Fraud systems are hard to test well.

Not because engineers cannot write tests, but because the data needs to tell a story. A suspicious transaction is rarely suspicious because of one field. It is usually suspicious because of patterns:

a new account with too many beneficiaries

many transactions in a short window

failed logins followed by a device change

KYC failures mixed with country mismatches

transaction amounts far above normal behavior

Using production customer data for this is risky and often unacceptable. Basic mock data is usually too flat.

So I built fintech-fraud-sim , a TypeScript CLI that generates synthetic fintech users and transactions with configurable fraud patterns.

NPM package: https://www.npmjs.com/package/fintech-fraud-sim

Quick Start

npx fintech-fraud-sim generate --users 1000 --fraud-rate 0.08

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That command generates:

users.csv transactions.csv users.json transactions.json summary.json

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