3 papers in the archive (2024–2025), average empirical rigor 6.3/10, average math complexity 5.5/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in Machine Learning for Market Forecasting.
Frequent co-authors: Boris Wolfson (1), Catayoun Azarm (1), Mickey van Zeelt (1), Linh Nguyen (1), Marcel Boersma (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Fraudulent activity in the financial industry costs billions annually. Detecting fraud, therefore, is an essential yet technically challenging task that requires carefully analyzing large volumes of data. While machine learning (ML) approaches seem like a viable solution, applying them successfully
Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses. Conventional rule-based systems struggle to keep pace with evolving fraud tactics, leading to high false positive rates and missed detections. Machine learning techniques offer
Current trends in Machine Learning prefer explainability even when it comes at the cost of performance. Therefore, explainable AI methods are particularly important in the field of Fraud Detection. This work investigates the applicability of Differentiable Inductive Logic Programming (DILP) as an ex