CYBERDETECT

AI and machine learning fraud and threat detection

AI fraud detection, AI threat detection, machine learning fraud detection, and machine learning threat detection, across SIM swap, payment fraud, OTP relay, account takeover, credential stuffing, impossible travel, new device, email phishing, smishing, eSIM clone, IMSI catcher, ATM fraud, and behavioral anomaly.

Each threat is read from logs and events you already produce, then linked into one incident. Alerts go to Slack, Teams, or your SOAR.

  • SIM swap detection for mobile operators

    SIM swap, eSIM clone, and IMSI catcher leave traces in separate systems. No single tool reads them together.

  • Payment fraud detection

    A bad payment still looks like a normal charge inside the processor. The login that came before it sits in a different system.

  • OTP relay detection

    The one-time code is approved, then the session continues from somewhere else. Each system records a success.

  • Account takeover detection

    A takeover crosses the login, the device, and the payment. Each tool only sees its own piece.

  • Credential stuffing detection

    Stolen passwords are tried across accounts in a burst. Each failure looks like a user who forgot a password.

  • Impossible travel detection

    Two successful logins, two countries, and not enough time to have made the trip. Each login is valid on its own.

  • New device detection

    The password is correct and the one-time code may be correct. The device has never been seen on that account.

  • Email phishing detection

    A lookalike invoice or a login link does not show up in the identity system until someone has already clicked.

  • Smishing detection

    A text that imitates the operator or a bank is gone by the time the fraud team hears about the payment.

  • eSIM clone detection

    A cloned SIM or eSIM keeps the number and moves the service. The customer still looks registered.

  • IMSI catcher detection

    A catcher sits near the subscriber and the core network never has to be opened for the trace to exist in the events you already store.

  • ATM fraud detection

    Cash-out and card use can be accepted by the network and still be the wrong pattern for that card.

  • Behavioral anomaly detection

    The fraud that does not match a named rule still shows up as a break in how that user normally behaves.