Actuarial Science with Business Analytics

• 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 designed to provide a fundamental understanding of R and Python. They are highly recommended for all the students on the programme and are compulsory for those who either opt for the MSc in Actuarial Science with Business Analytics or remain on the MSc Actuarial Science but take Business Analytics elective modules. Completing these two modules will also help you with the computer-based elements of the CM and CS subjects.

• Analytics Methods for Business (non-exemption module)

  This module provides a collection of standard analytical methods and explains how data analysis is performed in the real world. It represents an introduction to specific tasks that a business analyst encounters on a daily basis that ultimately help in analysing, communicating and validating recommendations to change the business and policies of an organisation. Furthermore, the module provides the foundation for using R to translate theory into practice.

• Financial Mathematics (CM1(1))

  You 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. You will analyse and compare alternative capital projects and value fixed-interest stock.

• Probability and Mathematical Statistics (CS1)

  This module will enable you 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. You will apply and debate the principles of statistical inference, explain and evaluate the theory underlying statistical techniques. You will construct statistical displays of data, solve problems with more than one random variable, find moments of distributions, carry out and interpret linear regression and generalised linear regression models. You will test hypotheses and derive confidence intervals. You will explain the fundamental concepts of Bayesian statistics. Finally, you will become proficient in a broad range of related computer-based applications in R.

• Finance and Financial Reporting (CB1)

  You 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. You will master the principles underlying the construction of financial statements and be able to apply and evaluate alternative approaches to interpreting the financial statements of companies and financial institutions. You will also be able to construct financial statements in a form suitable for publication.

• Business Economics (CB2)

  This module will give you 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. You will discuss the economic analysis at both 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. You 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 (non-exemption module)

  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.

• Machine Learning (non-exemption module)

  This module provides an 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 in the lectures.

• Contingencies (CM1(2))

  You will gain an understanding of a broad range of life insurance products, their pricing and reserving, and a mastery of life insurance mathematics. You 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, you 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 fundamentals of risk modelling for insurance applications. You 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)

  You 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. You will analyse insurance problems in terms of utility theory, define measures of investment risk, and describe how insurance companies help reduce or remove risk. You 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 evaluate derivatives.

To complete the MSc in Actuarial Science with Business Analytics, you have several options in your third term:

• Modelling Practice (CP2), Professional Communication (CP3) and two electives from Applied Machine Learning, Applied Natural Language Processing, and Data Management Systems
• Applied Research Project and three electives with at least two from Applied Machine Learning, Applied Natural Language Processing, and Data Management Systems
• Five electives with at least two from Applied Machine Learning, Applied Natural Language Processing, and Data Management Systems.

The electives aim to offer a breadth of subject matter in actuarial science, insurance, business analytics, finance and investment.

Modelling Practice (CP2)

This module will provide you with an understanding of how to model data and processes using spreadsheets, how to document your work, analyse the methods used and outputs generated, and communicate the approach, results and conclusions to your junior and senior colleagues.

Professional Communication (CP3)

You will be introduced to the skill of communicating with non-actuaries on topics related to actuarial practice. You will revise its main areas and study how technical actuarial concepts and other specialist information can be explained to clients in plain, jargon-free English. You will learn how to draft letters to those seeking professional advice in a manner suited to the level of knowledge of the recipient.

Applied Research Project (ARP)

ARP will be of approximately 3,000-5,000 words. It offers an opportunity to specialise in a contemporary topic in actuarial science, finance or business analytics and should be based on independent research. The topic is supplied by Bayes faculty and initial guidance is offered but no formal supervision.

Subject to availability, you can also choose projects designed by our industry partners that aim to develop students’ consulting skills, including 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 these projects are directly supervised by the industry partner representatives together with our academic staff.

Electives offered in 2022

• 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 with Business Analytics programme specification