Fraud prevention and risk management for digital lending
When your product is money and you are selling online, you know that you will attract fraudsters. This is why the digital lending industry has become very adept at measuring risk. But risk measurement is expensive: credit bureaus, bank account scraping and other data providers cost money and kill conversion rates.
Improve your score-card
A unique data feed at your team's disposal
Thanks to Nethone Profiler we can gather 5,500+ attributes about every user who lands on your page. Across a wide set of geographies we have observed that this unique “here-and-know” data is capable of improving the credit scoring and fraud prevention algorithms employed by our clients. In simpler terms, the chances of default of a customer running a virtual machine or using the latest Apple hardware are not the same. Give the power of more data to your data science teams!
Machine learning for credit-scoring
For clients capable of providing credit bureau data to Nethone through our API, we are able to mix that data to that obtained with Profiler. Our team of data scientists will then use all the different loan defaults we have seen on the market to provide a recommendation to either accept or deny a transaction.
Get rid of unwanted users and future defaulting customers
You wouldn't allow someone wearing a balaclava to enter your branch, would you? Why then would you allow this to happen on your site? Nethone will allow you to block people using: VPNs, proxies, virtual machines, emulating devices, spoofing user agents, associated with TOR network and over 50 other signals tied to fraud and criminal behaviour. All of this to ensure that your platform stays fraud-free while you operate.
Don't spend money on applications that you will eventually reject
In order to assess your customer's credit score, you are probably calling on various credit agencies. These calls are expensive, and in roughly 60% of the cases, you are rejecting the application for the loan! Nethone is able to sit as a first line of defense which uses only our Profiler data to identify the users which you will reject after you make the other calls. Typically we are able to flag roughly 20% of your traffic this way, with 0% false positives!