Learn Fraud Analysis

This dataset contains a card testing attack. Can you find it?

Your Challenge

Card testers verify stolen cards with small charges. Find the suspicious merchant.

Suspicious Merchant

Or dive straight in:

transactions— 311 rows

Schema: id, timestamp, account_id, amount, merchant, category, risk_score, status

Not a Video Course

Write queries. Find fraud. That's it.

Start writing SQL. By the end you're doing z-score analysis in Python notebooks.

Romance scams, account takeover, money laundering, API abuse, credential stuffing. Real fraud patterns, not textbook examples.

Goes from basic SELECT statements to tracing money through shell companies and crypto mixers.

SQL works immediately. Create a free account for Python notebooks.

Each one harder than the last.

Cases 1-3

Stolen cards, fake merchants, elder fraud

CURRENT

Cases 4-6

Card testing, refund abuse, velocity attacks

Cases 7-11

Romance scams, account takeover, BEC, ATM skimming

Cases 12-18

Crypto laundering, shell companies, insider threats, synthetic IDs

Cases 19-21

API exploitation: negative refunds, race conditions, credential stuffing

Cases 22-24

Python notebooks: clustering, anomaly detection, network analysis

A customer reported unauthorized wire transfers. You have the transaction data. Find the fraud.

100% free

No signup required to start

Works on mobile