nethone fraud ocean hero background

Deep learning about your DG&S users from just a bit of given information

When you sell a digital product online subscriptions, game codes, eBooks, gift cards, digital currencies you can expect to be favorited on the fraudsters’ playlist.

Why do fraudsters prefer to target DG&S companies?

Global sales
Using only a device connected to the Internet, criminals can defraud companies all over the world.
No shipment
Orders are usually sent via email, which makes it extremely convenient to resell them.
Instant delivery
There’s literally no time between payment and delivery — it’s extremely difficult to stop fraud after checkout.

The Nethone Guard fraud prevention solution: 4 Pillar Protection (Profiler, ML, API, & human experts)

Nethone Profiler: verify your users even when given little data from the checkout form

Less friction, more security, more revenue

The basic checkout form is the common way to complete an online transaction. But forms cause friction and are often a reason for cart abandonment. Shortening the form as much as possible is required to keep conversions — we make that possible!

Gathering clues in an instant

Nethone Profiler gathers 5,000+ attributes about every customer to see if they are attempting to anonymize their actions, conceal their true location, or use automation tools to deceive your business. Profiler also observes raw behaviour associated with keystrokes, mouse and accelerometer to inform you whether your customer is real or a fraudster.
Benefits of using Machine Learning

Merging contexts and quick adaptation

Reviewing 5,000 attributes about every user in real time is impossible for a human, but it’s perfectly suited for a Machine Learning model. ML releases the full data potential by merging contexts, automates decision making by providing actionable recommendations in plain language to your risk team, and adapts to constant changes in user traffic and fraud methods.

Minimize false positives

If you are still using basic heuristics and manual rules sets, you’ve taken an important first step. Heuristics can be effective in the short term, but they generate false positives over the long term. Since fraud changes all the time, it’s necessary to use decisioning techniques which adapt quickly.
An API that becomes a true fraud prevention data hub
We integrate multiple data sources and teach our ML models how to use them, including:
  • What Nethone has learned about online fraud over the years and what Profiler is seeing in real time;
  • Fraud attempts that your company has already encountered;
  • External data sources from 3rd party partners who specialize in online fraud.
This gives Nethone a 360⁰ view on each online customer to make the best decision possible.
The fourth pillar: human support for the Machine Learning models

Personalized Machine Learning

Our clients are assigned a dedicated Data Scientist who understands your KPIs and shares actionable insights about your business.

Our Data Scientist provides personalised service, so that you are never stuck waiting for Customer Support.

The power of partnership

Our Data Scientist cooperates with your risk analysts to find new fraud patterns affecting your business. Machine Learning models will be tuned to respond to new patterns of activity.

Panel - the way we visualize all of these data

Our panel also has a state of the art case manager for your fraud team. They can use it to automate their day-to-day activities to gain more time and information to tackle the most challenging cases that fall into manual reviews.


Aggregated traffic characteristics in a specified time frame

Let’s say you want to determine the traffic on your website in the context of fraud and payments.
The panel is the start of a comprehensive view of user profiles, visualization of data, and tracking of ML decisions based on signals and connections.

Nethone dashboard example


User’s activity session within the platform

In the Profiling Details view, you can see all the user’s activity (e.g. creating account or adding product to the cart) within one session. All their actions are gathered in a consecutive order on a timeline which we refer to as profiling.

Nethone profiling example

Triggered signals

View of fraudulent attempts, events, block and pass requests

When looking at a given inquiry the first thing you will see are the signals which were triggered. Signals, in short, are notifications about abnormal situations which may be indicators of a higher probability of fraud. [...]

Nethone trigerred signals example


List of user’s actions that trigger Nethone’s recommendation

Different symbols represent different inquiries within the same activity session of the user. For example, you can check what card was used and find out that it was different for both inquiries.

Nethone inquires example


Interactive display of user’s activities and geolocalisation

The map view gives you in a quick glance all the geographic information you need about a given transaction attempt. Where did they buy from? What route did they buy? Where is the card from?

Nethone map example

Connection Graph

Analyze the web of relations in your merchant ecosystem

We are particularly proud of what we call the connection graph which shows the links between different profilings. All the nodes presented on the graph are related to each other by some feature. As you can notice these two profilings are connected by the same email address, user, IP address and cookie. [...]

Nethone connection graph example


Preference selection for a specific client

You can customise the graph by changing the depth of the displayed connections and by selecting which features you want to track.

Nethone customisation example

Value delivered: fraud detection for a crypto exchange and an online game store

bitcan logo
See how crypto exchange improved transaction acceptance while lowering fraud rates using Know Your User approach.
Blocked traffic reduction
Unauthorized transactions reduction
Total worth of prevented fraudulent transactions
Global online game store
Check how Nethone boosted the rates of online traffic approvals and conversion rates, using advanced solutions based on ML and Nethone Profiler.
Conversion rate improvement
Chargeback rate decrease

They entrusted us to guard their transactions and we delivered. See what others say about our solution.

Nethone has created a set of deep user profiling tools that together with heavy expertise in machine learning allow us to better understand our end customers.
Konrad Howard
Konrad Howard
Co-Founder, Chief Product Officer
Bitcan managed to find a partner who, with its flexible offer, adapted to the needs of the currency exchange and increased the security of its customers. In just over 2 months, Nethone managed to significantly reduce the level of blocked traffic from 60% to 25%.
Piotr Bień
Piotr Bień
Chief Operating Officer of Litpay Group
Having a professional FDP solution is a crucial part of our product, that is why we are analysing all traffic with them. Thanks to Nethone we can maintain a low fraud ratio. We also highly appreciate their flexibility and customer care.
Przemysław Kowalczyk
Przemysław Kowalczyk
With Nethone implemented in our mobile app, we see a huge difference in user quality and only pay for verified, real user-driven subscriptions. This saves our team a tremendous amount of time, so we can focus on growing our business.
Przemek Szustak
Przemek Szustak
Product Owner

Let’s grow your business!

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