| # 🏦 BankShield-2M: Synthetic Banking & Fraud Detection Dataset |
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| > **2,000,000 records · 5 relational tables · 64 features · Multi-country · Analyst-labeled** |
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| > **[⬇ Get the Full Dataset →](https://synthox.gumroad.com/l/jsyco)** |
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| ## Why This Dataset Exists |
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| 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. |
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| 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. |
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| ## Dataset at a Glance |
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| | 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** | |
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| ## Schema & Relational Structure |
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| ``` |
| 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) |
| ``` |
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| **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. |
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| ## Feature Deep-Dive |
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| ### `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) |
| ``` |
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| **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. |
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| ### `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 |
| ``` |
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| **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. |
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| ### `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 |
| ``` |
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| ### `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 |
| ``` |
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| --- |
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| ### `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 |
| ``` |
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| ## What You Can Build |
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| ### 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%) |
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| ### 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` |
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| ### 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` |
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| ### 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? |
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| ### 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` |
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| ### Time-Series Analysis |
| - Transaction velocity features (hourly/daily aggregations) |
| - Customer behavioral drift detection over 2021–2024 |
| - Seasonal fraud pattern analysis |
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| ### Multi-Task Learning |
| - Simultaneous prediction of `is_fraud`, `risk_score`, and `alert_type` |
| - Joint customer + account + transaction risk models |
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| ### 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 |
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| ## Statistical Properties |
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| ### Realistic Class Distribution |
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| | 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 | |
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| ### Geographic Realism |
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| | 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 | |
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| ### 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) |
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| ## Data Quality Notes |
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| | 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 | |
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| 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. |
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| ## Comparison to Existing Public Datasets |
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| | 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 | ✅ | ❌ | ❌ | ❌ | |
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| ## License & Usage |
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| - **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 |
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| ## Get the Full 2-Million-Record Dataset |
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| The files in this repository are a **0.25% sample** of the complete dataset. |
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| 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 |
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| **[⬇ Purchase on Gumroad →](https://synthox.gumroad.com/l/jsyco)** |
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| ## Citation |
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| If you use this dataset in academic work: |
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| ```bibtex |
| @dataset{BankShield2m_2024, |
| title = {BankShield-2M: Synthetic Banking and Fraud Detection Dataset}, |
| year = {2024}, |
| publisher = {Synthox}, |
| url = {https://synthox.gumroad.com/l/jsyco} |
| } |
| ``` |
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| *Dataset generated and maintained by [Synthox](https://synthox.gumroad.com). For questions, feature requests, or bulk licensing, contact via Gumroad.* |
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