Join January's Data & Drinks for a session discussing how Transactie Monitoring Nederland (TMNL), an alliance of five of the biggest banks in the Netherlands, is using data and AI to counter money laundering. Our two guest speakers for the day are TMNL's Lead MLOps Engineer Simon Stiebellehner and AI and Data Science Lead Maryam Miradi, who will host two separate discussion about the topic.
Summary of talk #1: Scaling the fight against Money Laundering with MLOps by Simon StiebellehnerOrganizations often struggle with the efficient scaling of production-ready data science, facing hurdles that span little standardization, inefficient manual work, a lot of handovers, among others. This is where MLOps comes into play. In this session, Simon will go over why and how MLOps are at the very core of what TMNL does, how it helps streamline and scale model development and how it is organized there.
Summary of talk #2: Graph analytics and Unsupervised Learning for Anti Money Laundering by Maryam MiradiWhen fighting money laundering or monitoring clients or their transactions, different varieties of unsupervised learning methods such as isolation forest or autoencoders can detect unknown patterns in the data. In addition to that, graph analytics can help follow money or detect communities, which adds another layer of intelligence to determining these patterns. In this talk, we unpack both anomaly detection (unsupervised learning) and graph analytics methods for fighting money laundering.
About the speakers:
Simon Stiebellehner is Lead MLOps Engineer at Transactie Monitoring Nederland (TMNL). Next to his work at TMNL, he is university lecturer on data mining and data warehousing. Simon originally started out as a data scientist, but half a decade ago began building MLOps solutions without knowing the term “MLOps” even existed.
Maryam Miradi is AI and Data Science Lead at Transactie Monitoring Nederland (TMNL). She has a PhD in artificial intelligence deep learning, with a specialization in NLP and computer vision from Delft University of Technology. The last 15 years, she has developed different AI solutions for organizations such as Ahold-Delhaize, Belastingdienst, Alliander, Stedin and ABN AMRO
The Data & Drinks organization (Xomnia) is excited about the technical possibilities to counter fraud, but also is also closely monitoring the public debate with regards to responsible use of AI around the monitoring capabilities. We believe talks like these benefit the debate.