Faculty of Actuarial Science and Insurance Seminar with Rendani Mbuvha (University of Witwatersrand)

Location: Room 2004, Bayes Business School, 106 Bunhill Row
 

Abstract

Short-term insurance ((or property and casualty) pricing traditionally relies on statistical methods like Generalised Linear Models and policyholder-specific rating factors to estimate expected claims costs associated with specific risk profiles. However, recent years have witnessed a growing influence of weather-related events in the industry, with the potential for amplification due to both climate change and meteorological phenomena like the El Niño–Southern Oscillation (ENSO) and La Niña.

In this talk, we directly link a dataset of buildings' risk exposure and associated claims to a high-resolution gridded precipitation dataset. The objective is to assess the predictive power of precipitation on both actual and forecasted bases. Our proposed modelling framework enables the estimation of both the frequency and severity of buildings' claims, considering the combined dataset. We evaluate the added precipitation feature's significance compared to traditionally used rating factors, exploring the sensitivity of claims frequency and severity to precipitation variations.

Through diverse precipitation scenarios, we demonstrate how quantifying the risks associated with excessive precipitation enables more accurate financial forecasts and facilitates the exploration of effective risk mitigation strategies.

Biography

Rendani Mbuvha is the Google DeepMind Academic Fellow in Machine Learning at Queen Mary University of London and Associate Professor in Actuarial Science at the University of Witwatersrand, Johannesburg. He received his PhD from the University of Johannesburg in 2021 and a Masters in Machine Learning from KTH Royal Institute of Technology in Sweden. He is a fellow of the Institute and Faculty of Actuaries (UK) and holds the Chartered Enterprise Risk Actuary designation. Rendani’s current research interests lie in the intersection of probabilistic Machine Learning and climate risk modelling. He is a non-executive director of Bidvest Life and a trustee of Discovery Health Medical Scheme in South Africa. He is the author of the book "Hamiltonian Monte Carlo Methods in Machine Learning", and he is a previous recipient of the Google PhD fellowship.