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)

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.

Yesterday we were amazed by the first smartphones. Today they have almost become an extension of ourselves.
People are now used to be connected all the time, with highly efficient devices on highly responsive services, everywhere and for every possible need.

This is a new industrial revolution - the digitization . and it forces corporations to transform their business models to meet customers on these new channels.

Banks worldwide are on the first line in this regards and for many years now they have well understood the urgency in proclaiming digitization as a key objective.
From a user perspective, the digitization confers enormous benefits in the form of ease, speed and multiple means of access and a paradigm shift in engagement. Since banking as a whole benefits from going digital, it is only a matter of time before operations turn completely digital.

The journey to digital transformation requires both strategy investments as well as tactical adjustments in orienting operations for the digital road ahead.
Fortunately, if technology can be perceived as a challenge, it is also a formidable opportunity.
And in this regards, Artificial Intelligence is a category on its own.

In my current company, we implement a state-of-the art banking Fraud Detection system using an Artificial Intelligence running on a Big Data Analytics platform. When working on preventing banking fraud, looking at SWIFT messages is extremely interesting. 98% of all cross-border (international) funds transfers are indeed transferred using the SWIFT Network.
The SWIFT network enables financial institutions worldwide to send and receive information about financial transactions in a secure, standardized and reliable environment. Many different kind of information can be transferred between banking institution using the SWIFT network.

In this article, I intend to dissect the key SWIFT Messages Types involved in funds transfers, present examples of such messages along with use cases and detail the most essential attributes of these payments.

These key messages are as follows:

• MT 101 - Request for Transfer
• MT 103 - Single Customer Credit Transfer
• MT 202 - General Financial Institution Transfer
• MT 202 COV - General Financial Institution Transfer for Cover payments

This article presents each and every of these messages, discuss their typical use cases and details key SWIFT fields involved.

The below is an extract from an interview I ran in February 2019 during the EPFL Forward event.

NetGuardians is a Swiss Software Publisher based in Yverdon-les-bains that edits a Big Data Analytics Solution deployed Financial Institution for one key use case: fighting financial crime and preventing banking Fraud.
Banking fraud is meant in the broad sense here: both internally and externally.
Internal fraud is when employees misappropriate funds under management and external fraud is when cyber-criminals compromise ebanking applications, mobile devices used for payment or credit cards.

In the digital age, the means of fraudsters and cyber-criminals have drastically increased.

Cyber-criminals have become industrialized, professionalized and organized. The same technology they use against banks is also what gives us the means to protect banks

At NetGuardians we deploy an Artificial Intelligence that monitors on a large scale, in depth and in real time all activities of users, employees of the bank, but also those of its customers, to detect anomalies.
We prevent bank fraud and fight financial crime by detecting and blocking all suspicious activity in real time.

Jérôme Kehrli, how did you manage to convince a sector that is, in essence, very traditional, to trust you with your digital tools to fight against fraud?
Two different worlds, two languages, two visions?

The situation of the banks is a bit peculiar, the digitization and with it the evolution of the means and the behaviours of the customers in the digital age, was at the same time both a traumatic and a formidable solution.

The digital revolution was a traumatic because the banks, which by their very nature are very conservative, especially in Switzerland with our very strong private banking culture, were not prepared for the need to profoundly transform the customer experience of the banking world: to meet the customer where he is, on his channels, with mobile banking, this culture of all and everything immediately, with instant payments, the opening of the information system, with the explosion of the External Asset Managers model and external service providers with the PSD2 European standard, etc.

The digital revolution has imposed these changes, sometimes brutally, in banks and it is the source of a tremendous increase of the attack surface of banks.

But this same technology that spawned the digital revolution has proved to be the solution too.
Technology has made it possible to build digital banking applications that provide all of the bank's services on a mobile device.
Technology has made it possible to implement innovative solutions that secure the information system and protect client funds.

And in this perspective, Artificial Intelligence is really a sort of panacea: robot advisory, chatbots, personalization of financial advice and especially, especially the fight against financial crime: banking fraud and money laundering

In the end, if five years ago our solutions seemed somewhat avant-garde, not to say futuristic and sometimes aroused a bit of skepticism, today the banks are aware of the digital urgency and it is the bankers themselves who eagerly seek our solutions.

You support the digital shift of the banking sector.
Do banks sometimes have to change their way of operating, their habits, to be able to use your technologies?
(Do you have to prepare them to work with you?)

So of course the digital revolution profoundly transforms not only the business model but also the corporate culture, its tools, and so on.

At NetGuardians we have a very concrete example.

Before the use of Artificial Intelligence, banks protected themselves with rules engines. Hundreds of rules were deployed on the information system to enforce security policies or detect the most obvious violations.
The advantage with rules was that a violation was very easy to understand. A violation of a compliance rule reported in a clear and accurate audit report was easy to understand and so was the response.
The disadvantage, however, was that the rules were a poor protection against financial crime and that's why fraud has exploded over the decade.

Today with artificial intelligence, the level of protection is excellent and without comparison with the era of the rules.
But the disadvantage of artificial intelligence is that accurately understanding a decision of the machine is much more difficult.

At NetGuardians, we develop with our algorithms a Forensic analysis application that allows bankers to understand the operation of the machine by presenting the context of the decision.
This forensic analysis application, which presents the results of our algorithms, is essential and almost as important as our algorithms themselves.

This is a powerful application but requires a grip.

Tom Cruise in Minority Report who handles a data discovery application playing an orchestra conductor, it's easy in Hollywood, but it's not in reality.
In reality, we provide initial training to our users and then regular updates.

In the end, a data analysis and forensic application is not Microsoft Word. Our success is to make such an application accessible to everyone, but not without a little help.
In conclusion i would say that the culture transformation end the evolution of the tools do require some training and special care.

In general, what should a company prepare for, before making a digital shift?

In the digital age, many companies must transform their business model or disappear. Some services become obsolete, some new necessities appear.
We can mention Uber of course but also NetFlix, Booking, eBookers, etc.

For the majority of the industrial base, the digitalization of products and services is an absolute necessity, a question of survival.

Successful process and business model transformation often requires a transformation of the very culture of the company, down toits identity:
Among other things one could mention the following requirements:

• scaling agility from product development to the whole company level
• involving digital natives to identify and design digital services
• realizing the urgency or if necessary create a sense of urgency
• understanding the scale of the challenge and the necessary transformation. Some say "if it does not hurt, it is not digital transformation"

In summary I would say that a company is "mature" for digitalization if it is inspired by the digitalization of our daily life to adapt its products and services AND if it has the ability to execute its ideas.
Ideas without the ability to execute leads to mess, the ability to execute without the ideas leads to the status quo.

From there I would say that a company must prepare itself on these two dimensions, bring itself the conditions and resources required to identify and to design its digital products and those required to realize them.

The digitalization with its changes of means and behaviours and the induced society and industrial evolution is putting increasingly more pressure on banks.
Just as if regulatory pressure and financial crisis weren't enough, banking institutions have realized that they need to transform the way they run their business to attract new customers and retain their existing ones.
I detailed already this very topic in a former article on this blog: The Digitalization - Challenge and opportunities for financial institutions.

In this regards, Artificial Intelligence provides tremendous opportunities and very interesting initiatives start to emerge in the big banking institutions.

In this article I intend to present these three ways along with a few examples and detail what we do at NetGuardians in this regards.

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.

The Bangladesh bank heist - or SWIFT attack - is one of the biggest bank robberies ever, and the most impressive cyber-crime in history.

This is the story of a group of less than 20 cyber-criminals, composed by high profile hackers, engineers, financial experts and banking experts who gathered together to hack the worldwide financial system, by attacking an account of the central bank of Bangladesh, a lower middle income nation and one of the world's most densely populated countries, and steal around 81 million US dollars, successfully, after attempting to steal almost a billion US dollars.

In early February 2016, authorities of Bangladesh Bank were informed that about 81 million USD was illegally taken out of its account with the Federal Reserve Bank of New York using an inter-bank messaging system known as SWIFT. The money was moved via SWIFT transfer requests, ending up in bank accounts in the Philippines and laundered in the Philippines' casinos during the chinese New-Year holidays.

Fortunately, the major part of the billion US dollars they intended to steal could be saved, but 81 million US dollars were successfully stolen and are gone for good.

The thieves have stolen this money without any gun, without breaking physically in the bank, without any form of physical violence. (There are victims though, there are always victims in such case, but they haven't suffered any form of physical violence)
These 81 million US dollars disappeared and haven't been recovered yet. The thieves are unknown, untroubled and safe.

The Bangladesh bank heist consisted in hacking the Bangladesh central bank information system to issue fraudulent SWIFT orders to withdraw money from the banking institution. SWIFT is a trusted and closed network that bank use to communicate between themselves around the world. SWIFT is owned by the major banking institutions.

In terms of technological and technical mastery, business understanding, financial systems knowledge and timing, this heist was a perfect crime. The execution was brilliant, way beyond any Hollywood scenario. And the bank was actually pretty lucky that that the hackers didn't successfully loot the billion US dollars as they planned, but instead only 81 million.
As such, from a purely engineering perspective, studying this case is very exiting. First, I cannot help but admire the skills of the team of thieves team as well as the shape of the attack, and second, it's my job in my current company to design controls and systems preventing such attack from happening against our customers in the future.

In this article, I intend to present, explain and decipher as many of the aspects of the Bangladesh bank heist and I know.