--- 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/ # 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 ```json { "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": { "": 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) ```python 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 ```python 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 ```python 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. ---