Actuarial Science

• Introduction to R Programming

  This module is designed to provide a fundamental understanding of R programming and no previous programming experience is expected. The teaching model is learning by doing and basic concepts are built up in an incremental manner. The online material is formulated via multiple R code examples that enable the students to work independently when dealing with small R programming tasks.

• Introduction to Python Programming

  This module is designed to provide a fundamental understanding of Python programming and no previous programming experience is expected. The teaching model is learning by doing and basic concepts are built up in an incremental manner. The online material is formulated via multiple Python code examples that enable the students to work independently when dealing with small Python programming tasks.

Both programming modules are highly recommended for all the students on the programme. They are compulsory for students who either opt for the MSc Actuarial Science with Business Analytics or remain on the MSc Actuarial Science but take some of the Business Analytics elective modules.

The modules are designed to provide a fundamental understanding of R and Python and no previous programming experience is expected. You are strongly encouraged to complete these modules as they will help you with the computer-based elements of the CM and CS subjects and ensure that you have the minimum specific background required for the Business Analytics modules.

• Financial Mathematics (CM1(1)) compulsory module unless you hold a prior exemption

  Students will learn how to apply compound interest theory to find the present value or the accumulation of a cash flow, and to apply financial mathematics to solve a broad range of practical problems also via computer-based applications. In addition, this module will demonstrate how loan repayments can be determined, once interest rate assumptions have been made. Students will analyse and compare alternative capital projects and value fixed-interest stock.

• Probability and Mathematical Statistics (CS1)

  This module will enable students to master the axioms of probability and conditional probability, the concept of a random variable and a probability distribution, and to define and use generating functions. They will apply and debate the principles of statistical inference, explain and evaluate the theory of underlying statistical techniques. They will construct statistical displays of data, solve problems with more than one random variable, find moments of distributions, carry out and interpret analysis of variance, linear regression, and generalised linear regression models. They will test hypotheses and derive confidence intervals. They will explain the fundamental concepts of Bayesian statistics and use them to compute Bayesian estimators. They will also apply bootstrap methods. Finally, students will become proficient in a broad range of related computer-based applications in R.

• Finance and Financial Reporting (CB1)

  Students will be able to explain the structure of joint stock companies, define the principal forms of financial instruments, and discuss the characteristics of different financial statements. They will master the principles underlying the construction of financial statements and be able to apply and evaluate alternative approaches in interpreting the financial statements of companies and financial institutions. They will also be able to construct financial statements in a form suitable for publication.

• Business Economics (CB2)

  This module will give students the ability to understand the key aspects of the operation of markets, consumer demand, the production decisions of a firm, the determinants of market structure, and the effects of market structure on a firm’s supply and pricing decisions. Students will discuss the economic analysis at both the micro and macro levels, focusing on those areas most relevant to actuarial science, as well as the implications of changes in relevant variables on the equilibrium operation of markets. They will also develop an understanding of macroeconomic analysis and interpret the economic environment with regard to inflation, investment returns, stock market behaviour, exchange rates and economic growth.

• Research Methods for Actuarial Professionals

  Strong research is a key element of development strategy for companies and institutions, large and small. This module aims to provide a ground in statistical learning research, particularly supervised and unsupervised learning, which you will be able to apply to real data. The content is tailored to support you and to develop your research and statistical learning skills. The module will utilise specific training in statistical learning techniques in order to provide a strong foundation for the in-depth and specialist teaching and learning in Terms 2 and 3. You will also develop an intuition behind the methods which you will be able to use to support your learning, substantiate your arguments and make assessments about the nature of the evidence you are using.

• Analytics Methods for Business

  This module provides a collection of standard analytical methods and explains how data analysis is performed in the real world.

  They represent an introduction to specific tasks that a business analyst has on a daily basis that ultimately would help in analysing, communicating and validating recommendations to change the business and policies of an organisation.

  Furthermore, the module provides the foundation for using the R programming language to translate theory into practice.

• Contingencies (CM1(2)) compulsory module unless you hold a prior exemption

  Students will gain an understanding of a broad range of life insurance products, their pricing and reserving, and a mastery of life insurance mathematics. They will also be able to evaluate means and variances of present values of cash flows for complex insurance contracts, and calculate gross premiums and reserves using the equivalence principle, profit testing and related ideas. Finally, they will be able to apply mathematics and statistics to related practical problems via computer-based applications.

• Insurance Risk Modelling (CS2)

  This module aims to explain the fundamental risk modelling for insurance applications. Students will develop proficiency in using statistical and stochastic modelling for life and non-life insurance risks. Various topics will be accompanied by computer-based applications.

• Financial Economics (CM2)

  Students will develop a proficiency in the application of models used in financial economics and understand how these models are used, also via computer-based applications. They will analyse insurance problems in terms of utility theory, define measures of investment risk, and describe how insurance companies help reduce or remove risk. They will be able to explain the assumptions and ideas underlying different financial models, and apply finance theory to assess risk, make portfolio decisions, model asset prices and interest rates, and value derivatives.

• Machine Learning

  This module provides on overview of machine learning concepts, techniques and algorithms used in practice to describe and analyse complex data, and design predictive analytics methods. You should expect to engage with the main idea and intuition behind modern machine learning tools from a practical perspective. Standard computing skills in R,Python and Matlab will be used to put in practice the theory discussed during the lectures.

Students have the option of studying specialised electives in Term 3 to give them a breadth of subject matter. If students would like to study one particular area of interest in depth, they can take a project, which in some cases may be completed in partnership with a sponsoring organisation.

Business Research Project (BRP)

BRP will be of approximately 10,000 words. The BRP offers an opportunity to specialise in a contemporary topic in actuarial science or finance related to students’ future careers. The BRP should be based on independent research. Students are encouraged from the start of the course to think about a topic for their BRP. A member of academic staff, allocated on the basis of the student’s project proposal, supervises the BRP.

Applied Research Project (ARP)

ARP will be of approximately 3,000-5,000 words. In this case, the topic is supplied by Bayes faculty and initial guidance is offered but no formal supervision.  BRP or ARP must be completed and submitted by the end of August.

Over the years, several students taking research projects in this programme have been recipients of the prestigious SCOR award.

Students on the MSc Actuarial Science with Business Analytics can choose* projects designed by our industry partners that aim to develop the students’ consulting skills. These include various analytics consulting companies, companies from the finance and insurance sectors, well-known retailers, etc., and some examples are: Bank of England, Ekimetrics, Fiat Chrysler Automobiles, Government Actuary's Department, Velador Associates and Vodafone UK. Most of the projects are directly supervised by the industry partner representatives together with our academic staff.

* subject to availability

Electives offered in 2021

• Applied Machine Learning
• Applied Natural Language Processing
• Data Management Systems
• Introduction to Copula Modelling
• Introduction to Model Office Building in Life Insurance
• Modelling and Data Analysis
• Emerging Global Risks
• Stochastic Claims Reserving in General Insurance
• Ethics, Society and the Finance Sector
• Financial Crime
• Financial Statement Analysis & Valuation in Banks
• Liability Insurance
• Technical Analysis and Trading Systems.

International electives

• FinTech (taught in Italy)

Please note that electives are subject to change and availability.

Download the MSc Actuarial Science programme specification

Download the MSc Actuarial Science with Business Analytics programme specification