Datasets:
license: cc-by-nc-4.0
language:
- en
task_categories:
- tabular-classification
tags:
- synthetic-data
- fraud-detection
- fintech
size_categories:
- 10M<n<100M
Fraud Detection Dataset
A production-grade, large-scale dataset containing 50 million transactions designed to serve both the beginner data scientist and the enterprise data engineer.
What is inside:
- 50 million rows of high-fidelity synthetic transaction data
- 19 carefully engineered fields covering core transaction data, velocity signals, geospatial context, device fingerprinting, and diagnostic fraud labels
- A realistic 2.5% fraud rate simulating the exact needle-in-a-haystack challenge faced by real fraud teams at major financial institutions
- Granular
fraud_typelabels enabling multi-class classification, not just binary detection
File formats provided:
sample_50rows.csv— No gate. Inspect immediately.fraud_detection_50M.parquet— Full 50 million row file. No gate. Free access.
Quick load example:
import pandas as pd
df = pd.read_parquet('fraud_detection_50M.parquet')
print(df.head())
Schema Overview:
| Block | Fields |
|---|---|
| Core | transaction_id, timestamp, user_id, amount, currency, merchant_id, merchant_category, card_type, location_country |
| Velocity & Context | ip_address, device_id, shipping_zip, billing_zip, is_international, account_age_days, prev_transaction_amt |
| Diagnostic | fraud_label, fraud_type |
| Metadata | metadata_marker |
Our New Home
Visit our official new home where our full portfolio of datasets lives and grows:
Zia-Data-Labs on Hugging Face — https://huggingface.co/Zia-Data-Labs
Contact
License & Usage CC BY-NC 4.0 — Attribution-NonCommercial This dataset is released under CC BY-NC 4.0. You are free to use, share, and adapt this dataset for personal, research, academic, and educational purposes, provided you give appropriate credit to Zia Data Labs. Commercial use is not permitted without separate written permission. Full license text: https://creativecommons.org/licenses/by-nc/4.0/