You already know how to respond to ATO fraud damage. But what if you could stop it from even happening?
ATO Module can stop an account takeover in its tracks at the login attempt and provide human-readable explanations of the ML models’ reasoning to your team.
What can ATO fraud damage look like?
The solution is frictionless customer account defense with ATO Module
Fraudsters love to use automation tools to attack customer accounts, which are often “secured” with weak passwords. Maybe it’s time to invest in powerful, invisible ML defense of accounts so that your customers can focus on buying.
Machine Learning designed for the clients’ needs
Deep learning to extract only the most relevant data
Machine learning models are carefully paired with the client’s needs and trained on the client’s historical data (both web and mobile).
We practice contextual Machine Learning, so each model is trained for one functional environment. For example, one is trained for device data, and another for behavioral biometrics (some anti‑fraud companies take a one‑size‑fits‑all approach with ML models).
Real-time detection and analysis
Instant recognition of user supported by graph analysis
In seconds, we’ll gather info about how a user behaves in the client's environment, which we can use for future reference. We’ll compare a user's session with all of the user's previous sessions looking at 5,000+ attributes (by the way, your customers and even your attackers won’t notice a thing.)
A graph of networks is built by matching transactions using shared attributes’ values.
Comprehensible recommendations
No “black box” solution, only explainable AI
Together with the recommendation, we deliver a human readable explanation of the ML results in one place — Nethone Panel.
Stolen accounts are easier than ever to acquire
