Technological Thoughts by Jerome Kehrli

The world of fraud prevention in banking institutions has always been largely based on rules.
Bankers and their engineers were integrating rules engines on the banking information system to prevent or detect most common fraud patterns.
And for quite a long time, this was sufficient.

But today we are experiencing a change of society, a new industrial revolution.
Today, following the first iPhone and the later mobile internet explosion, people are interconnected all the time, everywhere and for all kind of use.
This is the digital era and the digitization of means and behaviours forces corporations to transform their business model.

As a consequence, banking institutions are going massively online and digital first. Both the bank users and customers have evolved their behaviours with the new means offered by the digital era.
And the problem is:
How do you want to protect your customer's assets with rules at a time when, for instance, people connect to their swiss ebanking platform from New York to pay for a holiday house rental in Morocco? How would you want to define rules to detect frauds when there are almost as many different behaviours as there are customers?

At NetGuardians, we prevent fraud using a completely different approach. We use Artificial Intelligence to monitor financial transactions and user behaviour in real time and detect suspicious transactions or activities.
With our Big Data Analytics Platform - NG|Screener - the machine analyzes the past transactions of the customers to understand their transactional behaviour, as well as past activities of users on the banking information system.
The machine is able to analyze a very important depth of history in real time, capturing what customers and users usually do in so called Dynamic Profiles.
Then, whenever a transaction is input on the system, the Artificial Intelligence is able to compare that specific transaction against the customer profile and compute a risk score for it. If the risk score is sufficiently high, the machine will decide to block the transaction and qualify it for further investigation by the bank.

Using advanced machine learning techniques, we are able to have a very broad spectrum of detection, while minimizing wrong alerts, these famous false positives, to an unprecedented low ratio. Instead of focusing on identifying fraudsters, at NetGuardians we focus on understanding user and customer behaviours and habits. In order to find frauds, we don't look only for Fraud. Our approach is to detect and block every transaction that is simply too unusual and risky for the banking institution to afford letting it go out without a further investigation. And it turns out the frauds are simply always part of this set of risky transactions.
Our unique combination of unsupervised and supervised approaches makes it possible to minimize false positives, while still being able to detect fraud patterns never encountered before. In the world of Fraud Prevention Solutions, this is the Holy Grail, being able to use models that can detect what has never been encountered before while at the same time keeping the amount of alerts very low. This is hardly seen in other AI solutions on the market today.

Over the time, we have been able to develop our Artificial Intelligence further to make it smarter and smarter. For instance, pretty soon we started to compare transactions against different background sets, the past customer or user activities of course, but also the banking institution past activities as a whole or even the customer or user peer group.
The peer groups are also built using Machine Learning to analyze customers and users past activities, this time to group them together, and achieve better and more accurate scoring of risky transactions or other activities.

Today, our unique Artificial Intelligence platform is able to use a dozen of different Analytics and Machine Learning approaches going much beyond solely transaction scoring. For instance, we are able to qualify the legitimacy of a specific interaction on the ebanking platform, or to monitor PSD2 providers activity, or even to do frequency and timing anomalies detection on card transactions.

At NetGuardians, we use AI to detect anomalies and prevent fraud by providing the experts and investigators within the bank with the tools aimed at making them more efficient than has ever been possible before.
We enhance the human investigation and analytics process, but we don't supplant it.
This is called Augmented Intelligence, the core of what we do at NetGuardians.