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README.md

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+# 🏦 BankShield-2M: Synthetic Banking & Fraud Detection Dataset
+
+> **2,000,000 records · 5 relational tables · 64 features · Multi-country · Analyst-labeled**
+> 
+> **[⬇ Get the Full Dataset →](https://synthox.gumroad.com/l/jsyco)**
+
 ---
-license: cc-by-sa-4.0
+
+## Why This Dataset Exists
+
+Every serious fraud-detection, credit-risk, or behavioral-analytics model eventually hits the same wall: **real banking data is locked behind NDAs, GDPR constraints, and institutional gatekeepers**. Public alternatives are either too small, too narrow, or stripped of the relational structure that makes real-world models actually work.
+
+BankShield-2M was engineered to close that gap — a fully synthetic, privacy-safe dataset that mirrors the statistical properties, relational schema, and domain complexity of a real retail bank operating at scale.
+
 ---
+
+## Dataset at a Glance
+
+| Table | Full Dataset | Sample | Key Features |
+|---|---|---|---|
+| `transactions` | 2,000,000 | 5,000 | 12 cols, fraud labels, geo, device, merchant |
+| `customers` | 50,000 | 2,500 | 16 cols, PII-safe, credit score, income, risk tier |
+| `accounts` | 75,000 | 3,750 | 11 cols, IBAN, multi-currency, balance, credit limit |
+| `fraud_alerts` | 22,000 | 1,100 | 9 cols, risk score, analyst notes, alert lifecycle |
+| `devices` | 35,000 | 1,750 | 10 cols, fingerprint, OS, browser, trust status |
+| **Total** | **2,182,000** | **14,100** | **58 features** |
+
+---
+
+## Schema & Relational Structure
+
+```
+customers (customer_id PK)
+    │
+    ├──< accounts (account_id PK, customer_id FK)
+    │       │
+    │       └──< transactions (transaction_id PK, account_id FK)
+    │                   │
+    │                   └──< fraud_alerts (alert_id PK, transaction_id FK)
+    │
+    └──< devices (device_id PK, customer_id FK)
+```
+
+**Full referential integrity across all five tables.** Every `account_id` in transactions traces back to a `customer_id`; every `transaction_id` in alerts traces back to a flagged transaction. This is the graph structure that production fraud systems actually operate on.
+
+---
+
+## Feature Deep-Dive
+
+### `transactions.csv` — The Core Signal Table
+```
+transaction_id    │  UUID, unique per event
+account_id        │  FK → accounts
+transaction_date  │  ISO 8601 with milliseconds (2021–2024)
+merchant_name     │  50+ real-world merchants (Walmart, Apple, Texaco…)
+merchant_category │  11 categories: ONLINE_RETAIL, GROCERY, RESTAURANT,
+                  │  GAS_STATION, TRAVEL, ENTERTAINMENT, ATM_WITHDRAWAL,
+                  │  HEALTHCARE, UTILITY, WIRE_TRANSFER, OTHER
+amount_usd        │  $0.67 – $49,622.28 (median $58, p95 $640)
+transaction_type  │  DEBIT / CREDIT / TRANSFER / REVERSAL
+location_city     │  Real city names across 10+ countries
+location_country  │  US(35%), GB(18%), DE(15%), AE(10%), ES/FR/NL/IT…
+device_type       │  MOBILE_APP(41%), POS_TERMINAL(30%), WEB_BROWSER(21%),
+                  │  ATM(7%), PHONE(1%)
+ip_address        │  Unique IPv4 per transaction
+is_fraud          │  Binary label — 0.84% positive rate (realistic imbalance)
+```
+
+**Why it matters:** The class imbalance of 0.84% is not arbitrary — it mirrors the empirical 0.5–1.5% fraud rate documented across major card networks. Models trained on artificially balanced datasets fail in production; this one won't.
+
+---
+
+### `fraud_alerts.csv` — The Intelligence Layer
+```
+alert_id              │  UUID
+transaction_id        │  FK → transactions
+alert_timestamp       │  When the alert was generated
+alert_type            │  GEO_ANOMALY(30%), ML_MODEL_FLAG(19%),
+                      │  AMOUNT_ANOMALY(18%), VELOCITY_CHECK(15%),
+                      │  BEHAVIORAL_ANOMALY(11%), DEVICE_FINGERPRINT(8%)
+risk_score            │  Continuous [0.102 – 0.989], mean=0.722
+alert_status          │  CONFIRMED_FRAUD(42%), RESOLVED(21%),
+                      │  INVESTIGATING(14%), FALSE_POSITIVE(12%), NEW(10%)
+analyst_notes         │  Free-text investigation notes (NLP-ready)
+resolution_timestamp  │  SLA-trackable, NULL for unresolved cases
+confirmed_fraud       │  Final binary label — 77.4% confirmation rate
+```
+
+**Why it matters:** Six alert types encode the real taxonomy of financial fraud detection — geographic impossibility, behavioral deviation, device compromise, velocity abuse, and ML-model flagging. The analyst notes column is a rare NLP training signal for financial domain adaptation.
+
+---
+
+### `customers.csv` — The Identity Graph
+```
+customer_id       │  UUID
+full_name         │  Internationalized (UK, US, DE, AE, FR, NL names)
+date_of_birth     │  Full age distribution
+gender            │  M / F / Non-binary
+national_id       │  Format-correct per country (SSN, NIN, UAE ID…)
+email             │  Realistic domain distribution
+phone             │  E.164 international format
+address/city/zip  │  Country-coherent (UK postcodes, US ZIPs, DE PLZs)
+country           │  US(39%), GB(20%), DE(15%), AE(11%), CH/IT/NL/FR…
+credit_score      │  FICO-range [300–850], mean=679, std=90
+income_annual_usd │  [$12K – $689K], realistic skew
+customer_since    │  2010–2023 — enables customer lifetime features
+risk_tier         │  HIGH(36%), LOW(26%), MEDIUM(23%), VERY_HIGH(14%)
+is_fraud_suspect  │  2.68% flagged — enables customer-level fraud scoring
+```
+
+---
+
+### `accounts.csv` — The Financial Ledger
+```
+account_id      │  UUID
+customer_id     │  FK → customers (up to 3 accounts per customer)
+account_type    │  CHECKING(44%), SAVINGS(30%), CREDIT(20%), BUSINESS(5%)
+account_number  │  10-digit synthetic number
+iban            │  Format-valid IBANs for GB, DE, US, AE
+currency        │  USD(40%), EUR(24%), GBP(21%), AED(10%), CHF(3%)
+opened_date     │  Account age signal
+balance         │  [-$63K – $1.44M] (negative balances included)
+credit_limit    │  Present only for CREDIT accounts [$517 – $74K]
+status          │  ACTIVE(88%), SUSPENDED(7%), CLOSED(5%)
+is_flagged      │  2.9% — account-level risk signal
+```
+
+---
+
+### `devices.csv` — The Trust & Telemetry Layer
+```
+device_id           │  UUID
+customer_id         │  FK → customers
+device_fingerprint  │  MD5-format hash — unique per device
+device_type         │  MOBILE(55%), DESKTOP(35%), TABLET(10%)
+os                  │  iOS(36%), Android(29%), Windows(20%), macOS(11%), Linux(4%)
+browser             │  App(38%), Chrome(32%), Safari(18%), Firefox(6%), Edge(6%)
+first_seen          │  Device registration date
+last_seen           │  Last activity date — enables recency features
+is_trusted          │  75.7% trusted baseline
+is_fraud_device     │  3.8% compromise rate
+```
+
+---
+
+## What You Can Build
+
+### Supervised Learning — Fraud Detection
+- Binary classifier on `is_fraud` with full feature engineering across all 5 tables
+- Multi-label classification (alert type prediction)
+- Probability calibration benchmarking under real class imbalance (0.84%)
+
+### Risk Scoring & Regression
+- Customer-level risk score modeling using `credit_score`, `income_annual_usd`, `risk_tier`, transaction history
+- Account-level default probability from `balance`, `credit_limit`, `status`, `is_flagged`
+
+### Anomaly Detection (Unsupervised)
+- Isolation Forest / Autoencoder baselines on transaction patterns
+- Device trust scoring from behavioral telemetry
+- Geographic impossibility detection from `location_city/country` + `ip_address`
+
+### Graph Neural Networks
+- Heterogeneous graph: customer → account → transaction → alert
+- Fraud ring detection via shared device fingerprints or IPs
+- Link prediction: which accounts belong to the same fraud ring?
+
+### NLP / LLM Fine-Tuning
+- Analyst notes as training signal for financial-domain LLMs
+- Named entity recognition on merchant names
+- Text classification of `analyst_notes` → `alert_type`
+
+### Time-Series Analysis
+- Transaction velocity features (hourly/daily aggregations)
+- Customer behavioral drift detection over 2021–2024
+- Seasonal fraud pattern analysis
+
+### Multi-Task Learning
+- Simultaneous prediction of `is_fraud`, `risk_score`, and `alert_type`
+- Joint customer + account + transaction risk models
+
+### MLOps & Benchmark Infrastructure
+- Reproducible train/val/test splits with temporal holdout
+- Class-imbalance benchmarking: SMOTE, focal loss, class-weighted XGBoost
+- Model performance baselines on a scale unavailable in public datasets
+
+---
+
+## Statistical Properties
+
+### Realistic Class Distribution
+
+| Signal | Positive Rate | Notes |
+|---|---|---|
+| `transactions.is_fraud` | 0.84% | Matches real-world card fraud rates |
+| `fraud_alerts.confirmed_fraud` | 77.4% | High-quality alert pipeline |
+| `customers.is_fraud_suspect` | 2.68% | Customer-level exposure |
+| `accounts.is_flagged` | 2.93% | Account-level risk |
+| `devices.is_fraud_device` | 3.83% | Compromised device rate |
+
+### Geographic Realism
+
+| Country | Customers | Primary Currency |
+|---|---|---|
+| United States | 39% | USD |
+| United Kingdom | 20% | GBP |
+| Germany | 15% | EUR |
+| UAE | 11% | AED |
+| Switzerland, Italy, Netherlands, France | 15% combined | CHF / EUR |
+
+### Temporal Coverage
+- **Transaction window:** January 2021 – June 2024 (3.5 years)
+- **Customer tenure:** 2010–2023 (14-year range for long-term behavioral modeling)
+- **Alert resolution SLA:** Computable from `alert_timestamp` → `resolution_timestamp` (27% unresolved — mirrors real investigation queues)
+
+---
+
+## Data Quality Notes
+
+| Table | Known Nulls | Notes |
+|---|---|---|
+| `transactions` | 0.38% in `transaction_date` | Realistic ETL artifacts |
+| `fraud_alerts` | 24.5% in `resolution_timestamp` | Unresolved/open investigations |
+| `accounts` | 79.8% in `credit_limit` | NULL only for non-CREDIT accounts |
+| `customers` | 0 | Complete |
+| `devices` | 0 | Complete |
+
+Nulls are **by design**, not data corruption. `credit_limit` is NULL for CHECKING/SAVINGS/BUSINESS accounts because it is inapplicable. Unresolved `resolution_timestamp` values represent active investigation cases — a feature, not a bug.
+
+---
+
+## Comparison to Existing Public Datasets
+
+| Dataset | Records | Tables | Fraud Labels | Relational | Multi-Country | Analyst Notes |
+|---|---|---|---|---|---|---|
+| **BankShield-2M** | **2M+** | **5** | **✅ Multi-level** | **✅ Full FK** | **✅ 10+ countries** | **✅ Yes** |
+| IEEE-CIS Fraud 2019 | 590K | 2 | ✅ | ❌ | ❌ | ❌ |
+| PaySim | 6.3M | 1 | ✅ | ❌ | ❌ | ❌ |
+| Credit Card Fraud (Kaggle) | 284K | 1 | ✅ | ❌ | ❌ | ❌ |
+| BankSim | 594K | 1 | ✅ | ❌ | ❌ | ❌ |
+
+---
+
+## License & Usage
+
+- **Fully synthetic** — no real individuals, no PII, GDPR/CCPA compliant
+- **Commercial use permitted** under the dataset license
+- Suitable for academic research, ML product development, FinTech prototyping, red-team simulation, and fraud analytics education
+
+---
+
+## Get the Full 2-Million-Record Dataset
+
+The files in this repository are a **0.25% sample** of the complete dataset.
+
+The full release includes:
+- `transactions.csv` — 2,000,000 rows
+- `customers.csv` — 50,000 rows
+- `accounts.csv` — 75,000 rows
+- `fraud_alerts.csv` — 22,000 rows
+- `devices.csv` — 35,000 rows
+- Data dictionary (`schema.md`)
+- Suggested train/val/test split methodology
+
+**[⬇ Purchase on Gumroad →](https://synthox.gumroad.com/l/jsyco)**
+
+---
+
+## Citation
+
+If you use this dataset in academic work:
+
+```bibtex
+@dataset{BankShield2m_2024,
+  title     = {BankShield-2M: Synthetic Banking and Fraud Detection Dataset},
+  year      = {2024},
+  publisher = {Synthox},
+  url       = {https://synthox.gumroad.com/l/jsyco}
+}
+```
+
+---
+
+*Dataset generated and maintained by [Synthox](https://synthox.gumroad.com). For questions, feature requests, or bulk licensing, contact via Gumroad.*