account takeover Fraud

Identify account takeover fraud without disrupting the user experience.

Tackling the Most Elusive Cyberthreat

Account fraud is driven by criminals who understand how the system works and know how to beat it. The fact is, 100% of fraud comes from authenticated sessions. Today’s fraudsters are simply waiting in the background for the right time to maliciously exploit someone else’s access. A new generation of techniques, including Man-in-the-Browser (MitB) attacks, data injection and manipulation, Remote Access Trojans (RAT) attacks and spoofing, are making traditional login techniques ineffective and obsolete.

Addressing authentication, malware and social engineering, our system detects account takeover and automated fraud by making a clear distinction between an authorized user’s behavior and that of an intruder. Whether another human, a piece of malware or robotic activity, our technology offers real-time alerts to prevent fraud and protect assets.

How BioCatch Helps Prevent Account Takeover Fraud

Applying more than 500 unique metrics to analyze behavior, BioCatch can distinguish between a legitimate user and an imposter to prevent account takeover fraud. Our platform detects fraud in real time by identifying threats like:

  • Credential theft — Obtaining user credentials is easier than ever for fraudsters. Phishing is on the rise and every data breach offers more personally identifying information to exploit. With user credentials gathered from various sources and techniques,fraudsters accesses an account from their own devices. Because behavioral biometrics doesn’t rely on static identifiers, our system can detect when stolen credentials are being used in account takeover fraud.
  • Malware — Malware can initiate various actions, such as money transfers or changing payee information, in the background while a user is logged into an online banking application. Behavioral biometrics detects when malware is being used for account takeover fraud by continuously monitoring a session, looking for any suspicious behavior or activity. If behavior diverges from a user’s norm, our system flags the account for potential fraud.
  • Social engineering — In social engineering attacks, a fraudster gains the confidence of a victim to manipulate them into handing over or entering personal, confidential information. Fraudsters gain remote control over a user’s device — using malware or having gained the unsuspecting users’ trust — and take control of a machine. Behavioral biometrics detects social engineering by recognizing how information is entered, not whether the correct credentials were used.

By modeling different types of genuine and malicious behavior and employing real-time counter measures, our system can detect machine/bot activity, targeted malware, spoofing, and other fraudulent activity before any damage is done.