RiskShield RealTalk On-Demand: Machine Learning in Action with Jan Veldsink

by David Weaver

We now have 2 episodes of RiskShield RealTalk in RealTime in the books! Most recently, I had the opportunity to interview Jan Veldsink, Lead Artificial Intelligence at Rabobank. We discussed some of the hurdles financial institutions face when implementing machine learning into their fraud fighting processes as well as many of the benefits it brings.

I met Jan for the first time in 2019 and his passion for machine learning was immediately evident. We met at a Machine Learning Summer School at Nyenrode Business University in the Netherlands. The event was co-hosted by a company called BigML. Jan put on 2 insightful sessions titled: “Machine Learning for managers” and “Adopting Machine Learning at scale”. Next to his role at Rabobank, Jan is also a Professor at Nyenrode University, teaching in the areas of cybercrime and data security.

One topic we discussed was the discrepancy between institutions talking about and testing machine learning in the financial crime fighting space compared to the number of institutions actually putting models into production. In the clip below, I asked him to identify some key factors that will help financial institutions jump from a “testing lab” to production when it comes to integrating machine learning into fraud-fighting and compliance operations. One of the main conclusions: oftentimes, it is not a technological challenge, rather an organizational one.

https://vimeo.com/582509045/b2ac5a773e

I discussed several other topics with Jan that could prove to be helpful for any financial institution looking to take a deeper dive into machine learning. Other topics we covered include:

Jan introduces himself and his background in machine learning.
Are we still talking more about machine learning than we are applying it?
How to move from machine learning lab to fighting fraud.
Concreate examples of how banks can use machine learning to detect fraud
How can machine learning help in the AML Compliance space?
Impact of machine learning on false positives.
Are there any drawbacks to using machine learning to detect financial crime?
Biggest hurdles for implementing machine learning?
Advice for banks that are just beginning their machine learning journeys.

You can request access to the on-demand version of Episode 2, Machine Learning in Action”, here.

Request Access to the Recording



You may also like

Using Artificial Intelligence in the Fight Against Money Laundering and Terrorist Financing

Read

PSD2 Made Strong Customer Authentication Mandatory – Risk Based Authentication Makes it Convenient

Read

Billing Codes: The Golden Gateway to Fraud

Read

About the author

  • I started working for INFORM in 2011 and have since developed a passion for fighting fraud and financial crime. Other topics of interest include supply chain and logistics management. In the end, it is all about helping companies make intelligent operational decisions.

    All posts by this author

    More about the author at:

Our authors

Find all our authors at a glance!

All authors

Back to top