3 papers in the archive (2023–2025), average empirical rigor 7.8/10, average math complexity 6/10. Quadrant mix: Holy Grail ×3.
Works mostly in Insurance & Actuarial Risk, Machine Learning for Market Forecasting, Risk Management & Tail Risk.
Frequent co-authors: Freek Holvoet (2), Roel Henckaerts (1), Ho Ming Lee (1), Benjamin Avanzi (1), Lorenzo Marchi (1), Rui Zhou (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%).
Incorporating spatial information, particularly those influenced by climate, weather, and demographic factors, is crucial for improving underwriting precision and enhancing risk management in insurance. However, spatial data are often unstructured, high-dimensional, and difficult to integrate into p
Ensuring equitable treatment (fairness) across protected attributes (such as gender or ethnicity) is a critical issue in machine learning. Most existing literature focuses on binary classification, but achieving fairness in regression tasks-such as insurance pricing or hiring score assessments-is eq
Insurers usually turn to generalized linear models for modeling claim frequency and severity data. Due to their success in other fields, machine learning techniques are gaining popularity within the actuarial toolbox. Our paper contributes to the literature on frequency-severity insurance pricing wi