Identify and stop unauthorized account access using behavioral signals, device intelligence, and real-time risk detection.
Account takeover (ATO) happens when someone other than your real customer controls that customer’s login. They might use stolen passwords from a breach elsewhere, phishing that captures a one-time code, or credential stuffing—automated login attempts with reused email/password pairs until one works.
Once inside, they change contact details, add a new bank account, send transfers, or use saved cards. For fintechs, the damage is rarely “just” one user: it erodes trust, triggers regulatory scrutiny, and can scale fast if the same pattern hits many accounts.
Strong account takeover fraud detection treats every login and sensitive action as a risk moment-not only the first password check. For abuse at the payment layer, see our payment fraud detection system guide.
For every scenario in one place, browse our fraud detection use cases.
What legitimate users rarely do—but compromised sessions often do.
Bursts of wrong passwords followed by a success can indicate stuffing or password guessing before the right combo lands.
A session origin that doesn’t match recent history—without a plausible travel or VPN profile your product expects.
First-time device or browser fingerprint for an established user, especially right before a high-risk action.
Different navigation speed, menu paths, or feature usage compared to that user’s own baseline after authentication.
Cookie theft or token replay can show up as IP or device drift mid-session, impossible travel, or two “live” contexts for one account. Correlating device stability with step-up events helps catch it before money moves.
Most ATO still slips through because controls stop at the gate—not at behavior after login.
Users reuse passwords. Breach dumps are cheap. A “correct” password is weak proof that the human you expect is the one typing it.
SMS and push prompts help, but they’re phishable and annoying at scale. Attackers who pass one factor can still look like a “verified” session to downstream systems.
Static rules fire after thresholds you defined last quarter. They miss novel sequences and create noise for ops teams—so real takeovers get buried in false positives.
Continuous signals from device, behavior, and context—scored at login and at sensitive actions.
Stable identifiers for browser and app environments, without treating every new device as automatically hostile.
Compare this session to that user’s history—timing, navigation, and interaction patterns—not only global averages.
One score that combines geography, device, velocity, and reputation signals so you can step up or block in milliseconds.
Challenge, delay, or halt high-risk moves (transfers, credential changes) before the attacker locks the real user out.
We focus on fast, explainable risk at authentication and at money-moving steps—so security and product teams share one language.
Score each sign-in and flag sessions that diverge from the user’s device and behavior fingerprint.
Re-score on sensitive actions so a “clean” login can’t silently turn into a takeover when transfers start.
Same decisioning model whether your users arrive from the app, mobile web, or desktop—where fingerprints differ on purpose.
Built for paths where adding hundreds of milliseconds of friction matters—so you can step up only when the score demands it.
Protect user accounts with real-time detection.
Related: Payment fraud · All use cases · Home