0=normal, 1=suspicious)- fraud_probability: Calculated fraud risk score

TEMPORAL FEATURES:- hour: Hour of transaction (0-23)- day_of_week: Day of week (1=Monday, 7=Sunday)- day_of_month: Day of month (1-31)- month: Month number (1-12)

METADATA:- metadata: JSON object containing:  * timestamp: Exact transaction datetime  * location: City, state, country, postcode  * device_info: Device type, OS, IP address  * payment_method: Specific payment method used  * merchant_info: Merchant details (if applicable)  * risk_indicators: Comprehensive risk scoring metrics

DATASET STATISTICS:- Total transactions: 1,048,575 (1M+)- Legitimate transactions: 1,047,028 (99.85%)- Money laundering transactions: 1,547 (0.15%)- CSV file rows: 1,048,576 (including header row)- Payment types: 6 different methods- Transaction categories: 5 main categories- Time period: 192-day simulation- Geographic coverage: Australian cities and postcodes

USAGE:This dataset is designed for:- Anti-money laundering research and algorithm development- Financial fraud detection benchmarking- Machine learning model training and validation- Academic research in financial crime detection- Commercial AML system development and testing

Licensed under CC BY 4.0. Free to use for any purpose with proper attribution.See LICENSE.txt for full terms.

CITATION:If you use this dataset, please cite:Huda, S., Foo, E., Jadidi, Z., Newton, M.A.H., & Sattar, A. (2025). AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions. Expert Systems with Applications.

CONTACT:s.huda@griffith.edu.au

VERSION: 1.0DATE: July 2025
description: LICENSE.txt

Creative Commons Attribution 4.0 International License (CC BY 4.0)

This dataset is licensed under CC BY 4.0.You are free to:- Share: copy and redistribute the material- Adapt: remix, transform, and build upon the material- Use for any purpose, including commercial purposes

Under the following terms:- Attribution: You must give appropriate credit and indicate if changes were made

Full license: https://creativecommons.org/licenses/by/4.0/