The role of a traditional actuary is changing. Organisations require actuarial professionals with the analytical and statistical skills needed to analyse the large sets of data that they are being presented in today’s data-driven world. You should study this programme if you are seeking to pursue or further your actuarial professional qualification, while at the same time future-proofing your skills.
If you have prior training in actuarial science, to obtain an Associate level qualification from the Institute and Faculty of Actuaries (IFoA), you will need to pass the Core Practices (CP1, CP2, CP3) examination subjects after completing the Core Principles. If you have already passed or achieved exemption from the Core Principles, you can jump straight into studying modules consistent with the Core Practices, which are at the heart of this programme.
If you have not studied actuarial science before, we also give you the option to include selected Core Principles in your MSc programme. This includes subjects covering Actuarial Mathematics and Actuarial Statistics (consistent with IFoA's CM2 and CS2 examinations).
Find out more and apply
After successful completion of the course students should have the skills necessary to obtain employment in a wide variety of roles in the field of general or life insurance, pension funds, healthcare, banking, regulatory bodies, or broadly in consultancy and other corporations or public organisations.
The compulsory component of the programme includes Actuarial Risk Management I and II (consistent with Actuarial Practice, CP1 of the IFoA syllabus), Machine Learning with Python, Time Series for Business, and a Dissertation Project (consistent with Modelling Practice, CP2 and Communication Practice, CP3 of the IFoA syllabus).
In addition, students will choose from a range of modules covering both Data Science and Actuarial Science. Depending on the students’ interest and their previous background they can choose modules in financial engineering, asset and liability modelling, advanced machine learning, financial data analytics, neural networks and deep learning, computational statistics with R.