Datasets:
license: cc-by-4.0
task_categories:
- graph-ml
- tabular-classification
language:
- en
tags:
- anti-money-laundering
- AML
- financial-crime
- fraud-detection
- graph-neural-networks
- GNN
- synthetic
- banking
- transactions
- PyTorch-Geometric
- Neo4j
pretty_name: AusAML — Synthetic Australian Banking Dataset for AML Detection
size_categories:
- 10M<n<100M
AusAML: Synthetic Australian Banking Dataset for AML Detection
AusAML is a large-scale synthetic banking dataset designed for training and benchmarking graph neural network (GNN) models on anti-money laundering (AML) detection tasks. It simulates a realistic four-bank Australian financial ecosystem with fully labelled AML typology scenarios embedded within a background of legitimate transaction activity.
Dataset Summary
| Property | Value |
|---|---|
| Jurisdiction | Australia (AU) |
| Banks | 4 (Yellow, Red, White, Orange) |
| Customers | 50,000 (primary) |
| Accounts | 112,620 |
| Transactions | 35,554,888 |
| Date range | 2025-11-01 → 2026-05-31 (7 months) |
| AML customers | 2,502 (5.0% of total) |
| AML scenario instances | 740 across 4 banks (297 unique) |
| AML typologies | 29 |
| Random seed | 42 |
All personally identifiable information is entirely synthetic. No real customer, account, or transaction data was used.
AML Typologies
The dataset covers 29 distinct money-laundering typologies spanning cash structuring, network-based layering, digital-asset obfuscation, and professional abuse patterns:
| Category | Typologies |
|---|---|
| Cash structuring | smurfing, micro_structuring, exchange_micro_structuring, geo_smurfing |
| Network layering | layering, chain_branch, circular_ring, split_merge, fanin_fanout, hub_network, mule_network, pass_through, funnel_account, intrabank_hub |
| Burst & timing | burst_transfers, dormant_activation |
| Cash-intensive business | cash_intensive, gambling_wash, gambling_rapid_cycling, insurance_cycling |
| Digital assets | crypto_atm_withdrawal, crypto_mining_wash, crypto_salary_obfuscation, stablecoin_chain |
| Professional & corporate abuse | ghost_payroll, corporate_round_robin, director_loan_recycling, loan_back, hawala_mirror |
Each typology has 7–12 scenario instances per bank. Scenario boundaries (customer IDs, account IDs, date window, signal columns) are recorded in labels.json.
Dataset Structure
Files
oneview/
shared/
labels.json # Ground-truth: all 297 unique scenarios + node labels + train/val/test splits
dataset_card.md # Auto-generated summary
parquet/ # Combined Parquet files (all tables, all banks merged)
pyg/ # PyTorch Geometric tensors (node features, edge index, masks)
neo4j/ # Neo4j Cypher load files (combined graph)
<bank_id>/ # Per-bank outputs for: yellow_bank, red_bank, white_bank, orange_bank
parquet/ # Per-bank Parquet files
pyg/ # Per-bank PyG tensors
neo4j/ # Per-bank Neo4j Cypher files
shared/
labels.json # Per-bank filtered labels (cross-bank scenarios include all_account_ids)
Schema (30+ tables)
Core tables across all banks:
| Table group | Tables |
|---|---|
| Customers | customer_master, customer_individual, customer_business, customer_trust, customer_identity, customer_address, customer_kyc, customer_risk_profile, customer_financial_profile |
| Accounts | account_base, account_deposit, account_credit_card, account_loan, account_term_deposit, account_behavioral_baseline |
| Transactions | transaction_base, transaction_card_pos, transaction_bpay, transaction_transfer_internal, transaction_transfer_external, transaction_payid, transaction_wire_international, transaction_cash_deposit, transaction_cash_withdrawal, transaction_direct_debit, transaction_digital_context |
| AML labels | _aml_designations, _scenario_log |
Graph Schema
For GNN use, the dataset exports a heterogeneous graph with:
- Node types (6): Customer, Account, Device, IP, Beneficiary, Bank
- Edge types (6): Owns, Has_Relationship, Uses, In_Scenario, Transfers_To, Is_Located_In
labels.json Structure
{
"scenarios": [
{
"scenario_id": "SCN_0001",
"typology": "smurfing",
"customer_ids": ["uuid", ...],
"account_ids": ["uuid", ...],
"date_from": "2026-02-16",
"date_to": "2026-03-06",
"signal_columns": ["TRANSACTION_CASH_DEPOSIT.note_count", ...]
}
],
"node_labels": { "<customer_id>": 1, ... },
"splits": { "train": [...], "val": [...], "test": [...] }
}
node_labels maps each customer UUID to 1 (AML) or 0 (legitimate). Note: the predefined train/val/test split assigns all 297 scenarios to train because the 7-month window does not provide sufficient temporal separation for held-out splits; users should define their own splits for evaluation.
Customer Population
| Segment | Proportion |
|---|---|
| Individual | 75% |
| Business | 20% |
| Trust | 5% |
| Domestic (AU resident) | 85% |
| International | 15% |
| Cross-bank (2 banks) | 20% |
| Cross-bank (3 banks) | 5% |
Additional customer attributes: KYC status, risk rating, PEP/sanctions screening (2.0% PEP rate), adverse media flags (3.0%), employment type, annual income (log-normal, mean AUD 238k).
Transaction Characteristics
| Metric | Value |
|---|---|
| Avg transactions / account / month | ~44 (individual deposit) |
| Weekday/weekend volume ratio | 2.6× |
| Business-hours (07:00–22:00) POS/ATM share | 80% |
| PayID proportion (individual accounts) | 8.5% |
| International wire destinations | 40 countries |
| FATF high-risk wire proportion | 15% |
| CTR-flagged cash deposits (≥ AUD 10,000) | 3.4% of cash deposits |
| Mobile transaction share | 48.8% |
| Benford's Law first-digit '1' | 33.3% (expected 30.1%) |
Usage
Load Parquet (pandas)
import pandas as pd
txn = pd.read_parquet("oneview/parquet/transaction_base.parquet")
cust = pd.read_parquet("oneview/parquet/customer_master.parquet")
Load Ground-Truth Labels
import json
with open("oneview/shared/labels.json") as f:
labels = json.load(f)
# Dict mapping customer_id → 0/1
node_labels = labels["node_labels"]
# All AML scenarios with their account IDs and date windows
scenarios = labels["scenarios"]
Load PyTorch Geometric Graph
import torch
data = torch.load("oneview/pyg/graph.pt")
# data.x — node feature matrix
# data.edge_index — edge connectivity
# data.y — node labels (0 = legitimate, 1 = AML)
# data.train_mask — training mask
Considerations for Using the Data
Intended Use
- Training and benchmarking GNN-based AML detection models
- Evaluating graph anomaly detection, link prediction, and node classification algorithms in a financial-crime context
- Studying multi-bank, cross-institution typology patterns
Out-of-Scope Use
This dataset must not be used to build systems intended to evade AML controls, launder money, or circumvent financial crime detection in production systems.
Limitations
- Synthetic only: The dataset does not reflect the full complexity of real banking data, including regulatory edge cases, legacy system artefacts, or jurisdiction-specific product variations beyond the Australian configuration.
- Balanced typology coverage: Scenario instances per typology are capped (7–12 per bank) to ensure coverage breadth. Real AML distributions are highly skewed toward a small number of typologies.
- Payroll–income alignment: Synthetic payroll amounts are sampled independently from declared annual income; ~44% of customers fall within ±40% of their declared income (a known generator constraint).
- Class imbalance: AML customers are 5% of the population by design. Downstream models should account for this imbalance (weighted loss, oversampling, etc.).
- No held-out test split: All 297 scenarios fall within the training window. Users evaluating generalisation should define their own typology-held-out or time-based splits.
Social Impact
The dataset is designed to advance automated AML detection, which supports financial crime prevention. No real individuals are represented.