Preventing online fraud using Behavioral Biometrics
bugFraud verifies the user’s identity in real time, throughout the entire online session.
A User-Centric solution unique to the market
Only buguroo prevents online fraud no matter when or how the fraudster attempts to hijack the online session, monitoring the user from login to logout.
bugFraud guarantees the users are who they claim to be
Many companies spend large amounts of money trying to prevent fraudsters from compromising their customers’ data, using phishing techniques, to commit Account Takeover (ATO) fraud. Thanks to the highest account takeover detection rate in the market, with fewer false positives, buguroo is able to identify the user´s behavioral biometrics and differentiate legitimate users from fraudulent ones.
bugFraudprotects the users from being manipulated
Fraudsters usually attempt to bypass authentication challenges using several techniques, such as Remote Access Trojan (RAT), malware, webinjects, form-grabbers and others. bugFraud offers a unique approach, combining techniques of “white” list, “black” list and “grey” list techniques in its anomaly detection and classification engine, preventing fraud before it happens.
HOW does IT WORK?
Regulatory compliance PSD2 for banks or fintechs
bugFraud effectively helps PSD2 compliance by incorporating protection measures on your Web or Mobile channel:
It provides new authentication elements for the implementation of Strong Client Authentication (SCA)
Provides technologies for Malware Detection in the endpoint environment
It provides an effective real-time transaction risk assessment mechanism (Transaction Risk Analysis).
Behavioral Biometrics
bugFraud gathers Behavioral Biometrics patterns and environment information to prevent fraud from the first login by detecting anomalies in screen pressure, typing speed, mouse or mobile movements, gyroscope position and more. Knowing your customer (KYC) prevents Account Takeover (ATO).
Our anonymized user data technology ensures compliance with cybersecurity industry and data protection standards (PSD2, GDPR,etc.).
New Account Fraud prevention
bugFraud predicts in one shot that the cybercriminal is trying to commit fraud during the onboarding phase (NAF) or from first-time login onwards, by recognizing criminal behavior and differentiating these behaviors from those legitimate users.
Deep Learning fraud prevention
bugFraud uses Deep Learning algorithms powered by an Artificial Intelligence Neural network to identify fraudsters or bots trying to impersonate legitimate users. It detects suspicious users and prevents attacks from happening.
Machine Learning to detect unknown zero-day malware
bugFraud can detect unknown malware campaigns aiming at manipulating the content that the user is watching on their web browser or mobile Apps. (web injections, MiTB, RAT, RitB), regardless of whether or not the interaction matches a known malware signature. Our approach goes beyond known signatures or blacklists, analyzing whether web page navigation has been subverted by fraudsters. bugFraud classifies grey anomalies to prevent false positives and false negatives.
Real-Time fraud detection
bugFraud is a flexible, next-generation solution that is able to adapt to the evolving landscape of fraud and its constantly morphing threats. Our hybrid engine combines real-time anomaly detection with Machine Learning models to ensure your company is protected against emerging campaign throughout the entire session, from onboarding until the user logs out, and in real time.
Frictionless: protect without impacting the user experience
bugFraud doesnot require users to install any software in their devices. This completely transparent system secures banking transactions behind the scenes, without delivering any alerts that might alarm the user. Moreover, customers are protected regardless of the device (PCs, laptops, tablets, or smartphones), OS or browser improving their user experience.
Multichannel protection
bugFraudanalyze dozens of sensor parameters collected from the user’s behavior, device and environment (finger size, pressure, speed, gestures,typing fluency, gyroscope position, geolocalization, etc.) in order to identify any attack attempt nevertheless the channel, device or OS.
![bugfraud׃ deep learning for online fraud prevention [Full HD 1080p]](https://play.vidyard.com/gd77VcrNZ63n9qZAw7rFo3.jpg)
