Lan Wang

Doctorat en mathématiques,
Université McMaster

Direction de recherche:

  • Anas Abdallah, professeur au Département de mathématiques et de statistique de l’Université McMaster, Ontario
  • Jean-Philippe Boucher, professeur au Département de mathématiques de l’Université du Québec à Montréal

Publications

  • A.Abdallah, J.-P. Boucher & L.Wang (2026), Refined Trip Consolidation Framework and Extended Difference-in-Differences Analysis, [Projet en cours]

    Telematics data provide unprecedented granularity for studying driving behavior, yet raw data often suffer from fragmented trip records that can bias subsequent analyses. This study proposes a novel trip consolidation method based on time gaps, similarity metrics, and Mahalanobis anomaly scores, with a threshold selection approach guided by anomaly distribution shifts. Using a large-scale telematics dataset—substantially larger and more diverse than those used in prior work (e.g., Bolancé et al., 2024)—we build upon a recent Difference-in-Differences (DiD) framework by (i) incorporating additional covariates derived from policyholder and vehicle characteristics (e.g., payment plan, vehicle use, marital status, etc.), (ii) introducing new outcomes (number of trips, anomaly score changes, idle ratio, etc.) to assess the multifaceted effects of accidents on driving behavior, and (iii) relaxing the original sample restriction to include drivers with post-treatment claims, enabling a direct comparison of treatment effects under alternative assumptions.

  • A.Abdallah & L.Wang (2023), Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves, Risks, 11 (11), 187.

    The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the Sarmanov family of multivariate distributions to the actuarial literature. In fact, we study rank-based methods using the Sarmanov distribution to adequately estimate the loss reserves and properly capture the dependence between lines of business. An inadequate choice of the dependence structure may negatively impact the estimation of the marginals and, hence, the reserve. Thus, we propose a two-stage inference strategy in this research to address this, while taking advantage of the flexibility of the Sarmanov distribution. We show that this strategy leads to a more robust estimation, and better captures the dependence between the risks. We also show that it generates smaller risk capital and a better diversification benefit. We extend the model to the multivariate case with more than two lines of business. To illustrate and validate our methods, we use three different sets of real data from both a major US property–casualty insurer and a large Canadian insurance company.

Implications

Présentations locales

  • Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves, McMaster Graduate Research Symposium, Hamilton, Canada (ON), 15 octobre 2024.

Enseignement

Démonstrations