Actuarial Science with Business Analytics MSc
Key information
Duration: 12 months
Attendance mode: Full-time
Location: Bunhill Row
Start of programme: September 2025
Application deadline: Rolling applications
Entry year: Showing course information for 2025
Develop sought-after business analytical skills of modern actuaries
Overview
Actuarial Science with Business Analytics MSc 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 will 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.
50th anniversary of actuarial science at Bayes
This year marks the 50th anniversary of actuarial science at Bayes, a milestone for its esteemed Faculty of Actuarial Science and Insurance (FASI). Established five decades ago, FASI continues to be pioneering and highly respected, continuously evolving with market trends and cultivating students who drive innovation and shape the future of the industry.
How to become an actuary
For aspiring actuaries who thrive on innovative thinking and creativity
An enduring and constantly evolving degree embracing traditional and cutting-edge actuarial fields
Ranked 1st Business School in Europe for actuarial research. Top 50 UNL Global Research Rankings of Actuarial Science and Risk Management & Insurance.
Course objectives
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.
If you succeed on the MSc in Actuarial Science with Business Analytics or MSc Actuarial Science you can also proceed to the MSc in Actuarial Management.
Teaching staff
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 MSc in Actuarial Science with Business Analytics.
Course Director:
Module leaders include:
- Professor Vali Asimit
- Dr Zoltan Butt
- Dr Michail Chronopoulos
- Dr Russell Gerrard
- Dr Zaki Khorasanee
- Professor Ioannis Kyriakou
- Dr Pietro Millossovich
- Professor Keith Pilbeam
- Professor Jens Perch Nielsen
- Dr Iqbal Owadally
- Professor Rosalba Radice
- Professor Ben Rickayzen
- Dr Douglas Wright
- Dr Rui Zhu
- Mrs Komal Shah
- Mr David Hargreaves
- Mr Nick Silver
Accreditation details
Bayes Business School is recognised as a Center of Actuarial Excellence (CAE) from the Society of Actuaries (SOA), one of the main actuarial professional bodies in the US. Together with our accreditation by the Institute and Faculty of Actuaries in the UK, this reinforces our establishment as a global hub for actuarial studies and intellectual capital development. Our courses are also recognised as preparing students for certain SOA actuarial exams.
Course content
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 transferring to MSc Actuarial Science with Business Analytics.
Course structure
Programme content is subject to change. We regularly review our module offering and amend to keep up to date and relevant.
Induction weeks
All 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.
Pre-Study Modules
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 teaching 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 teaching 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. You are encouraged to undertake these modules so that you can get the most out of your degree. Completing these two modules will also help you with the computer-based elements of the CM and CS subjects.
Term 1
Compulsory Modules
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 wide range of practical problems also via computer-based applications. You will analyse and compare alternative capital projects and perform investment valuation.
Along with Contingencies (CM1(2)), this module will cover Subject CM1 of the UK Institute and Faculty of Actuaries’ professional examinations.
Elective Modules
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. The module will cover Subject CS1 of the UK Institute and Faculty of Actuaries’ professional examinations.
Finance and Financial Reporting (CB1)
The module is designed to provide you with the skills and knowledge to engage professionally with finance and financial reporting within a company. The finance section of the module will provide you with a basic understanding of the methods and types of instruments used by companies to raise finance. The financial reporting section will enable you to interpret the published financial statements of companies and financial institutions. The module will cover Subject CB1 of the UK Institute and Faculty of Actuaries’ professional examinations.
Business Economics (CB2)
This module will introduce you to fundamental concepts of economic analysis at both micro and macro levels, focusing on those areas most relevant to actuarial science. In doing so, it will cover Subject CB2 of the UK Institute and Faculty of Actuaries’ professional examinations.
Students with a prior exemption from CM1 may also be exempted from Bayes CM1(1) and CM1(2), subject to the provision of evidence of the prior exemption. However, they will be required to replace these with other term 1 and 2 modules.
Term 2
Compulsory Modules
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 and their pricing, and a mastery of life insurance mathematics. You will be able to calculate gross premiums and reserves for fixed and variable benefit contracts. You will be introduced to annuities and assurances involving two lives, and multiple decrement models. Finally, you will be able to apply mathematics and statistics to related practical problems via computer-based applications.
Along with Financial Mathematics (CM1(1)), this module will cover Subject CM1 of the UK Institute and Faculty of Actuaries’ professional examinations.
Elective Modules
Financial Economics (CM2)
You will develop a proficiency in the application of models used in financial economics and understand how they 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. The module covers Subject CM2 of the UK Institute and Faculty of Actuaries’ professional examinations.
Insurance Risk Modelling (CS2)
This module aims to explain the fundamental risk modelling for insurance applications. Specifically, statistical and stochastic modelling for life and non-life insurance risks are discussed in order to cover Subject CS2 of the UK Institute and Faculty of Actuaries’ professional examinations. Various topics will be accompanied by computer-based applications.
Students with a prior exemption from CM1 may also be exempted from Bayes CM1(1) and CM1(2), subject to the provision of evidence of the prior exemption. However, they will be required to replace these with other term 1 and 2 modules.
Term 3
To complete the MSc in Actuarial Science with Business Analytics, you have several options in your third term:
- Five electives plus Career Management Skills and Research Methods
- Three electives and a General Research Project
- Modelling Practice (CP2), Professional Communication (CP3), Career Management Skills and Research Methods and two elective modules.
For each option, at least two electives should be chosen from:
Applied Machine Learning
This module is designed to introduce you to machine learning and to give you some practical experience of applying a range of machine learning techniques. The lectures will be based around case studies which will be demonstrated in Python or R. Coursework will require you to adapt and extend the code provided during the lectures and to carry out a machine learning project from scratch. The course is focused mainly on practical aspects of machine learning but some relevant theory will be discussed.
Applied Natural Language Processing
This module focuses on modern frameworks and tools to analyse massive datasets containing ‘natural language’ data, e.g., economic-financial reports, press releases, speeches, or online product reviews. The students should expect: a) to learn efficient methods to represent and store ‘natural language’ data; b) to familiarise with the latest developments in the area of semantic and topic modelling; and c) to mobilize natural language processing algorithms in the context of deep learning frameworks (e.g., Google’s Tensor Flow library). Standard computing skills in Python are required in order to put in practice the theory discussed during the lectures.
Data Management Systems
This module focuses on modern frameworks and tools to getting, storing, and deploying massive datasets. The students should expect: a) to appreciate the latest technological developments in the area of data management systems; b) to familiarise with two highly popular database management systems, namely Postgre and MongoDB; c) to leverage Postgre and MongoDB in order to manipulate massive datasets―that is, datasets that do not fit in RAM; d) to use Postgre and MongoDB in the context of two key data management tasks―that is, storing continuous flows of data brought by a web-scraper, and parsing XML files (widely adopted to create data dumps out of corporate or web archives). Standard computing skills in Python, R, and basic knowledge of MS Access and Excel are required in order to put in practice the notions discussed during the lectures.
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. It covers Subject CP2 of the UK Institute and Faculty of Actuaries’ professional examinations.
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. The module covers Subject CP3 of the UK Institute and Faculty of Actuaries’ professional examinations.
General Research Project
This module will help you integrate your learning across a range of skills and in particular to enable you to demonstrate the results of your learning through the development of a final project. You may choose to complete more traditional academic desk research, complete a business plan or carry out a consultancy project and any reasonable proposal is acceptable providing that it is agreed with the project supervisor and the course director
Career Management Skills and Research Methods.
Supporting your wider learning and career success through the development of key transferrable skills and attributes that you will use both in your programme and across your career. In particular: research and analysis, career planning and employability skills development, and sustainable working.
Develop a grounding in research methods, targeted to your particular degree requirements. You will be able to use these skills to support your learning, substantiate your arguments and make assessments about the nature of the evidence you are using.
Career and employability skills development, delivered in a highly targeted and applied format throughout the year, will help you develop your aspirations, take a planned and efficient approach to your job search and / or entrepreneurial plans, and help you create a strong base from which to manage your career in the longer term. The content of this module will also help you understand how to apply your learning in the wider context of your career.
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.
Short electives offered in 2023
The short electives aim to offer a breadth of subject matter in actuarial science, insurance, business analytics, finance and investment.
- Applied Machine Learning
- Applied Natural Language Processing
- Data Management Systems
- Emerging Global Risks
- Topics in Quantitative Risk Management
- Advanced Predictive Analytics
- Ethics, Society and the Finance Sector
- Financial Crime
- Financial Risk: The Future of Finance (jointly with USS)
- Valuation of Financial Institutions.
International electives
- FinTech (taught in Italy)
- International Real Estate Markets (Dubai).
More about elective modules at Bayes
Please note that electives are subject to change and availability.
Download course specification:
Actuarial Science with Business Analytics MSc [PDF]Assessment methods
Term dates
Term dates 2025/26
- Induction: 15th September 2025 - 26th September 2025
- Term one: 29th September 2025 - 12th December 2025
- Term one exams: 5th January 2026 - 16th January 2026
- Term two: 19th January 2026 - 3rd April 2026
- Term two exams: 20th April 2026 - 1st May 2026
- Term three - international electives: 4th May 2026 - 15th May 2026
- Term three: 18th May 2026 - 3rd July 2026
- Term three exams: 6th July 2026 - 17th July 2026
- Resits: 10th August 2026 - 21st August 2026
- Additional resit week - tests only: 24th August 2026 - 28th August 2026.
Timetables
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.
Fees & funding
UK/Home fee
September 2025 entry
£15,800
MSc Full-time
Tuition fees are subject to annual change.
International fee
September 2025 entry
£26,000
MSc Full-time
Tuition fees are subject to annual change.
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).
Tuition fee increases
Where applicable, tuition fees for Bayes' programmes will be subject to inflationary increases in each year of study. Our policy for these increases is set out in our terms and conditions of study.
Scholarships & bursaries
Scholarships, sponsorships, loans and other funding could support your education at Bayes Business School.
Learn about the cost of living as a Bayes student in London.
View our scholarships and fundingCareers
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 become invaluable to the increasing number of companies seeking actuarial students who have knowledge of business analytics.
Over the past 12 months, our Actuarial Science with Business students have had the opportunity to attend sessions such as an introduction to the IFoA and the actuarial industry, along with a panel on business analytics in the actuarial field. Various alumni panels provided insights into typical career paths followed by our graduates, while Sector Overviews covering areas such as insurance and consultancy, offered further industry exploration. Students are also encouraged to engage with professionals through our annual careers fair and many corporate events throughout the year.
Recent graduates have secured positions such as:
- Actuarial Analyst
- Trainee Actuary
- Model Management and Development Analyst
- Junior Specialist
- Auditor.
Download our latest MSc Employment Report
Dedicated to your success
From the moment you accept your offer, you'll have access to our dedicated Postgraduate Careers Service, tailored to support your career journey. Our Career Accelerator module, launched over the summer, provides early access to valuable resources, employer insights, and career planning tools to prepare you for the job market.
Our expert Postgraduate Careers Team, made up of experienced recruiters and career coaches offers personalised one-to-one guidance, covering everything from networking and CV building to job offer management and mock interviews.
Whether you’re pursuing a new career path, seeking an entry-level role, or launching your own business, you'll receive support to develop the skills and knowledge needed to succeed.
You'll also benefit from exclusive events such as Careers Fairs, panel discussions, and networking opportunities with industry professionals and alumni, helping you explore career options and make valuable connections.
Workshops led by our Careers Team and industry experts cover essential professional skills, including presentation techniques, personal branding, and assessment centre success, ensuring you stand out in the job market.
Additionally, Bayes Careers Online (BCO) offers career planning tools, guidance leaflets, access to job postings directly from employers and key career events, helping you stay on track with your career goals.
Continuing study
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.
Recent employers
Alumni stories
Entry requirements
- A UK upper second-class degree or higher, or the equivalent from an overseas institution, in a highly quantitative programme, such as mathematics, statistics, or engineering
- 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.
Full list of approved English language tests/qualifications and minimum requirements.
Apply
Please see our Application Guide for details of the documents you will need to supply as part of your application, and other useful information.
We cannot comment on individual eligibility before you apply. We can only make a decision on your application once it is fully complete, with all requested information received.
Frequently asked questionsApply for September 2025 entry
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. You will need to provide a supporting personal statement and successfully pass the pre-entry modules Introduction to R Programming and Introduction to Python Programming.
We only accept online applications.
Individual Appointments
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.
Student life
We are located right in the heart of London. Being a student at City allows you to take advantage of all that London has to offer.
London is ranked first as 'Best Student City' in the world to study in (QS, 2025).
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