Create README.md

README.md

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+---
+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
+
+```text
+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
+
+```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": { "<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)
+
+```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.
+
+---