Who is it for?
This pathway is for you if you like to think about innovative tools – there is a rising need for these people in actuarial practice, and demand is far higher than supply. Our dedicated MSc in Actuarial Science with Business Analytics, established in 2020, aims to fill this gap.
Choose this MSc if you are interested in understanding the fundamentals of traditional areas of actuarial practice, and in taking advantage of unique opportunities to grow in the new areas of business analytics methods, machine learning, data management systems and natural language processing. You’ll already hold the equivalent of a UK upper second class degree in a highly quantitative programme, such as mathematics or statistics.
You will be taught and receive advice on study and exam techniques by qualified actuaries, academics and other subject-specialists with commercial experience and research expertise. Bayes Business School is currently ranked 1st in Europe for actuarial research according to the Top 50 UNL Global Research Rankings of Actuarial Science and Risk Management & Insurance.
This postgraduate programme, with a choice of study options, will ensure that you learn the right tools and cultivate the new skillset for actuaries and become competent and ready to harness these in an ever-changing global business environment.
Like our MSc in Actuarial Science, this pathway will offer you the choice of the Core Principles of the Institute and Faculty of Actuaries’ curriculum, enabling you to gain exemptions from Subjects CM1, CM2, CS1, CS2, CB1, CB2. Also recognising the importance of modelling and communication practices in a business environment, you will be given the opportunity to gain exemptions from Subjects CP2 and CP3*.
Add further value to your course by studying four compulsory Business Analytics modules. You will learn how data analysis is performed in the real world and study machine learning techniques and their use in analysing complex data and designing predictive analytics methods.
Ask a student
Chat to one of our MSc Actuarial Science with Business Analytics students now and have them answer your questions on everything from application to student life.
What will you learn
On the MSc Actuarial Science with Business Analytics you will:
- Summarise and critically assess fundamental concepts in actuarial science, insurance, economics, finance, investment, financial reporting and business
- Make use of analytical skills to evaluate and solve complex problems within the organisation’s strategic perspective
- Demonstrate critical awareness of current analytical methods in order to transform information into knowledge
- Increase your understanding and knowledge of how current analytical methods could be applied in practice
- Analyse the breadth of machine learning techniques and their applications
- Frame analytics problems from a machine learning perspective and suggest practical solutions to them
- Carry out modelling and effectively communicate the results to a defined audience.
Note: the key difference from the MSc in Actuarial Science is that the modules Analytics Methods for Business (term 1), Machine Learning (term 2) and at least two from Applied Machine Learning, Applied Natural Language Processing, and Data Management Systems in term 3 become compulsory in this programme. You are also expected to pass the (non-credit-bearing) modules Introduction to R Programming and Introduction to Python Programming before being allowed to transfer to MSc Actuarial Science with Business Analytics.
All of our MSc courses start with two compulsory induction weeks which include relevant refresher courses, pre-study programming modules, an introduction to the careers services and the annual careers fair.
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.
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.
* Pending approval
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.
- FinTech (taught in Italy)
Please note that electives are subject to change and availability.
Assessment of modules on this programme, in most cases, is by means of coursework and unseen examination. Coursework may comprise computer-based components, unseen tests and problem sets, classwork, individual and group presentations, group reports and standard essays. Please note that group work will include an element of peer assessment in most cases.
Term dates 2023/24
- Induction: 11th September 2023 - 22nd September 2023
- Term one: 25th September 2023 - 8th December 2023
- Term one exams: 8th January 2024 - 19th January 2024
- Term two: 22nd January 2024 - 5th April 2024
- Term two exams: 22nd April 2024 - 3rd May 2024
- Term three - international electives: 6th May 2024 - 17th May 2024
- Term three: 20th May 2024 - 5th July 2024
- Term three exams: 8th July 2024 - 19th July 2024
- Resits: 12th August 2024 - 23rd August 2024
- Additional resit week - tests only: 26th August 2024 - 30th August 2024.
Course timetables are normally available from July and can be accessed from our timetabling pages. These pages also provide timetables for the current academic year, though this information should be viewed as indicative and details may vary from year to year.
View academic timetables (add timetable and search for your programme)
Please note that all academic timetables are subject to change.
The teaching staff on the MSc in Actuarial Science with Business Analytics have many years of practical experience working in the insurance, pensions and financial services sectors and are also active researchers in their fields. This knowledge and experience inform the highly interactive lectures that make up the Actuarial Science with Business Analytics.
Module Leaders include:
- Professor Vali Asimit
- Dr Zoltan Butt
- Dr Michail Chronopoulos
- Dr Russell Gerrard
- Mr David Hargreaves
- Dr Zaki Khorasanee
- Dr Pietro Millossovich
- Professor Jens Perch Nielsen
- Dr Iqbal Owadally
- Professor Keith Pilbeam
- Professor Rosalba Radice
- Professor Ben Rickayzen
- Mr Nick Silver
- Dr David Smith
- Dr Jaap Spreeuw
- Dr Douglas Wright
- Dr Rui Zhu
How to apply
Individuals wishing to apply fo MSc Actuarial Science with Business Analytics should apply for the MSc Actuarial Science in the first instance.
Applicants who are interested in the MSc Actuarial Science with Business Analytics will be able to opt for it during Term 1. They will need to provide a supporting personal statement and successfully pass the pre-entry modules Introduction to R Programming and Introduction to Python Programming.
Documents required for decision-making
- Transcript/interim transcript
- Grading system used by your university
- Current module list if still studying
- Confirmation of professional qualification examinations/exemptions/passes if applicable
- Personal statement - this should be around 500 words in length and answer the following:
- Why have you selected this course? What are your motivating factors?
- What are your areas of interest within the course?
- What contributions do you feel you can make to the course?
- How do you see the course affecting your career plans?
Documents which may follow at a later date
- English language test result if applicable
- Two references
- For a successful application to receive an unconditional status, all award documents must be verified, and should be provided to the relevant Admissions Officer via one of the methods stated in your offer.
We cannot comment on individual eligibility before you apply and we can only process your application once it is fully complete, with all requested information received.
If you would like to arrange an individual appointment to discuss the application process and be given a tour of the facilities, please complete this form.
Please note - these are subject to availability.
Terms and conditions
Students applying to study at Bayes Business School are subject to City, University of London's terms and conditions.
- A UK upper second class degree or above, or the equivalent from an overseas institution, in a highly quantitative programme such as mathematics or statistics is required to enter this course
- Work experience is not a requirement of this course.
English language requirements
If you have been studying in the UK for the last three years it is unlikely that you will have to take an English language test.
If you have studied in the UK at degree level for less than three years (e.g. 3+1, 2+1, 2+2, etc.) you will be required to provide the results of an approved English language test and possibly resit the test to meet our academic entry requirements.
Fees in each subsequent year of study (where applicable) will be subject to an annual increase of 2%. We will confirm any change to the annual tuition fee to you in writing prior to you commencing each subsequent year of study for continuing students (where applicable).
Deposit: £2,000 (usually paid within 1 month of receiving offer and non-refundable unless conditions of offer are not met)
First installment: Half fees less deposit (payable during on-line registration which should be completed at least 5 days before the start of the induction period)
Second installment: Half fees (paid in January following start of course)
Career destinations for MSc Actuarial Science with Business Analytics
Your Master’s in Actuarial Science with Business Analytics prepares you to tackle roles in actuarial and risk analysis, consultancy and underwriting in leading banking, insurance and financial services firms. As more technology and analytics are introduced across the actuarial industry, your skills will be invaluable to the increasing number of companies seeking actuarial students who have knowledge of business analytics.
Our expert Careers Team will give you advice on your finding your ideal career path and how to achieve your professional goals.
MSc Actuarial Science class profile
Recent graduates have secured positions such as
- Graduate Actuary, Reinsurance Reserving -Aspen Insurance
- Actuarial Analyst - Willis Towers Watson
- Junior Actuarial Consultant, Actuarial - EY
- Senior Actuarial Analyst (Pricing) - AXA
- Consultant - Samsung SDS
The MSc in Actuarial Management serves as a continuation of the MSc in Actuarial Science with Business Analytics allowing successful candidates to focus on the application of concepts learned, study the key areas of actuarial practice, choose from the various actuarial specialist subjects and attain further technical knowledge. Students taking that MSc get an opportunity to obtain further exemptions from the later professional subjects of the Institute and Faculty of Actuaries, enabling them to reach the highest possible level of the professional qualification.
Hear from our alumni. Discover the real experiences of learning at Bayes and how Bayes helped to boost our graduates' careers.Read all stories