Deep learning for online fraud prevention

PROTECTION FROM ANY TYPE OF FRAUD ATTACK

bugFraud detects the greatest number of online fraud cases on the market.

Account Takeover
Remote Access Tool
Malware

identification of fraudsters in your online channels

bugFraud fraudster hunter finds the fraudsters hidden behind supposedly legitimate user accounts among your customers.

continuous, invisible protection in real time

From log in to log out, bugfraud provides continous, frictionless protection for customer sessions in real time, even preventing the fraud generated in the onboarding phase (New Account Fraud).

FALSE POSITIVES OR NEGATIVES REDUCTION

bugFraud’s advanced behavioral biometrics algorithms detect deviations in customer behavior in the fastest, most precise way compared to other solutions offered by the competition.

DEVICES

More than 30 parameters make it possible to profile users’ devices and identify associated anomalies. Is it a normally-used device? Is it a device that is at risk or compromised? Etc.

THREAT INTELLIGENCE

Enriches online user session information with intelligence on external threats, to improve fraud risk assignment.

NETWORK

Identifies attackers who attempt to mask their connections using anonymization systems (TOR, VPN, anonymous proxy); knows its users’ usual connection patterns and therefore detects anomalies, making it possible to prevent online fraud or detect risks associated with suspicious geolocations.

BEHAVIORAL BIOMETRIC

Recognizes the behavioral biometry of your users (gestures, patterns, rhythms, device movements, etc.) to detect anomalies that make it possible to avoid attempts at identity theft. It detects whether the user sessions are being carried out by real humans, robots or attackers.

CONTENT

Detects any illegitimate modification in the content shown to the user which seeks to deceive the user and puts the session in danger. A unique approach in the market, able to prevent attacks such as malware, phishing, automatic transfers made by bots, etc.

OMNI-CHANNEL

Using a cross-analysis of information from different channels, it is possible to perceive anomalies that appear around a fraud attack, enabling proactive identification of such an attack.