Course Structure

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

Semester 3

Applied Research Project

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.

Facilities

Students have access to Bloomberg software, a market leader in financial news, data and analytics, which is used by many financial institutions. The Trading Room allows for an interactive and exciting learning environment which, brings textbook theory to life.
https://www.qub.ac.uk/schools/queens-business-school/student-opportunities/fintru-trading-room/

Modules

• Year 1

  Core Modules

  Applied Research Project (60 credits)

  ×

  Applied Research Project

  Overview

  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.

  Coursework

  50%

  Examination

  50%

  Practical

  0%

  Credits

  60

  Module Code

  FIN7041

  Teaching Period

  Summer

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Financial Modelling in Python (15 credits)

  ×

  Financial Modelling in Python

  Overview

  The aims of this module are to:
  
  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

  Coursework

  30%

  Examination

  70%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7029

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Survival Modelling (15 credits)

  ×

  Survival Modelling

  Overview

  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.

  Coursework

  0%

  Examination

  70%

  Practical

  30%

  Credits

  15

  Module Code

  FIN7040

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Statistical Analysis for Actuarial Applications (15 credits)

  ×

  Statistical Analysis for Actuarial Applications

  Overview

  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

  Coursework

  0%

  Examination

  100%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7036

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Actuarial Mathematics and Contingencies (15 credits)

  ×

  Actuarial Mathematics and Contingencies

  Overview

  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.

  Coursework

  30%

  Examination

  70%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7038

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Corporate Finance (15 credits)

  ×

  Corporate Finance

  Overview

  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.

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN9005

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Financial Mathematics (15 credits)

  ×

  Financial Mathematics

  Overview

  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

  Coursework

  0%

  Examination

  70%

  Practical

  30%

  Credits

  15

  Module Code

  FIN7035

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Economic Thinking for Actuaries (15 credits)

  ×

  Economic Thinking for Actuaries

  Overview

  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

  Coursework

  30%

  Examination

  70%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7037

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Statistics for Insurance (15 credits)

  ×

  Statistics for Insurance

  Overview

  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

  Coursework

  0%

  Examination

  70%

  Practical

  30%

  Credits

  15

  Module Code

  FIN7039

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

Career Prospects

Employment after the Course

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.

Tuition Fees

Northern Ireland (NI) 1	£8,800
Republic of Ireland (ROI) 2	£8,800
England, Scotland or Wales (GB) 1	£9,250
EU Other 3	£25,800 (£6,000 discount, see T&Cs link below)
International	£25,800 (£6,000 discount, see T&Cs link below)

£6,000 Scholarship available for 2025 entry. Click this link to view the Terms and Conditions.

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.

More information on postgraduate tuition fees.

Additional course costs

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.

Read the full terms and conditions at the link below:
https://www.qub.ac.uk/Study/postgraduate/tuition-fees/deposit-refunds-policy/

All Students

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 to Apply

Apply using our online Queen's Portal and follow the step-by-step instructions on how to apply.

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Terms and Conditions

The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study.
Queen's University Belfast Terms and Conditions.

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