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bugFraud, a holistic user-centric view to evolve the way to prevent online banking fraud.

The ever-evolving landscape of cyber crime needs to be outsmarted using next-generation solutions with innovative approaches to prevent emerging fraud techniques, such as machine learning in cyber security. Combine the best of our proprietary malware detection engine, the use of deep learning behavioral models and advanced cognitive analytics to detect the fraud in real-time.




There are two main points where the user can be targeted by fraudsters: during authentication (login) and during authorization (operation)

In the first scenario, the main goal of the attacker is to compromise user data, steal credentials or achieve an account take over (ATO). In the second scenario, the fraudster tries to bypass authorization mechanisms.

Several techniques can be deployed to achieve these goals: phishing, RAT, malware, webinjects, form-grabbers and others. Moreover, most of these attacks can be executed anytime during the user’s session.

bugFraud collects data throughout the whole session to prevent online fraud at every stage.



Deep Learning Fraud Prevention

bugFraud proprietary engine uses deep learning algorithms to predict fraud. Detect suspicious users and prevent attacks from happening.


Biometric Fraud Detection

bugFraud gathers human biometric patterns to identify fraudsters or bots trying to impersonate legitimate users: by detecting anomalies in mouse acceleration, by identifying suspicious keyboard velocity, by alerting anomalous user elliptic curves and more. Knowing your customer avoids Account Take Over (ATO).

User Cognitive Analytics

bugFraud uses context based data and user behavioral analytics to prevent fraud. Our solution evaluates device risk, geolocation data, user’s patterns and fraud intelligence data to detect any risk associated to the user.


Machine Learning in Cyber Security to detect unknown threats

bugFraud uses a proprietary pattern similarity-based mechanism that can detect new malware campaigns that interact with the victim’s browser like MITB threats, regardless of whether or not the interaction matches a known malware signature. Our approach goes beyond the known signatures or blacklist, analyzing whether web page navigation has been subverted by fraudsters. bugFraud classifies grey anomalies to avoid false positives and false negatives.

Real Time Fraud Detection

bugFraud is a flexible next generation solution 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 always protected against emerging campaigns.


Frictionless: protect without disrupting the user

bugFraud gives advanced fraud detection capabilities with an agentless approach avoiding unnecessary friction to your customers. 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 they use for banking—PCs, laptops, tablets, or smartphones.