Technological Thoughts by Jerome Kehrli

I spoke to to Rudolf Falat, founder of the Voice of FinTech podcast about leveraging AI in anti-fraud prevention and cybersecurity.

The Podcast is available here and can be listened to directly from hereunder:

Happy listening !

The full transcript is available hereunder

(Article initially published on NetGuardians' blog)

NetGuardians overcomes the problems of analyzing billions of pieces of data in real time with a unique combination of technologies to offer unbeatable fraud detection and efficient transaction monitoring without undermining the customer experience or the operational efficiency and security in an enterprise-ready solution.

When it comes to data analytics, the more data the better, right? Not so fast. That’s only true if you can crunch that data in a timely and cost-effective way.

This is the problem facing banks looking to Big Data technology to help them spot and stop fraudulent and/or non-compliant transactions. With a window of no more than a hundredth of a millisecond to assess a transaction and assign a risk score, banks need accurate and robust real-time analytics delivered at an affordable price. Furthermore, they need a scalable system that can score not one but many thousands of transactions within a few seconds and grow with the bank as the industry moves to real-time processing.

AML transaction monitoring might be simple on paper but making it effective and ensuring it doesn’t become a drag on operations has been a big ask. Using artificial intelligence to post-process and analyze alerts as they are thrown up is a game-changing paradigm, delivering a significant reduction in the operational cost of analyzing those alerts. But accurate fraud risk scoring is a much harder game. Some fraud mitigation solutions based on rules engines focus on what the fraudsters do, which entails an endless game of cat and mouse, staying up to date with their latest scams. By definition, this leaves the bank at least one step behind.

At NetGuardians, rather than try to keep up with the fraudsters, we focus on what we know and what changes very little – customers’ behavior and that of bank staff. By learning “normal” behavior, such as typical time of transaction, size, beneficiary, location, device, trades, etc., for each customer and internal user, and comparing each new transaction or activity against those of the past, we can give every transaction a risk score.

(Article initially published on NetGuardians' blog)

Whenever our software is run head-to-head in a pitch situation against that of our rivals, we always come out top. We always find more fraud with a lower number of alerts. For some, this is a surprise – after all, we are one of the youngest companies in our field and one of the smallest. To us, it is no surprise. It is testament to our superior analytics.

A focus on customer behavior

We began working in fraud prevention in 2013 and quickly realized the futility of rules engines in this endless game of cat-and-mouse with the fraudsters. The criminals will always tweak and reinvent their scams; those trying to stop the fraud with rules engines will always be left desperately working as fast as possible to identify and incorporate the latest scams into their surveillance. Far better to focus on what we know changes very little – customer behavior.

If a bank knows how a customer spends money, it can spot when something is awry by looking for anomalies in transaction data. However meticulous the fraudster is at trying to hide, every fraudulent transaction will have anomalous characteristics. People’s lives are constantly changing – they buy from new suppliers, they move house, go on holiday and their children grow up – all of which will affect their spending and transaction data. Every change will throw up false alerts that will undermine the customer experience unless you train your models correctly.

The three pillars of 3D AI

We train our models using what we call our 3D AI approach. This enables them to assess the risk associated with any transaction with extraordinary accuracy, even if it involves new behavior by the customer. This also keeps false alerts to the minimum.

Developed by us at NetGuardians, this approach has three pillars, each of which uses artificial intelligence (AI) to constantly update and hone the models.

The pillars are: anomaly detection, fraud-recognition training analytics and adaptive feedback. Together, they give our software a very real advantage by not only spotting fraud and helping banks stop fraudulent payments before any money has left the account, but also by minimizing friction and giving the best possible customer experience. This is what differentiates our software in head-to-head pitches.

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?

In this article, I intend to present my company's - NetGuardians - approach when it comes to deploying Artificial Intelligence techniques towards better fraud detection and prevention.
This article is inspired from various presentations I gave on the topic in various occasions that synthesize our experience in regards to how these technologies were initially triggering a lot of skepticism and condescension and how it turns our that they are now really mandatory to efficiently prevent fraud in financial institutions, due to the rise of fraud costs, the maturity of cybercriminals and the complexity of attacks.

Here financial fraud is considered at the broad scale, both internal fraud, when employees divert funds from their employer and external fraud in all its forms, from sophisticated network penetration schemes to credit card theft.
I don't have the pretension to present an absolute or global overview. Instead, I would want to present things from the perspective of NetGuardians, from our own experience in regards to the problems encountered by our customers and the how Artificial Intelligence helped us solve these problems.

I am presenting in this video NetGuardians' Big Data approach, technologies and its advantages for the banking institutions willing to deploy big data technologies for Fraud Prevention.

The speech is reported in textual form hereafter.