FraudForge โ€” Synthetic Fraud Data for Fintech ML Teams
๐Ÿ”’ Zero PII ยท GDPR-safe ยท Production-ready
โšก The only purpose-built fraud data platform

Synthetic fraud data for fintech ML teams

Stop waiting on compliance. Stop labeling by hand. Get realistic synthetic transaction data โ€” structured fields + fraud narratives โ€” across 4 dataset types, ready to train your model today.

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Only on FraudForge: Every record includes a fraud_narrative โ€” a human-readable explanation of why the transaction is fraudulent. Unique LLM fine-tuning signal; no other synthetic dataset has it.

Sample record from FraudForge

FRAUD โœ—
{
  "transaction_id": "synth_a3f2b91c",
  "card_hash": "card_7d4e9f2a1b3c",
  "amount_usd": 4250.00,
  "merchant_mcc": "5944",
  "merchant_mcc_label": "Jewelry Stores",
  "timestamp": "2026-06-15T02:47:00Z",
  "hour_of_day": 2,
  "location": { "city": "Lagos", "region": "NG" },
  "is_card_present": false,
  "velocity_last_1h": 6,
  "velocity_last_24h": 22,
  "distance_from_home_km": 8432.1,
  "fraud_label": 1,
  "fraud_pattern": "account_takeover",
  "fraud_score": 0.934,
  "fraud_narrative": "New device fingerprint โ€” no match to cardholder's known devices. Transaction in Lagos (8,432km from billing address) at 2:47 local time..."
}

Real fraud data is a nightmare.

Your ML team needs millions of labeled examples. Here's what getting real data actually looks like.

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PII compliance blocks everything

Real transaction data has cardholder names, cards, and behavioral signals. Every dataset needs legal review, DPA agreements, and audit trails. 6 months before your data engineer can even open the file.

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Labeling fraud is slow and expensive

Hand-labeling fraud patterns takes months. Rare fraud types (synthetic identity, bust-out rings) are underrepresented in real data โ€” exactly the cases your model needs most.

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Class imbalance kills your model

Real fraud rates are 0.1-2%. Training on imbalanced data produces models that flag nothing or everything. You need controlled synthetic datasets with exactly the right fraud-to-legit ratio.

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AML models starve for training data

Anti-money laundering models need layered, multi-hop transaction patterns that almost never appear cleanly in real data. Compliance teams can't share what they have. Synthetic is the only path.

Ready to train in 48 hours.

No legal review. No labeling sprint. No PII headaches.

1

Tell us your specs

Dataset size, dataset type (General / Credit Card / Banking / P2P), fraud rate (5%โ€“50%, your choice), patterns to include. We configure the generator to match your model.

2

We generate your dataset

Realistic synthetic transactions โ€” structured fields + narrative fraud descriptions โ€” generated to your exact specifications. JSON + CSV, ready for your pipeline.

3

Train and ship

Drop the dataset into your training pipeline. No DPA. No compliance sign-off. Your model is in production faster.

8 fraud patterns. All included.

Every dataset includes configurable proportions of each pattern โ€” including the rare ones your real data doesn't have enough of.

Account Takeover

New device, off-hours, geographic anomaly

Card Not Present

CNP, billing mismatch, CVV failures

Merchant Routing

Shell merchants, MCC mismatch, ring activity

Multi-Card Ring

Coordinated cluster, cross-bank, cash-out

Velocity Abuse

High-frequency low-value, systematic draining

Social Engineering

Authorized push payment, KBA coaching

Synthetic Identity

ITIN, mail drop, thin-file bust-out

Bust-Out

Rapid spend, no payments, credit limit approach

Money Laundering

Layered transfers, smurfing, structuring below reporting thresholds

Simple pricing. 4 dataset types.

Choose General, Credit Card, Banking, or P2P. Standard or Enhanced. Pay only for records you need.

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General

Multi-pattern fraud across all verticals

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Credit Card

CNP, chargebacks, card testing patterns

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Banking

ACH fraud, wire fraud, AML patterns

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P2P

Zelle, Venmo, CashApp fraud patterns

Standard

$0.05/record (1โ€“10K)

$500 base + $0.03/record over 10K

  • โœ“ 21 fields per record
  • โœ“ 8 fraud patterns + narratives
  • โœ“ Configurable fraud rate (5โ€“50%)
  • โœ“ JSON + CSV, instant delivery
Get free sample โ†’

Enhanced โญ

$0.075/record (1โ€“10K)

$750 base + $0.045/record over 10K

  • โœ“ 42โ€“48 fields per record
  • โœ“ VPN detection, auth signals, device/OS/browser
  • โœ“ Risk reason codes + merchant MCC risk scores
  • โœ“ Temporal features + velocity numerics
Get free sample โ†’

Example: Credit Card Enhanced, 25K records = $750 base + (15K ร— $0.045) = $1,425

10K Standard

$500

10K Enhanced

$750

25K Enhanced

$1,425

Every dataset includes:

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Fraud narratives

Human-readable explanation per record. Unique LLM fine-tuning signal โ€” no other synthetic dataset has this.

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Configurable fraud rate

5%, 10%, 25%, or 50%. Unlike competitors locked at 0.84% โ€” you control class distribution for your model.

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All 8+ fraud patterns + AML

Account takeover, synthetic ID, bust-out, CNP, velocity abuse, merchant routing, social engineering, multi-card rings, money laundering.

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JSON + CSV, instant delivery

Signed S3 URL delivered to your inbox within minutes. No account required for samples.

Enterprise (100K+ records)? Custom patterns, custom schema, volume discounts. Get in touch โ†’

Get your free sample

1,000 synthetic fraud transactions. All 8 patterns. No credit card required. Delivered within 24 hours.

After you download, we'll send you three emails: Try it, Scale it, and Talk to us. Pick whichever fits.