The MSc Actuarial Science is a full-time postgraduate programme aimed primarily at students with undergraduate degrees from quantitative disciplines such as maths, statistics, finance or any other programme with a high degree of mathematical content.
The programme encompasses modules across the areas of actuarial mathematics, statistics, economics and finance, to provide graduates with the knowledge and skills to pursue a career as an actuary. This programme provides students with the opportunity to accrue exemptions from six of the earlier Institute and Faculty of Actuaries’ IFoA professional exams (CM1-2, CB1-2 and CS1-2) in addition to undertaking postgraduate study.
Actuarial Science highlights
Professional Accreditations
Subject to academic performance, students can gain up to six exemptions from the Institute and Faculty of Actuaries (IFoA) professional exams.
Industry Links
A ‘City Tour’ to London is organised every year, giving students the opportunity to meet actuarial employers in the city. Various networking events with industry leaders are also organised on campus. Lecturers also bring in guest lecturers from industry (e.g. actuaries working in different practice areas) to speak to students during their courses.
Internationally Renowned Experts
There are four fully qualified FIA actuaries in the faculty, with extensive industry experience. You will be taught by these qualified actuaries as well as academics, many of whom also have industry experience in various fields such as banking and insurance.
Students have the opportunity to engage with industry professionals who regularly deliver guest lectures.
Student Experience
The Student Managed Fund gives students the opportunity to manage a real money portfolio where they do the research and decide on their investment strategy.
World Class Facilities
Queen’s Business School (QBS) has recently undergone an innovative expansion that establishes a benchmark of global excellence for one of the top business schools in the UK and Ireland. A stunning new 6,000 square metre building, adjacent to the listed red-brick Riddel Hall has been designed with the latest digital infrastructure for media lecture capture, TED Talk provision and collaborative breakout sessions.
Fostering an enhanced social and educational experience the new state-of-the-art QBS venue boasts a 250-seat tiered educational space; 120-seat Harvard style lecture theatre; 150-seat computer laboratory; breakout study spaces; FinTrU Trading Room; a café, and a Business Engagement and Employability Hub.
Some of our classes are supported by DataCamp, an intuitive learning platform for data science and analytics. DataCamp allows our students to build their data science skills any time, anywhere and become an expert in R, Python, SQL, and more. https://www.datacamp.com/
The MSc Actuarial Science is a full-time postgraduate programme aimed primarily at students with quantitative undergraduate degrees from the disciplines of maths, statistics, finance or any other programme with a high degree of mathematical content.
The programme encompasses modules across the areas of actuarial mathematics, statistics, economics and finance, to provide graduates with the knowledge and skills to pursue a career as an actuary. This programme will provide students who have not undertaken an undergraduate Actuarial Science degree with the opportunity to accrue exemptions from six of the earlier Institute and Faculty of Actuaries’ IFoA professional exams (CM1-2, CB1-2 and CS1-2) in addition to undertaking postgraduate study.
Course Structure
Students pursuing a career in Actuarial Science should enjoy working with numbers, be effective communicators and work well with people as they will have to analyse and interpret financial and other information to meet the needs of different users, including clients, executive directors and investors.
Semester 1
Financial Mathematics
Statistical Analysis for Actuarial Applications
Corporate Finance
Economic Thinking for Actuaries
Semester 2
Actuarial Mathematics and Contingencies
Statistics for Insurance
Survival Modelling
Computational Methods in Finance
Queen’s Business School Mark Farrell is a Fellow of the Institute and Faculty of Actuaries and Senior Lecturer (Education) at The Business School, Queen's University Belfast. After obtaining a first class degree at Loughborough University, Mark spent a decade working in various actuarial roles in London, Toronto, Belfast and Dublin before making a move into academia in 2009 where he teaches and researches in actuarial science related fields. Mark is also a Fulbright scholar and the founder of proactuary.com – a global networking and learning hub for actuaries.
Queen's Business School Neil McConville is a Fellow of the Institute and Faculty of Actuaries and a former consulting pensions Actuary and Scheme Actuary certificate holder. Following his graduation from Queen’s University Belfast, with a degree in Finance, Neil commenced his career as an Actuary in Dublin before moving onto a number of senior roles within benefit consulting firms based in Belfast. Neil is a Senior Lecturer (Practice) and programme director of the BSc Actuarial Science and Risk Management degree programme.
Queen's Business School Bronagh Heaney is a Fellow of the Institute and Faculty of Actuaries and a Lecturer of Practice on the Actuarial Science and Risk Management degree programmes at the Business School, Queen's University Belfast.
Queen's Business School Gillian McMahon is a Fellow of the Institute and Faculty of Actuaries and a Lecturer on the Actuarial Science and Risk Management degree programme at the Business School, Queen’s University Belfast. After completing a master’s degree in Financial and Industrial Mathematics at Dublin City University and obtaining a first class bachelor’s degree in Theoretical Physics from University College Dublin, Gillian spent nine years working in an actuarial role with a Belfast based pensions consultancy. She joined the Business School in 2016.
Learning and Teaching
At Queen’s Business School, we aim to deliver a high quality learning environment that embeds intellectual curiosity, innovation and best practice in learning, teaching and student support to enable students to achieve their full academic potential. In line with this, one of QBS’ primary objectives is to deliver innovative learning and teaching programmes that provide students with the competences and skills to make a positive contribution to business, economic and civic life.
On the MSc Actuarial Science programme we aim to achieve these goals by providing a range of learning environments which enable our students to engage with subject experts both academic staff and industry guest speakers, develop skills and attributes and perspectives that will equip them for life and work in a global society and make use of innovative technologies and a world-class library that enhances their development as independent, lifelong learners. Examples of the opportunities provided for learning on this degree programme are:
E-Learning technologies
Information associated with lectures and assignments is often communicated via a Virtual Learning Environment (VLE) called Canvas. A range of e-learning experiences are also embedded in the degree programme through the use of, for example, interactive support materials, podcasts and web-based learning activities (including MS Teams). There are also opportunities to develop skills in the use of industry software associated with actuarial practice.
Lectures
These introduce foundation information about new topics as a starting point for further self-directed private study/reading. As the module progresses this information becomes more complex. Lectures, which are normally delivered in large groups, also provide opportunities to ask questions and seek clarification on key issues as well as gain feedback and advice on assessments. Additional guest lectures are also delivered by actuaries from a number of actuarial firms. In addition to the academic content of the lectures and workshops, this enables employers to impart their valuable experience to QBS Actuarial Science students and introduces important local employers to our students and allows our students to meet and engage with potential future employers.
Personal Development Planning
To encourage students to engage in independent learning.
Practicals
Actuarial Science is a very theoretical yet vocational subject and as such we facilitate opportunities for students to engage in the application of theory. You will have opportunities to develop technical skills and apply theoretical principles to real-life or practical contexts through the modules you study and through industry presentations and workshops that we host.
Self-directed study
This is an essential part of life as a Queen’s student when important private reading, engagement with e-learning resources, reflection on feedback to date and assignment research and preparation work is carried out.
Seminars/tutorials
A significant amount of teaching is carried out in small groups (typically 15-20 students). These sessions are designed to explore, in more depth, the information that has been presented in the lectures. This provides students with the opportunity to engage closely with academic staff who have specialist knowledge of the topic, to ask questions of them and to assess their own progress and understanding with the support of their peers. During these classes, students will be expected to present their work to academic staff and their peers.
Student Support Systems
QBS has an active and co-ordinated student support system to assist students in making the transition from school to university.
Support for students and their learning is provided through:
• The Programme Director (or other nominee)
• Student Guidance Centre, which provides access to University Counselling Service, Careers Service, Learning Development Service, and Disability Services
• International Student Support service
• Postgraduate Centre
• QUB Students’ Union
• Library facilities
• IT facilities (QSIS and Canvas)
• Notes for students and programme handbooks
Supervised Projects
As part of the continual assessment on a range of modules, you will be expected to undertake project work. You will receive support from the module coordinators who will guide you in terms of how to carry out your projects and will provide feedback to you during the write up stage.
Assessment
Actuarial Science modules are typically assessed by a combination of continuous assessment and a final written unseen time bound examination.
Continuous assessment consists of tutorial submissions, short class tests, individual project work, small group projects and presentations – this involves three/four students per group working on a specific task, for example, a solution to an actuarial problem.
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
The ARP comprises three inter-related elements, the first two of which help to prepare the students for the third
element, the research project.
The ARP provides students with the opportunity to utilise the knowledge and skills acquired over the previous two
semesters to plan, develop and produce a substantial piece of original, independent applied research.
The ARP is comprised of 3 elements. The first two parts are worth 25 % each and the main component, the “Data
Science Research Report” is worth 50% CAT points.
i. Financial Engineering
ii. Big Data and Statistics
iii. Data Science Research Report
The financial engineering element will cover some of the actuarial syllabus (CM2) and the actuarial research
methods component will cover some of the CS1 actuarial syllabus. Both elements will prepare students for the
main element, the data science research report.
Big Data and Statistics
The aim of the actuarial research methods element is to provide a comprehensive introduction to statistical
research techniques used in actuarial science, with a focus on Statistical Distributions used in actuarial work,
Monte Carlo Simulation, Data Analysis (including examining issues around ‘Big Data’) and statistical inference.
Assessment: A 2-hour theoretical class test (mapping to one third of the IFoA CS1 module).
This module builds on the previous Actuarial Statistics module and provides a comprehensive introduction to
statistical techniques used in actuarial science Topics will include but not be limited to:
Distributions
Monte Carlo Simulation,
Data Analysis (including examining issues around ‘Big Data’)
Statistical Inference.
Indicative readings:
• Acted: Course Notes and Core readings for Subject CS1
• Effective statistical learning methods for actuaries: I. [Generalised Linear Models] GLMs and extensions. -
Denuit, M., Hainaut, D. and Trufin, J. - Springer, 2019. ISBN 978-3030258207
• An introduction to statistical learning: with applications in R. Gareth James, Daniela Witten, Trevor Hastie,
Robert Tibshirani. Springer, 2014. ISBN: 9781461471370
Deleted: Actuarial Research Methods
Deleted: Actuarial Research Methods
Deleted: econometric and
Deleted: Confidence and Prediction Intervals,
Hypothesis Testing, Correlation, Linear Regression,
Generalised Linear Models and Bayesian Statistics.
Deleted: assignments and class based assessments.
Deleted: research
Deleted:
Statistical inference
• Hypothesis testing and goodness of fit.
• Correlation and causation
Regression theory
• Simple linear progression.
• Generalised linear models.
Bayesian Statistics
• Bayes theorem.
• Prior and posterior distributions.
• Loss functions.
Monte Carlo Simulation
• Asset Liability Modelling
• Modelling Defined Benefit and Defined Contribution
pension schemes via a Monte Carlo approach
Deleted:
• Generalized linear models. 2nd ed. McCullagh, P. and
Nelder, J.A. Chapman & Hall/CRC Press, 1989. ISBN
0412317605 [referenced in IFoA CS1 Core Reading]
Financial Engineering
The aim of the Financial Engineering element is to provide a grounding in the principles of modelling as applied
to actuarial work – focusing particularly on stochastic asset liability models and the valuation of financial
derivatives. Financial Engineering will cover the following topics:
Topic 1
Theories of financial market behaviour
• Overview of rational expectations and rational choice theories.
• Introduction to behavioural science and the application in financial markets.
Topic 2
Measures of investment risk
• Introduction to measures of investment risk, including variance, Value at Risk (VaR) and tail VaR.
• Application of measures to compare investment opportunities and relation to utility.
Topic 3
Stochastic interest rate models
Topic 4
Asset valuations
• Introduction to mean-variance portfolio theory.
• Introduction to and application of asset pricing models.
• Overview of single and multifactor models.
Topic 5
Liability valuations.
• Introduction to ruin theory and corresponding distributions.
• Probability of ruin in discrete and continuous time.
• Introduction to run off triangles.
• Nominal and real chain ladders.
Assessment: A 3-hour theoretical class test (mapping to one third of the IFoA CM2 module).
Indicative reading:
• John C Hull (2018), Options Futures and Other Derivatives. 9th edition, Prentice-hall International, Inc
Bodie, Z., Kane, A., and Marcus, A., 2014, Investments, 10th edition, Global Edition, McGraw-Hill Irwin
Data Science Research Report
The aim of the Research Report is to produce an empirical piece of work of approximately 6,000 words, which is
structured like a journal article and incorporates an element of data science analysis for an actuarial science
related problem.
Indicative reading:
An introduction to statistical learning: with applications in R. Gareth James, Daniela Witten, Trevor
Hastie, Robert Tibshirani. Springer, 2014. ISBN: 9781461471370
R Programming for Actuarial Science, Peter McQuire, Alfred Kume, Wiley, ISBN: 978-1-119-75499-2
Learning Outcomes
Upon successful completion of the module, students should be able to:
• Evaluate and apply theory and practice in advanced statistics related to actuarial science
• Effectively apply statistical procedures
• Critically evaluate the appropriateness of a range of statistical tests in solving a variety of actuarial problems
• Independently interpret the output of statistical tests and explain their practical and theoretical implications
• Gain experience in the use of modelling software and be able to demonstrate their software skills.
• Describe and interpret the theories on the behaviour of financial markets.
• Discuss the advantages and disadvantages of different measures of investment risk.
• Construct, interpret and discuss the models underlying asset valuations.
• Construct, interpret and discuss the models underlying liability valuations.
• Conduct a review of the current and relevant literature of the subject area chosen for the research study
• Derive hypotheses or formulate research questions
• Use data extracted from datasets to test hypotheses or answer research questions
• Draw conclusions and identify the limitations of the study and scope for further research
Skills
Through successful completion of the module, students should be able to:
• Use advanced statistical techniques to analyse actuarial problems
• Use Monte Carlo simulation to build appropriate financial models
• Appreciate, construct and analyse statistical models applied to real world actuarial problems
• Communicate complex statistical analysis in an effective and ethical way
• Communicate using financial market terminology
• Communicate aspects of financial market risk and return to non-specialist audiences
• Demonstrate understanding of technical aspects of financial valuation models
• Demonstrate understanding of various financial derivative products
• Reflect on their own financial and mathematical strengths and weaknesses as they progress through the
module
• Work independently and in groups
• Manage their time effectively to progress through the module
• Extend their learning through independent reading
• Reflect on their own statistical strengths and weaknesses
As well as developing the following skills:
• Search and critically review relevant literature
• Creative thinking and problem solving
• Technical model development
• Report writing
• Time management
• Ability to critically read and evaluate finance and risk-related academic literature
• Cognitive Skills
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self-assessment and reflection
• Intellectual humility
• Intellectual discipline
• The ability to synthesis information/data from a variety of sources
• Preparation and communication of ideas in written form.
i. develop the students' computational skills
ii. introduce a range of numerical techniques of importance in finance
iii. familiarise students with financial models and how to implement them
Areas to be covered include:
A primer on financial instrument pricing
o Bonds, forwards, options
o Discounting
o Probability distributions
o Expectation theory
Python
o Arrays and data structures
o Programming constructs
o Functions and classes
Numerical Methods
o Root finding
o Linear Algebra
Financial Modelling
o Stochastic processes
o Interest rate models
Option Pricing
o Black Scholes Merton
o The Greeks
o Lattice Models
o Model extensions
Monte Carlo
o Monte Carlo simulation
o Variance reduction
o Markov Chains
Credit Risk
o Merton Model
Learning Outcomes
Upon successful completion of this module, students will:
1. Describe and discuss the modelling frameworks used to value financial instruments.
2. Understand the salient features of prominent derivatives contracts.
3. Translate financial problems into mathematical models with appropriate numerical solutions
4. Have experience using Python to implement financial models
5. Critically evaluate the efficacy of different approaches to derivative pricing
Skills
This module provides opportunities for the student to acquire or enhance the following skills:
• Subject-specific skills
o The ability to appreciate, construct and analyse mathematical, statistical, and financial models
o Use of coding languages to implement financial models.
• Cognitive Skills
o Problem solving
o Abstraction
o Logical reasoning
o Critical evaluation and interpretation
o Self-assessment and reflection
• Transferable Skills
o Organisation and time management
o Use computational technology
This module introduces modelling techniques with a focus on actuarial applications, building on the Statistics module. Topics will include but not be limited to:
Stochastic processes
General principles of stochastic processes, and classification into different types
Define in general terms a stochastic process and in particular a counting process
Classify a stochastic process according to whether it operates in discrete or continuous time and state space, or is of mixed type
Markov Chains
Explain what is meant by the Markov property in the context of a stochastic process and in terms of filtrations.
Define and apply a Markov chain to actuarial problems such as a no claims discount scheme.
State the essential features of a Markov chain model.
State the Chapman-Kolmogorov equations that represent a Markov chain.
Calculate the stationary distribution for a Markov chain in simple cases.
Describe a time-inhomogeneous Markov chain model and describe simple applications.
Demonstrate how Markov chains can be used as a tool for modelling and how they can be simulated.
Markov Jump Processes
Define and apply a Markov process.
State the essential features of a Markov process model.
Define a Poisson process, derive the distribution of the number of events in a given time interval, derive the distribution of inter-event times, and apply these results.
Derive the Kolmogorov equations for a Markov process with time independent and time/age dependent transition intensities.
Solve the Kolmogorov equations in simple cases.
Describe simple survival models, sickness models and marriage models in terms of Markov processes and describe other simple applications.
State the Kolmogorov equations for a model where the transition intensities depend not only on age/time, but also on the duration of stay in one or more states.
Describe sickness and marriage models in terms of duration dependent Markov processes and describe other simple applications.
Demonstrate how Markov jump processes can be used as a tool for modelling and how they can be simulated.
define and apply a Markov chain and a Markov process
Survival models
Explain the concept of survival models.
Describe the model of lifetime or failure time from age x as a random variable.
State the consistency condition between the random variable representing lifetimes from different ages.
Define the distribution and density functions of the random future lifetime, the survival function, the force of mortality or hazard rate, and derive relationships between them.
Define the actuarial symbols (_t^)p_x and (_t^)q_x and derive integral formulae for them.
State the Gompertz and Makeham laws of mortality.
Define the curtate future lifetime from age x and state its probability function.
Define the expected value and variance of the complete and curtate future lifetimes and derive expressions for them.
Describe the two-state model of a single decrement and compare its assumptions with those of the random lifetime model.
Estimating lifetime distributions
Describe estimation procedures for lifetime distributions.
Describe various ways in which lifetime data might be censored.
Describe the estimation of the empirical survival function in the absence of censoring, and what problems are introduced by censoring.
Describe the Kaplan-Meier (or product limit) estimate of the survival function in the presence of censoring, explain how it arises as a maximum likelihood estimate, compute it from typical data and estimate its variance.
Describe the Nelson-Aalen estimate of the cumulative hazard rate in the presence of censoring, explain how it arises as a maximum likelihood estimate, compute it from typical data and estimate its variance.
Describe the Cox model for proportional hazards, derive the partial likelihood estimate in the absence of ties, and state its asymptotic distribution.
Application using software
Use software to implement practical models of the above content
Indicative readings:
Course Notes and Core readings for Institute and Faculty of Actuaries Subject CS2
International Series of Actuarial Science: Actuarial Mathematics for Life Contingent Risks, David C.M. Dickson, Mary Hardy and Howard R Waters
Stochastic Processes for Insurance and Finance, Tomasz Rolski, Hanspeter Schmidli, Volker Schmidt, Jozef Teugels
Probability and random processes, Geoffrey Grimmett
Learning Outcomes
Upon successful completion of this module students should be able to:
• Differentiate between different stochastic processes.
• Define and apply a Markov chain and a Markov jump process.
• Develop and articulate solutions to simple actuarial problems using Markov chains and Markov jump processes.
• Critically evaluate the suitability of Markov chains and Markov jump processes for a variety of actuarial problems.
• Explain the concept of survival models.
• Critically evaluate the suitability of different survival models to a population.
• Apply Kaplan-Meier, Nelson-Aalen and Cox regression models to given data sets.
• Critically evaluate the censoring present in a data set.
• Critically interpret the output of Kaplan-Meier, Nelson-Aalen and Cox regression models.
• Carry out calculations on the above topics using software.
Skills
Through successful completion of this module students should:
• Appreciate, construct and analyse Markov chains and Markov processes applied to actuarial problems such as no claims discount schemes.
• Demonstrate understanding of theory and examples of survival models, and methods for estimating lifetime distributions.
• Clearly communicate the results of the Markov and survival models used.
• Use software to develop solutions to complex actuarial modelling problems.
• Develop software techniques to keep up with changing industry trends.
• Reflect on their own strengths and weaknesses in relation to the actuarial modelling techniques used in the module.
• Extend their learning through independent reading.
• Work independently and in groups.
• Manage their time to progress through the module.
Statistics is at the core of actuarial science. This module provides the necessary foundation in statistics and will
be built on in the later statistics modules.
Topics will include but not be limited to:
Random Variables and Distributions
Regression theory
Bayesian Statistics
Indicative readings:
• Acted: Course Notes and Core readings for Subject CS1
• Effective statistical learning methods for actuaries: I. [Generalised Linear Models] GLMs and extensions. -
Denuit, M., Hainaut, D. and Trufin, J. - Springer, 2019. ISBN 978-3030258207
• Generalized linear models. 2nd ed. McCullagh, P. and Nelder, J.A. Chapman & Hall/CRC Press, 1989. ISBN
0412317605 [referenced in IFoA CS1 Core Reading]
• An introduction to statistical learning: with applications in R. Gareth James, Daniela Witten, Trevor Hastie,
Robert Tibshirani. Springer, 2014. ISBN: 9781461471370
Learning Outcomes
Upon successful completion of the module, students should be able to:
• Evaluate and apply theory and practice in statistics
• Effectively apply statistical procedures
• Critically evaluate the appropriateness of a range of statistical methods in solving a variety of actuarial problems.
Skills
Through successful completion of the module, students should be able to:
• Use statistical techniques to analyse actuarial problems
• Appreciate, construct and analyse statistical models applied to real world actuarial problems
• Communicate complex statistical analysis in an effective and ethical way
• Work independently and in groups
• Manage their time effectively to progress through the module
• Extend their learning through independent reading
• Reflect on their own statistical strengths and weaknesses
This module builds on the functions and techniques introduced in Actuarial Mathematics 1, with the introduction of cash flows dependent on death, survival or other uncertainties.
Topic 1
Life Table Functions
• Introduction to survival probabilities
• Determine expressions for survival probabilities and life table functions
• Life table functions at non-integer ages
• Understand Ultimate and Select mortality.
Topic 2
Life Assurance and Annuity Functions
• Define simple assurance and annuity functions
• Determine expressions for the mean and variance of the present value for these functions, with premiums and annuities paid annually, more frequently or continuously and benefits paid at varying times and frequencies
• Derive relationships between assurance and annuity functions.
Topic 3
Variable Benefits and Mortality Profit
• Calculate expected present value of an annuity, premium or benefit on death which increases by:
o A constant compound rate
o A constant monetary amount
• Discuss with profits contracts and calculate premiums and reserves for variable benefits.
• Evaluate elements of mortality profit for a single contract and a portfolio of contracts.
Topic 4
Gross Premiums and Reserves
• Introduction to gross premiums and reserves
• Calculate gross premiums using the principle of equivalence, with premiums and annuities paid annually, more frequently or continuously and benefits paid at varying times and frequencies
• Define and evaluate prospective and retrospective gross premium reserves and state the conditions they are equal, allowing for expenses
• Introduction to net premium reserves.
Topic 5
Joint Life Functions
• Introduction to joint life and survival functions
• Calculate the expected present value of simple assurance and annuity functions allowing for death/survival of one or both lives
• Extend principles already introduced to functions contingent on order of death.
Topic 6
Modelling multiple decrements
• Introduction to multiple states
• Determine how to value cash flows using the multiple-state Markov Model
• Evaluate expected cash flows dependent on more than one decrement for various benefit types using multiple decrement tables.
Topic 7
Discounting emerging costs, for use in pricing, reserving and assessing profitability
• Introduction to unit-linked contracts
• Evaluate expected cash flows for various contact types
• Profit test simple annual premium contracts and appreciate how the profit test may be used to price a product
• Demonstrate how the profit test may be used to determine reserves.
Topic 8
Mortality and selection
• Factors that contribute to variations in mortality and morbidity
• Types of selection and selection in life insurance contracts and pension schemes.
Indicative readings:
• www.ons.gov.uk/ons/dcp171778_345078.pdf
• https://www.theactuary.com/features/2018/10/2018/10/09/interpreting-mortality-trends
• https://www.actuaries.org.uk/learn-and-develop/continuous-mortality-investigation/cmi-working-papers/mortality-projections/cmi-working-paper-129/mortality-improvements-and-cmi2019-frequently-asked-questions-faqs
• Actuarial mathematics. 2nd ed. Bowers, N. L.; Gerber, H. U.; Hickman, J. C. et al. Society of Actuaries, 1997
• https://www.thepensionsregulator.gov.uk/en/trustees/managing-db-benefits/funding/valuing-your-scheme
• https://www.thepensionsregulator.gov.uk/en/document-library/codes-of-practice/code-3-funding-defined-benefits-
Learning Outcomes
Upon successful completion of this module, students should be able to:
• Evaluate and apply the theory and application of mathematics to actuarial problems:
o Understand further actuarial functions allowing for decrements used and the mathematical techniques employed by an actuary
o Demonstrate the relationship between simple annuity and assurance functions
o Solve equations of value to determine premium levels or reserves.
• Critically evaluate and price annuity products using profit test.
• Evaluate and apply theories of mortality and selection to insurance products and pension schemes.
• Interpret the output of an actuarial valuation model, and communicate the results.
Skills
Through successful completion of this module, students should:
• Use mathematical and demographic techniques to analyse actuarial problems.
• Appreciate, construct and analyse mathematical models of practical situations:
o Uncertainty in cash flows via decrements
o Life assurance and annuity functions and the impact of decrements on these functions
o Gross premiums and reserves
o Profit test various types of contacts.
• Connect business problems with actuarial practice through pricing insurance products and valuing pension benefits.
• Understand the wider implications of actuarial mortality modelling, e.g. through CMI resources.
• Use MS Excel to evaluate and develop solutions for the actuarial valuation of a pension scheme or insurance product.
• Communicate the method and results of an actuarial valuation.
• Demonstrate use of MS Excel to an industry standard.
• Extend their learning through independent reading.
• Reflect on their own strengths and weaknesses in actuarial mathematics.
• Work independently and in groups.
• Manage their time to progress through the module.
Course Description:
The purpose of this course is to analyse how corporations make major financial decisions. The theory of corporate behaviour is discussed and the relevance of each theoretical model is examined by an empirical analysis of actual corporate decision making.
Course Aim:
The aims of this module are to:
(i) familiarize students with the issues confronting corporations when making investment and financing decisions;
(ii) develop the ability of students to obtain corporate information from the Bloomberg database.
Course Coverage:
• Corporate Governance
• Investment Appraisal
• Dividend Policy
• Capital Structure
• Initial Public Offerings
• Mergers and Acquisitions
Learning Outcomes
Upon successful completion of this module, students will be able to:
• describe and synthesize academic theories which explain the approaches of corporations to investment and financing decisions;
• analyse how corporations can increase shareholder value;
• evaluate empirical evidence regarding whether corporate decision making is consistent with academic theories;
• apply theoretical principles to hypothetical situations;
• use the Bloomberg database in a trading-room environment.
Skills
This course provides opportunities for the student to acquire or enhance the following skills:
Subject-specific Skills
• The ability to construct arguments and exercise problem solving skills in the context of theories of finance and risk management
• The ability to use computer-based mathematical / statistical / econometric packages to analyse and evaluate relevant data
• The ability to read and evaluate finance and risk-related academic literature
• The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations
Cognitive Skills
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self assessment and reflection
Transferable Skills
• The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet
• The preparation and communication of ideas in finance, information economics and risk management in both written and presentational forms
• The ability to work both independently and in groups
• Organisation and time management
• Problem solving and critical analysis
• Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
• The ability to communicate quantitative and qualitative information together with analysis, argument and commentary in a form appropriate to different intended audiences.
This module provides an introduction to actuarial mathematics and the application of compound interest and simple annuity functions.
Topic 1
Actuarial Modelling and Data Analysis
• Overview of the use of models
• Distinction between deterministic and stochastic models
• Aims of data analysis and overview of the data analysis process.
Topic 2
Cashflow Models
• Introduction to cash flow models and
• Using cash flow models to describe financial instruments.
Topic 3
Time Value of Money
• Introduction to simple and compound interest
• Accumulate and discount a single investment at a constant rate of interest using simple and compound interest.
Topic 4
Interest Rates
• Nominal and effective interest and discount rates
• The relationships between the rate of interest payable once per effective period, payable more frequently and the force of interest
• The force of interest as a function of time
• Real and money interest rates.
Topic 5
Valuing Cashflows and Annuities
• Discounting and accumulating cash flows
• Introduction to annuities
• Derive formulae for level annuities payable in advance, arrears, paid annually, more frequently and continuously
• Derive similar formulae for annuities when the first payment is deferred
• Derive similar formulae for annuities that increase at a constant rate
• Derive similar formulae for compound increasing annuities.
Topic 6
Equations of Value
• Solving standard equations of value
• Solving equations of value when payments are uncertain.
Topic 7
Loan Schedules
• Identify capital and interest components of a loan repayment schedule
• Identifying the capital outstanding at any one time
• Flat rates and annual effective rates.
Topic 8
Project Appraisal
• Net present value and accumulated profit of cash flows from an investment project at a given rate of interest
• Internal rate of return implied by cash flows from an investment project
• Payback period and discounted payback period and their respective terms implied by cash flows from an investment project.
Topic 9
Investments
• Characteristics of assets classes
• Calculate the present value of payments from various assets and determine their respective yields.
Topic 10
Term structure of Interest Rates
• Evaluate discrete and continuous spot and forward rates
• Theories of the term structure of interest rates
• Evaluate duration, convexity and the conditions for Redington’s immunisation.
Indicative readings:
• Actuarial mathematics. 2nd ed. Bowers, N. L.; Gerber, H. U.; Hickman, J. C. et al. Society of Actuaries, 1997
• https://www.thepensionsregulator.gov.uk/en/trustees/managing-db-benefits/funding/valuing-your-scheme
• https://www.thepensionsregulator.gov.uk/en/document-library/codes-of-practice/code-3-funding-defined-benefits-
• https://www.thepensionsregulator.gov.uk/en/document-library/regulatory-guidance/integrated-risk-management
Learning Outcomes
Upon successful completion of this module students will be able to:
• Understand simple actuarial functions used and the mathematical techniques employed by an actuary and apply to actuarial problems
• Interpret interest rates and convert annual interest rates into continuous rates and rates of other compound frequencies
• Evaluate the present value of cash flows and/or the yield for various financial instruments.
• Critically evaluate whether a project should proceed using the internal rate of return, payback period and discounted payback period
• Understand and interpret the characteristics of different asset classes
• Develop models using the above techniques in MS Excel, applied to actuarial problems
• Critically evaluate and interpret output of models in MS Excel
Skills
Through successful completion of this module students should:
• Communicate using actuarial terminology
• Communicate complex mathematical techniques to non-specialist audiences
• Demonstrate understanding of compound interest and how to apply it to annuity functions to solve actuarial problems
• Analyse cash flows and develop solutions to technical problems using actuarial methods
• Appreciate, construct and analyse mathematical models of practical actuarial problems
• Use software to develop and critically evaluate more complex models, using MS Excel in line with industry standards
• Connect business problems with actuarial practice through the study of asset classes and project appraisal
• Extend their learning through independent reading
• Reflect on their own mathematical strengths and weaknesses as they progress through the module
• Work independently and in groups
• Manage their time to progress through the module
This module provides an introduction to the fundamental principles and concepts of
microeconomics and macroeconomics.
Economics is fundamental to understanding the choices of consumers, businesses, and
governments. This module provides an MSc-level introduction to the fundamental principles and
concepts of economics to Actuarial Sciences students.
The course covers microeconomics, focusing on consumer and firm behaviour modelling for
market understanding. It also covers macroeconomics, which models aggregated economic
activity to understand societal outcomes better.
The course explores consumer, firm, and market behaviour, focusing on key concepts like utility,
profit, and price. Utility drives consumer choices, profit guides firm decisions, and prices are set
through the interaction of consumers and firms in the market. The course examines various
market structures and discusses the consequences of market power and failure, highlighting
situations where government intervention may be necessary to improve outcomes. Through the
application of macroeconomic principles, students gain insight into the functioning of the
economic system, identify its shortcomings, and assess the effects of economic agents'
decisions on the ability of society to generate and distribute wealth.
The emphasis of the course is to provide students with an economic toolkit for assessing real world scenarios throughout a career in Actuarial Sciences.
Learning Outcomes
To provide students with an understanding of economic principles and concepts.
Skills
Articulate the fundamental concepts of microeconomics that explain how economic
agents make decisions.
Apply the principles underlying macroeconomics to explain how the economic system
works, where it fails and critically interpret how decisions taken by economic agents
affect the economic system
This module builds on the first semester Actuarial Statistics module, and develops sophisticated techniques with a focus on actuarial applications, with particular reference to the insurance industry. The module uses R Studio to apply the techniques taught in a practical manner.
Topics will include but not be limited to:
Actuarial Modelling
• The Actuarial Control Cycle and its application to General Insurance
Fundamentals of General Insurance
• Introduction to the basic short-term contracts, and the operation of simple forms of proportional and excess of loss reinsurance
Loss distributions
• Deriving moments and moment generating functions (where defined) of loss distributions and aggregate claim distributions.
• Deriving the distribution and moments of claim amounts paid by the insurer and reinsurer in the presence of excesses (deductibles) and reinsurance, and derive the moments of compound distributions after the operation of simple forms of proportional and excess of loss reinsurance.
• Describing a copula as a multivariate distribution, and using tail dependence to select a copula for modelling particular types of risk/
• Recognising extreme value distributions, and calculating measures of tail weight.
Time series
• Introduction to time series.
• Autoregressive, moving average, autoregressive moving average and autoregressive integrated moving average time series.
Indicative readings:
• Introductory statistics with applications in general insurance. Hossack, I. B.; Pollard, J. H.; Zehnwirth, B. 2nd ed. Cambridge University Press, 1999. 282 pages. ISBN: 052165534X
• Pricing in General Insurace. Parodi, P. CRC Press, Taylor & Francis Group, 2015. ISBN: 9781466581449
• Acted: Course Notes and Core readings for Subject CS2,
• An introduction to statistical modelling. Dobson, A. J. Chapman & Hall, 1983. 125 pages. ISBN: 0412248603
• Loss models: from data to decisions. Klugman, S. A.; Panjer, H. H.; Willmot, G. E. et al. John Wiley, 1998. 644 pages. ISBN: 0471238848
• Practical risk theory for actuaries. Daykin, C. D.; Pentikainen, T.; Pesonen, M. Chapman & Hall, 1994. 545 pages. ISBN: 0412428504
Learning Outcomes
The aims of this module are to build on the mathematical and statistical techniques learned in the Statistics module, and apply these to basic general insurance problems faced by actuaries. Specifically, on successful completion of this module a student will be able to:
• Calculate probabilities and moments of loss distributions both with and without limits and risk-sharing arrangements
• Develop risk models involving frequency and severity distributions
• Recognise extreme value distributions
• Critically evaluate and interpret statistical models using loss distributions
• Understand the concepts underlying time series models
• Critically evaluate the suitability of time series models for actuarial problems and real world financial data, and apply them
• Interpret the output from time series models of actuarial problems and real world financial data
• Use R to develop and interpret risk models of insurance products
• Use R to critically evaluate and analyse financial time series data
Skills
Through successful completion of this module, students should:
• Use loss distributions, compound distributions and risk models for problem solving in a general insurance setting
• Appreciate, construct and analyse time series models of actuarial problems and real world financial data
• Use R to analyse and evaluate financial time series data
• Effectively communicate the results of their statistical modelling
• Reflect on their own strengths and weakness in relation to statistical analysis
• Extend their learning through independent reading
• Work independently and in groups
• Manage their time to progress through the module
Normally a strong 2.2 Honours degree (with minimum of 55%) or equivalent qualification acceptable to the University in a highly quantitative discipline such as Mathematics, Statistics, Finance, Computer Science, Actuarial Science or Economics. This MSc requires prior mathematical knowledge therefore a strong performance in quantitative modules with mathematical content is required. Applicants must normally have achieved 2:1 standard or above in relevant modules such as Calculus; Linear Algebra; Differential equations; Probability; Statistics; Econometrics.
Applicants are advised to apply as early as possible. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal. Notifications to this effect will appear on the Direct Application Portal against the programme application page.
Please note: international applicants will be required to pay a deposit to secure a place on this course.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
Evidence of an IELTS* score of 6.5, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required.
*Taken within the last 2 years
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
Actuaries are constantly in demand and their skills are continually included in highly skilled occupation listings and for skills in demand listings. The Bureau of Labour Statistics project that employment of actuaries is expected to increase by 20% between 2018 and 2028.
Actuaries primarily work in insurance and financial services, which are heavily regulated and require a number of statutory disclosures. Graduates from the Actuarial Science programme obtain employment across the world, but particularly in Dublin and London. Dublin has seen an influx of financial services companies either entering the market for the first time or increasing their footprint in Ireland, as a result of the United Kingdom’s withdrawal from the European Union. Another primary driver for the demand for actuaries has been changes in legislation. A recent example is the implementation of Solvency II for the insurance industry, which caused a spike in the demand for actuaries and enabled actuaries to develop new transferable skills. Furthermore, the International Accounting Standards Board has published the new accounting standard for insurance contracts, IFRS 17. This standard was implemented in 2023 and there is a healthy demand for actuaries to help insurance companies embed the standard.
Finally, with the growth in data science and Insurtech, new, more technology based opportunities are available for actuaries. Rather than automation being a threat for actuaries, these new tools will enable actuaries to help their clients make better decisions, which contribute to the positive outlook for actuaries in the long term.
Actuaries are renowned for having excellent opportunities for strong salary progression, with many actuaries earning six figure incomes. Sample salaries for many different actuarial roles can be viewed here: https://proactuary.com/resources/salary
Graduate Plus/Future Ready Award for extra-curricular skills
In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.
1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.
2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
Terms and Conditions for Postgraduate applications:
1.1 Due to high demand, there is a deadline for applications.
1.2 International applicants will be required to pay a deposit to secure their place on the course. The current mandatory tuition fee deposit payment is £1000 International (Non- EU & EU except ROI).
1.3 This condition of offer is in addition to any academic or English language requirements.
Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.
Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.
Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.
If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.
Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.
There are also additional charges for graduation ceremonies, examination resits and library fines.
How do I fund my study?
The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.
A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.
