Master Thesis Student (m/f/d) – Explainable AI for Fraud Detection

Everyone's story matters. Come shape your story with us at Riverty.
But where does that take you?

To one of our 30 hybrid workspaces – designed for exchanging ideas, learning from others, and shaping the way we work. An international community of over 4,000 people, representing almost 80 nationalities across 12 countries. United by one mission: Combining empathy, advanced technology and data-driven insights to keep people and businesses in flow. With payments made for them. So that they don't have to worry about it.

And there's more: We are part of the family-owned Bertelsmann group. Established. Corporate. In a fast-paced industry. We enable flexible payments in various industries, simplifying the financial management of known brands and helping people repay debt to build financial confidence. In short: shaping FinTech.

Location: Berlin, Germany (Hybrid options available)
Team: Data Science and Machine Learning (the team is English speaking) 
Employment Type: Full-time (dedicated to the thesis project)
Starting Date: Flexible (Preferred start between September 2026 and March 2027)
Duration: up to 6 months (standard German university thesis timeline)
Compensation: €960.00 / month

Master Thesis: Explainable AI for Machine Learning Models in Risk and Fraud Detection

The thesis will focus on Explainable Artificial Intelligence (XAI) for machine learning models in risk and fraud detection for online payments. A key focus will be on applying and evaluating Shapley value-based explanations (e.g., SHAP) to improve model transparency and validation, with an emphasis on making these methods robust and suitable for production use. The exact research question will be defined together with the student based on their interests and the team's priorities.

Your Profile

• Currently enrolled in a Master's program in Computer Science, Data Science, Mathematics, Statistics, Artificial Intelligence, Engineering, or a related STEM field.
• Strong programming skills in Python.
• Basic knowledge of machine learning and statistical modeling.
• Familiarity with Explainable AI concepts or a strong interest in model interpretability.
• Experience with Python machine learning libraries (e.g., scikit-learn, XGBoost, LightGBM) and data analysis.
• Familiarity with SQL; experience with Spark, Databricks, Docker, or cloud environments is a plus.
• Ability to work independently while collaborating effectively in an international team.
• Fluent in English.

NOTE: Please submit your CV and a copy of your current university enrollment certificate (Immatrikulationsbescheinigung)

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