Who is it for?
If you have strong technical ability and an interest in solving business problems, becoming an actuary is one of the most rewarding career choices you can make. Changes in the world bring new risks, which mean new challenges for actuaries.
It will offer you a firm grounding in the fundamentals of actuarial science in insurance, finance and investment, business analytical methods, machine learning, data management systems and natural language processing. You will undertake a detailed study of the mathematical and statistical techniques for measuring the probability and risk of future events and their financial impact on a business and/or their clients.
MSc in Actuarial Science with Business Analytics
Since 2020 we are the first in the world to offer an MSc in Actuarial Science with Business Analytics pathway, which will also prepare you for the non-traditional actuarial field of business analytics.
You will gain a firm grounding in the fundamentals of actuarial science in insurance, finance and investment. You will also advance your knowledge in analytics methods for business, machine learning, data management systems and natural language processing, all of which are increasingly being adopted across actuarial practice areas.
As the needs of the actuarial profession and the Institute and Faculty of Actuaries evolve, the skill set of actuaries is used in wider applications in both traditional and non-traditional fields, and the intake of overseas members increases, this programme is designed to reflect these challenges and ensure that it is fit for purpose for actuaries in an ever-changing global business environment.
On this postgraduate course, you will study statistics, probability, stochastic processes, survival models, economics, finance and investment, insurance, pensions and financial contracts valuation, with computer-based applications. This broad and varied syllabus is equivalent to the Institute and Faculty of Actuaries’ Core Mathematics, Core Statistics and Core Business professional examinations (Subjects CM1, CM2, CS1, CS2, CB1, CB2), and enables you to gain exemptions from them.
In addition, this programme will give you the opportunity to study business analytical methods and learn how data analysis is performed in the real world. You will be able to study machine learning techniques and their use in analysing complex data and designing predictive analytics methods.
The programme is delivered via face-to-face lectures from qualified actuaries, academics and other subject-specialists, complemented by dedicated online support and computer-based applications, easy access to faculty members, and advice on study and exam techniques. Lecturers use their commercial experience and research expertise to deliver a challenging, relevant and intellectually stimulating course. Bayes Business School is currently ranked 2nd in Europe and 4th in the world in the UNL Global Research Rankings of Actuarial Science and Risk Management & Insurance.
Successful candidates on the MSc in Actuarial Science or the MSc in Actuarial Science with Business Analytics may also proceed to the MSc in Actuarial Management
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What will you learn
On the MSc Actuarial Science course, you will:
- Summarise and critically assess fundamental concepts in statistics, economics, finance, investment and business.
- Recognise and apply actuarial theory used in investment, insurance and probability modelling.
- Evaluate research papers and professional texts to produce an independent synthesis of knowledge and ideas.
- Demonstrate proficiency in the use of actuarial and statistical methods to solve problems in insurance, investment and analytics problems.
- Evaluate and apply alternative approaches in the analysis of financial reports.
- Develop and communicate effectively reasoned arguments on current issues relating to actuarial theory and practice.
- Use software as an effective tool for data analysis and financial modelling.
On the MSc Actuarial Science with Business Analytics course, you will additionally:
- 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 be able to suggest practical solutions to them.
- Carry out analysis and effectively communicate the results to a defined audience.
Modules corresponding to the actuarial professional subjects CM1, CM2, CS1, CS2, CB1 and CB2 are taught over Terms 1 and 2 of both the MSc Actuarial Science and MSc Actuarial Science with Business Analytics, in addition to the Research Methods for Actuarial Professionals taught in Term 1 and the (non-exemption) Business Analytics modules in Terms 2 and 3.
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 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.
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
Term 3 electives
- 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
- Stochastic Claims Reserving in General Insurance
- Topics in Quantitative Risk Management
- Ethics, Society & the Finance Sector
- Emerging Global Risks
- FinTech (international elective – Italy)
- Pension Finance
- VBA with Application for Finance
- Technical Analysis and Trading Systems
- Mergers & Acquisitions
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 any group work will include an element of peer assessment.
Please note that group work will include an element of peer assessment in most cases.
To satisfy the requirements of the MSc Actuarial Science, students must complete:
- at least five modules (across Terms 1 and 2) in addition to the Research Methods for Actuarial Professionals module (Term 1)
- five electives in Term 3
- one elective and a Business Research Project in Term 3
- three electives and an Applied Research Project in Term 3.
To satisfy the requirements of the MSc Actuarial Science with Business Analytics, students must complete:
- Introduction to R Programming
- Introduction to Python Programming
- at least six modules (across Terms 1 and 2), including Analytics Methods for Business and Machine Learning, in addition to the Research Methods for Actuarial Professionals module (Term 1)
- five electives in Term 3
- three electives and an Applied Research Project in Term 3.
For the MSc Actuarial Science with Business Analytics, the students must choose in Term 3 at least two of: Applied Machine Learning, Applied Natural Language Processing, and Data Management Systems
Term dates 2022/23
- Induction: 12th September 2022 – 23rd October 2022
- Term one: 26th September 2022 - 9th December 2022
- Terms two and three and exams to be confirmed.
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.
Please note that all academic timetables are subject to change.
The teaching staff on the MSc in Actuarial Science 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 MSc in Actuarial Science.
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
- Dr 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 for either the MSc Actuarial Science or the 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
- 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 documents must be verified, so an original or certified copy of the degree transcript must be uploaded to the application form or e-mailed to the relevant Admissions Officer upon request
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.
Unfortunately, as a result of the evolving situation regarding the coronavirus (COVID-19) pandemic, we can only offer online appointments to discuss your application. To book one, please get in touch with the relevant Admission Officer.
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 the IELTS test
If you have studied a 2+2 degree with just two years in the UK you will be required to provide IELTS results and possibly to resit the tests to meet our requirements.
- The required IELTS level is an average of 7.0 with a minimum of 6.5 in the writing section and no less than 6.0 in any other section.
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
Our graduates from the MSc in Actuarial Science are well prepared to tackle actuarial and risk analyst, consultancy and underwriting roles in leading firms. An actuarial career is a global professional passport in a role which is as challenging as it is rewarding.
An increasing number of companies are now looking for actuarial students who have knowledge of business analytics. This is due to the changing nature of actuarial roles with more technology and analytics brought into actuarial processes across most of the industry sectors mentioned above. The MSc Actuarial Science with Business Analytics route will enable students to gain a strong understanding of both the actuarial principles and the associated analytics.
Our dedicated Careers Team will help you identify your ideal career path and work with you to maximise the potential of accomplishing your professional goals.
Class of 2020 profile
Recent graduates have secured positions such as
- Actuarial Specialist - Life and Health Insurance - Munich Reinsurance
- Graduate Risk Consultant - Risk& Regulatory Compliance - Avantage Reply
- Graduate Actuarial Analyst - Insurance - HSBC
- Actuary Associate - Financial Services Advisory - KPMG
- Junior Data Scientist - R&D - Arrival
Where they are working now
- UK: 31%
- EU/EEA: 8%
- North America: 8%
- Asia: 54%
- Africa and Middle East: 0%
The remaining industries are made up of 8%
Data provided from alumni who completed the annual destination data survey 2019/20
Shriya Gupta,Pensions Graduate Offer, Aon, Class of 2017
'Using the experience and relevant knowledge of the career team helped me greatly through the job application process and also gave me the confidence to be successful in them'
Sean Quinn, Director, Cambridge Guarantee Group, 2016
'We have been recruiting actuarial trainees from the Business School (formerly Cass) for the past two years. The quality of the candidates has been high and the careers team have been extremely helpful and proactive. For us, the Business School is a treasure trove of talent ready to enter the business world.'
Daria Prychantovska, MSc in Actuarial Science, Class of 2016
'This year at the Business School (formerly Cass) has been one of the most challenging and fascinating experiences of my life. My high expectations were fulfilled: exceptional professors, incredible people from all over the world, an amazing city and, to top it all, a desirable job offer, obtained with a massive support from our careers professionals. Thank you! Thank you, London!'
The MSc in Actuarial Management serves as a continuation of the MSc in Actuarial Science allowing successful candidates to focus on the application of concepts learned, study the key areas of actuarial practice and 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 Core Practice and Specialist Principle subjects of the Institute and Faculty of Actuaries.
Hear from our alumni. Discover the real experiences of learning at Bayes and how Bayes helped to boost our graduates' careers.Read Andreas’ story
Course information and statistics (2021/22 cohort)
The programme is accredited by the Institute and Faculty of Actuaries to offer exemptions from their Core Modelling, Core Statistics and Core Business professional examinations (Subjects CM1, CM2, CS1, CS2, CB1, CB2). The MSc in Actuarial Science was introduced in 1985. Today it remains cutting edge, following regular reviews from employers through our Advisory Board, and now has an innovative Business Analytics pathway.