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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?

2X sales loss
The fraudster buys products through legitimate customers’ accounts, resulting in chargebacks and loss of the real customers’ (repeat) business.
Omni - vector infection
Sales loss, plus fraudsters use your customer’s account information as a base of operations to crack the customers’ other shopping, banking, social media accounts.
Death by 1K cuts
Low-key, hard to detect scams over a long period of time. And/or a fraudster just lurks, learns about the customer’s profile, waiting for a big score.

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).

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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.

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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.

Nethone dashboard example

Stolen accounts are easier than ever to acquire

We study the darknet all year around and there is a large community of fraudsters that share methods and sell credentials for very low prices. They’ve even opened shops on the indexed internet.
We also study fraudster behavior by reverse engineering their tactics. New tools that are helpful for ATO are released every year.
We discovered that the Clearnet and mobile devices have emerged as the new trends in ATO attacks. Stolen credentials are extremely inexpensive and come with tutorials.
There are so many options to steal customer data: phishing, gift card fraud, data dumps and breaches, man-in-the-middle attacks...

Follow our timeline

Step 1
User logs in to your environment. Countdown begins.
Step 2
We gather information about how the user keyed in the login credentials for the present analysis. What was the speed of the keystrokes? How does the left hand compare with the right hand? What about the mouse movement? We store the info for future reference and comparison.
Step 3
Your user's session is compared with all of the user's previous sessions looking at their attributes. It’s a combination of behavioral biometrics and the “cookie hijacking model.”
Step 4
After 1 second, we will have evaluated hundreds of attributes. After 2 seconds, it’ll be thousands.
Step 5
A graph of networks is built by matching transactions using shared attributes’ values. If it’s an account takeover, a picture develops. The probability that the user is a fraudster is calculated.
Step 6
3 seconds later, human readable and comprehensible recommendations appear in Nethone Panel. No inscrutable “black box” here – this is explainable AI.

Let’s grow your business!

We’ll prepare a solution focused on the singularity of your business.
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