Thesis Intern: Behavioral Science & Visual Analytics in Financial Crime

The Mission: Can we spot money laundering as instinctively as a baby chick spots a predator? Join us to find out.
In the Financial Crime Monitoring team at Mollie we are drowning in text but searching for patterns. Currently, our analysts spend hours reading through transaction logs, internal databases and online sources to identify suspicious behavior. We want to flip the script.
We are looking for a motivated Master’s student to research the psychology of visual pattern recognition, (such as z-glyphs) and other non textual representations. Our aim is to see if we can detect financial crime faster and more accurately than through traditional charts and text only based assessments.
Key Research Questions
- Cognitive Load: Does transaction and merchant/consumer data represented as a z-glyph (or similar) reduce the mental energy required to identify suspicious or anomalous patterns?
- Speed vs. Accuracy:



Can an analyst spot a Money Laundering or Terrorism Financing typology in a visual icon faster than they can read it in various sources of text without losing precision?
- Human in the loop: How do different visual encodings (color, stroke weight, shape) affect the psychological confidence of a Financial Crime investigator?
- Explainability: Design a controlled mechanism that proves all data (both through text and image) is explainable and useful in context wrt discussions with supervisory bodies such as DNB (De Nederlandsche Bank)
- UX/UI: How can we best display the various data types into images? What would these images look like and what are the differences between images in terms of accuracy?
- Skill requirement: To what extent is the displaying and assessing of the image dependent on the background knowledge of the person with regards to ML/TF?

What You Will Do
- Literature Review: Explore the intersection of Gestalt principles, preattentive attributes, and visual analytics in high stakes

📌 Internship - Merchant Monitoring (Italia)
🏢 Mollie
📍 Italia