Gain more loyal customers, reject only fraudsters
Nethone has developed a proprietary fraud prevention solution based on a Know Your Users™ approach, which enables comprehensive, in‑depth, multidimensional profiling of all online users. From knowing customers to understanding their real motives — it’s an essential tool for the new era of customer‑centric services.
is an answer to this challenge
We’ve built Nethone Profiler, a solution that allows us to collect unique first-hand data about online users.
Thanks to a proprietary use of machine learning techniques, we are able to profile gathered information, segment traffic and block fraud attempts.
We deeply believe that the future of eCommerce will mean less and less online friction for users. Years of experience in online business allowed us to understand how much of the user‑side friction is caused by imprecise risk management processes. Nethone was created to enable the further dynamic growth of the eCommerce channel, thanks to radically reducing the risk management related friction — manual verification and passive authentication.
From knowing to understanding
What’s the difference between Know Your Customer (KYC) and Know Your Users™ (KYU)?
KYC tries to recognise who’s on the other side of the screen — verifying the
identity of a customer. But why not use this data for other purposes also?
What if we could:
enrich and augment the process, and make much more use of it?
move from just protecting the bottom line of an organization to proactively boosting its top line?
broaden our perspective to take a super close look at all users, not just those who have already proceeded to the checkout or filled out the sign-up form?
reach beyond “knowing” to actually “understanding?”
These questions have led the Nethone team to where we are today.
Know Your Users™ is a step forward compared to KYC. It’s all about understanding what’s non‑declarative and unravelling what’s hidden. Nethone Profiler, the technology standing at the heart of the KYU approach, allows merchants to get an exhaustive picture.
It extracts over 5,000 attributes about the device itself, its software and network characteristics, and most importantly — about users’ behaviour — to get a holistic understanding of the customer the merchant is dealing with.
The outcome of this analysis fuels the machine learning engine responsible for spotting both typical and unusual patterns and correlations, and allows to train the models to industry‑specific needs.
As a result, the solution is able to provide real‑time recommendations about customers with very high accuracy, all to limit fraud risk and increase income by identifying recurring users and unleash sales potential.