Technological Thoughts by Jerome Kehrli

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.

A few weeks ago, I did a speech about the Digitalization and its impact on financial institutions, both in terms of challenges and opportunities in the context of my role as Head of R&D in my current company.
I am reporting here my speech as an article.

Even though the Digitalization and its impacts is something so widely discussed and studied nowadays, even in banking institutions, I still find it puzzling that so many of them struggle following the pace.
Having said that, many others on the other hand have well understood how much technology is about to disrupt the banking business just as Uber has disrupted the transportation business and AirBnB the lodging business and many good and enlightening initiatives start to flourish in the news.

But still, it seems to me that most innovations in banking are really coming from small players or even startups - think of fintechs - instead of coming from the big players of the banking industry. For instance, paying everything with a cellphone is a thing for a few years now in many African countries while it's not at all in Europe, even in Switzerland, THE country of banking.
Especially in Switzerland, financial institutions struggle keeping up with evolution of their business coming from the digitalization on one side and the regulatory pressure as well as the reduction of the margins on the other side.
Discussing this very matter further exceeds the scope of this article of course but I want to report below my speech notes and present what I see as the most important challenges and opportunities for the banking industry coming from the digitalization.

Big Data technologies are increasingly used in retail banking institutions for customer profiling or other marketing activities. In private banking institutions, however, applications are less obvious and there are only very few initiatives.
Yet, as a matter of fact, there are opportunities in such institutions and they can be quite surprising.

Big Data technologies, initiated by the Web Giants such as Google or Amazon, enable to analyze very massive amount of data (ranging from Terabytes to Petabytes). Apache Hadoop is the de-facto standard nowadays when it comes to considering Open Source Big Data technologies but it is increasingly challenged by alternatives such as Apache Spark or others providing less constraining programming paradigms than Map-Reduce.

These Big Data Processing Platform benefits from the NoSQL genes : the CAP Theorem when it comes to storing data, the usage of commodity hardware, the capacity to scale-out (almost) linearly (instead of scaling up your Oracle DB) and a much lower TCO (Total Cost of Ownership) than standard architectures.

Most essential applications for such technologies in retail banking institutions consist in gathering knowledge and insights on the customer base, customer's profiles and their tendencies by using cutting-edge Machine Learning techniques on such data.

In contrary to retail banking institutions that are exploiting such technologies for many years, private banking institution, with their very low amount of transactions and their limited customer base are considering these technologies with a lot of skepticism and condescension.

However, in contrary to preconceived ideas, use case exist and present surprising opportunities, mostly around three topics :

• Enhance proximity with customers
• Improve investment advisory services
• Reduce computation costs