3 papers in the archive (2023–2025), average empirical rigor 8/10, average math complexity 5.8/10. Quadrant mix: Holy Grail ×3.
Works mostly in Insurance & Actuarial Risk, Machine Learning for Market Forecasting.
Frequent co-authors: Benjamin Avanzi (3), Bernard Wong (3), Matthew Lambrianidis (1), Melantha Wang (1), Xingyun Tan (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%).
The use of neural networks trained on individual claims data has become increasingly popular in the actuarial reserving literature. We consider how to best input historical payment data in neural network models. Additionally, case estimates are also available in the format of a time series, and we e
High-cardinality categorical features are pervasive in actuarial data (e.g. occupation in commercial property insurance). Standard categorical encoding methods like one-hot encoding are inadequate in these settings. In this work, we present a novel Generalised Linear Mixed Model Neural Network (“G
Understanding the emergence of data breaches is crucial for cyber insurance. However, analyses of data breach frequency trends in the current literature lead to contradictory conclusions. We put forward that those discrepancies may be (at least partially) due to inconsistent data collection standard