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Actuaries provide assessments of financial security systems, with a focus on their complexity, their mathematics, and their mechanisms. Actuaries quantify the probability and manage the risk of future events in areas such as insurance, healthcare, pensions, investment, and banking and in non-financial areas. This course is taught by the Department of Mathematical Sciences and is intended for students with a first degree in mathematics, statistics, economics, or finance who would like to acquire knowledge in actuarial science.

Our MSc Actuarial Science course is based on the syllabus of most of the core subjects of the Institute and Faculty of Actuaries, so you’ll cover subjects as part of your course CB1 (Business Finance) depending on the optional module selected, CM2 (Financial Engineering and Loss Reserving) and CS2 (Risk Modelling and Survival Analysis). This focus on up-to-date research findings in actuarial methodologies and actuarial applications means that you gain a solid training in actuarial modelling and actuarial analysis.

As part of our Department of Mathematical Sciences you’re a member of an inclusive and approachable research community with an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics, and mathematical biology.

We are genuinely innovative, and student focused. Our research groups are working on a broad range of collaborative areas tackling real-world issues. Here are a few examples:

• Our data scientists carefully consider how not to lie, and how not to get lied to with data. Interpreting data correctly is especially important because much of our data science research is applied directly or indirectly to social policies, including health, care, and education.
• We do practical research with financial data (for example, assessing the risk of collapse of the UK’s banking system) as well as theoretical research in financial instruments such as insurance policies or asset portfolios.
• We also research how physical processes develop in time and space. Applications of this range from modelling epilepsy to modelling electronic cables.
• Our optimisation experts work out how to do the same job with less resource, or how to do more with the same resource.
• Our pure maths group are currently working on two new funded projects entitled ‘Machine learning for recognising tangled 3D objects’ and ‘Searching for gems in the landscape of cyclically presented groups’.
• We also do research into mathematical education and use exciting technologies such as electroencephalography or eye tracking to measure exactly what a learner is feeling. Our research aims to encourage the implementation of ‘the four Cs’ of modern education, which are critical thinking, communication, collaboration, and creativity.