Fraudmatic scores every transaction in real time — catching suspicious patterns before money moves. Think payment fraud detection, account takeover prevention, and fake account detection in one flow.
Fraud detection in fintech is not a single problem — it spans payments, account security, fake users, and automated abuse. This page breaks down real-world fraud scenarios and how modern systems detect them in real time before transactions complete. Explore detailed fraud detection use cases below.
Each pattern below is a real attack vector. Fraudmatic surfaces signals for all of them inside a single risk score — before your transaction settles.
Stolen cards, high-value anomalies, and rapid retries are classic attack patterns. We catch them the moment the request hits your API.
payment fraud detection system →
When a legitimate user's credentials are compromised, attacker behaviour is subtly different. We flag it the moment they try to act.
account takeover fraud detection →
Bot farms and identity farms spin up hundreds of accounts to abuse promotions, referrals, or payment systems. We catch them at signup.
Automated scripts probe limits, drain wallets, or abuse cashback loops at machine speed. Pattern detection stops them before they scale.
Fraudmatic combines three layers of intelligence into a single real-time risk score — returned in under 100ms, before your transaction completes.
Fingerprint every device silently. Track re-use, spoofing attempts, and emulator signals across your user base.
Compare in-session actions against a user's own baseline — not just aggregate norms. Anomalies surface faster.
Amount, frequency, merchant type, time of day — all weighted together against your rule-based engine.
Tell us your use case and we'll walk you through exactly how Fraudmatic would handle it — live, in under 30 minutes.
No commitment. Prefer email-only? Join the waitlist on the homepage.