Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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Fraud detection at a financial institution using unsupervised learning and text mining

David Dogon (Van Lanschot Kempen)
11:1511:55 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Average rating: ****.
(4.75, 8 ratings)

Who is this presentation for?

  • Data scientists and those working in risk management and compliance



Prerequisite knowledge

  • Familiarity with basic machine learning concepts (including supervised and unsupervised learning)

What you'll learn

  • Understand considerations when building fraud detection systems and how to prevent common mistakes
  • Learn how to tune machine learning models to adjust them effectively for fraud detection


A recent money laundering scandal at ING resulted in a $900M fine for the bank. David Dogon dives into a best practice use case to demonstrate that for financials, robust and cutting-edge systems to monitor (sometimes millions of) clients are an absolute must. Traditional monitoring systems fall short of adapting to changing behavior and cannot handle complex nonlinear relations in client data, but not having the right systems in place can be detrimental to the business. Join in to learn how machine learning models can provide a solution in cases where traditional systems fall short.

David explains how Van Lanschot Kempen created a system to detect suspicious behavior using unsupervised machine learning techniques on many different sources of internal and external information (including both structured and unstructured data). The result is a more robust and adaptive client monitoring system that also allows fraud analysts to work more efficiently. Along the way, David covers the challenges faced and how Van Lanschot Kempen effectively crossed-checked the machine learning models with external data sources.

Photo of David Dogon

David Dogon

Van Lanschot Kempen

David Dogon is a member of the data science team at Van Lanschot Kempen, where he primarily focuses on investments and asset management. David is driven by an interest in the insights and predictive power from data. A bit of an adventurer, he has performed research toward a PhD degree in mechanical engineering at TU Eindhoven in the Netherlands, holds a master’s degree in mechanical engineering from Columbia University in New York, and holds a bachelor’s degree in chemical engineering, which he completed in Cape Town, the same city where he was born.