Abdulmajeedyahya/BankShield-Enterprise-Grade-Multi-Pattern-Dataset

🏦 BankShield-2M: Synthetic Banking & Fraud Detection Dataset

2,000,000 records · 5 relational tables · 64 features · Multi-country · Analyst-labeled

⬇ Get the Full Dataset →

---

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 →

---

Citation

If you use this dataset in academic work:

@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. For questions, feature requests, or bulk licensing, contact via Gumroad.