Actuarial Science at University of Essex - UCAS

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Course summary

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 also 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 the majority 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. It is also possible to specialise on a topic of choice, with options including:

  • Actuarial and financial modelling
  • General insurance
  • Life insurance
You will also have the chance to study a problem in depth through a Masters thesis project on a subject chosen by you or your supervisor. 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.


Entry requirements

Entry requirements for this course can be found on the course finder on the University of Essex website - www.essex.ac.uk


Fees and funding

Tuition fees

England £7728 Year 1
Northern Ireland £7728 Year 1
Scotland £7728 Year 1
Wales £7728 Year 1
Channel Islands £7728 Year 1
EU £16560 Year 1
International £16560 Year 1
Republic of Ireland £16560 Year 1

Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .

Additional fee information

Fees displayed are for 2023-24 entry. Fees for part-time courses are calculated pro-rata to the relevant full-time fee. Tuition fees may be subject to annual increases in each year of study in line with inflation.
Actuarial Science at University of Essex - UCAS