DVK2026/AMLBench

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.

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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.

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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.

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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).

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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%)

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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

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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.

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