Data Science Research Group

Actuarial Mathematics theme

Computers and Big Data have led to significant changes in the field of actuarial mathematics over the past few decades, and at Essex we’re spearheading research into a range of areas within it.

Members of the theme conduct multidisciplinary research that is meeting the highest international standards in the broad areas of actuarial science and finance, including:

  • actuarial and financial modelling,
  • predictability,
  • asset-liability management,
  • risk management and risk theory,
  • mathematical finance,
  • financial data science
  • applied probability in actuarial science and queueing systems.

Our work includes the development of methodological frameworks for prediction of financial and macroeconomic series such as volatility, equity premium, recession indicators, as well as the development of Bayesian methods for mortality modelling and forecasting, optimal insurance pricing, optimal risk transfer and robust insurance mechanisms, term-structure models, affine stochastic processes, Gaussian processes, extreme value theory and ruin theory.

Members of the theme constantly look to achieve greater impact for industry and society. They have recently applied their research in a KTP with MSXi, developing a predictive, self-learning model for automotive warranty expenditure with reference to a broad range of factors, ranging from product quality to dealer and customer behaviour.

The theme also continues to examine important issues that cut across all areas of insurance and finance industry including predictability, portfolio optimisation, pricing, risk management and hedging, and extreme risks.

The theme’s key focus is to become one of the top three UK and one of the top ten European Actuarial Science and Finance research centres, producing research of the highest quality for the academic community and generating valuable impact for industry and society.

It will present its work at the most influential conferences and publish in the best journals. It will further increase its international research profile by developing new academic and industrial collaborations. It will grow its research income from UK Research Councils, expand its collaborations with industry partners via KTP projects (for example with insurance companies), and develop projects for local and national government such as in the area of risk analytics of social care data.

Latest papers

2021

2020

2019

A side shot of a row of cars parked on some grass.
Knowledge Transfer Partnership: MSXi

The Department of Mathematical Sciences is collaborating with MSXi in a Knowledge Transfer Partnership project that uses data science and optimisation techniques to improve warranty quotes for customers.

Visit the project page