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Actuarial Science BSc(Hons)

Attendance UCAS code/apply Year of entry
3 years full time N324 2017
4 years full time including sandwich year N323 2017
6 years part time Apply direct to the University 2017

Why choose this course?

This specialist course is designed for students who are already committed to a career in applying mathematical and statistical techniques in the financial world. It is aimed at high-achievers who are looking for professional entry into actuarial careers.

Accreditation

This course is accredited by the Institute of Mathematics and its Applications (IMA).

What you will study

The curriculum draws together a variety of subject areas to enable you to model real-world effects and their financial implications. You will explore a blend of applied mathematics and statistics, with appropriate computing support, and will cover background material in finance and accounting. The programme structure also offers flexibility – you may transfer to related courses at the end of Year 1.

Year 1 will provide a foundation for the rest of the course by equipping you with a broad understanding across a range of areas. You will study mathematical methods and fundamental statistical and computing concepts, which you will apply to the evaluation of financial risks later in the course. You will also be introduced to the study of financial mathematics.

Year 2 moves on to topics in actuarial science, as well as mathematical topics that underlie more realistic quantitative modelling. You will study actuarial modelling and models involving lifetime distributions, financial systems and mathematical problems involving several variables, together with differential equations that represent how quantities change. The predictive power of statistics will become evident, and the statistical module will further prepare you for final-year modules.

On successful completion of Year 2, you may transfer to our flexible Actuarial Mathematics & Statistics BSc(Hons) course. You will also have an opportunity to undertake a professional placement year to develop your skills in a real work setting.Year 3 allows you to develop an in-depth knowledge of actuarial science. Your studies will combine advanced topics from the fields of mathematics, statistics and actuarial science, and will prepare you for entry into the professional field. You will study statistical techniques of particular relevance to financial work, financial modelling of markets, investment and risk, as well as mathematical techniques that can be used to value cash flows dependent on death, survival or other risks.

Module listing

Please note that this is an indicative list of modules and is not intended as a definitive list. Those listed here may also be a mixture of core and optional modules.

Year 1

  • This module provides the foundations for further study of (applicable) mathematics. The basic ideas of mathematics as a discipline are introduced. Topics from different areas of mathematics which may readily be applied to solve problems in the real world are considered with emphasis on study of the Calculus, one of the most powerful tools of modern mathematics and theoretical science. As a necessary preliminary to this work we first clarify our ideas of rational, real and complex numbers. The fundamental concepts of calculus, in particular, that of a limit, are introduced and the continuity and differentiability of functions on the real line are explored. The derivative concept is generalised for functions of several variables extending the breadth of its application greatly and the study of ordinary differential equations is commenced.

    On successful completion of the module, you will be able to:

    • Discuss the idea of systems constructed on axiomatic foundations and the use of definitions, theorems and proof in mathematics.
    • Explain the evaluation of derivatives and integrals of functions as limiting processes, and perform the evaluation for simple examples.
    • Apply the techniques of calculus in a range of appropriate situations.
    • Formulate and solve mathematical models based on simple ordinary differential equations (ODEs).
    • Perform calculations with sets of vectors to determine their theoretical properties and to solve simple geometrical problems in three dimensions.
    • Communicate simple mathematical ideas and arguments in written form – where appropriate incorporating material from a variety of information sources.

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  • This module is a core part of most mathematics courses and builds upon A-level study in three strands: it aims to develop s personal skills and understanding of degree-level study; it introduces computer programming and software as a useful problem-solving tool in mathematics; and it develops mathematical techniques in a computing context that will be used in parallel and subsequent modules.

    On successful completion of the module, you will be able to:

    • Work in groups to solve problems and present their work effectively.
    • Describe the relationship between the role of professional societies, their codes of practice and ethical (professional) behaviour.
    • Use computer packages for symbolic algebra and linear algebra.
    • Use a modern programming environment to assist in the solution of computational problems in mathematics.
    • Integrate a function numerically, showing awareness of the concepts of convergence and errors in arithmetic processes.
    • Use matrices to represent, analyse and solve simple systems of equations.

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  • This module introduces basic probability and statistical theory, concepts and their applications to real life problem solving and learn about different types of data and how to present and summarise these. The module also covers statistical inference and the concepts of confidence intervals and hypothesis testing for the population mean and variance, for proportions, for comparing measures between two populations and for contingency tables and goodness of fit to a known distribution.

    On successful completion of the module, you will be able to:

    • Summarise the main features of a dataset by using appropriate statistical measures and diagrams.
    • Apply the concepts of probability, random variables, probability distributions and random sampling; and select probabilistic models appropriate to problems described in words.
    • Construct confidence intervals and conduct hypotheses tests for unknown parameters in well-defined circumstances and interpret the results.
    • Investigate the linear relationship between variables using correlation and regression analysis.
    • Use appropriate software for basic statistical analysis and presentation of data.

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  • This module is designed to build on the work previously gained in order to deliver more advanced tools in calculus and numerical methods thus permitting the solution of a much wider set of problems associated with the real world. In turn, concepts developed in this module are used extensively at Level 6.

    On successful completion of the module, you will be able to:

    • evaluate multiple integrals, in different co-ordinate systems.
    • perform vector algebra and calculus, including evaluations of gradient, divergence and curl  and applications of (integral) theorems linking these quantities.
    • solve a variety of ordinary differential equations (ODEs) including linear systems of ODEs.
    • solve systems of linear and nonlinear equations numerically.
    • obtain eigenvalues numerically.
    • understand and apply methods of approximation using truncated series or splines.

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Year 2

  • This module is designed to build on the work previously gained in order to deliver more advanced tools in calculus and numerical methods thus permitting the solution of a much wider set of problems associated with the real world. In turn, concepts developed in this module are used extensively at Level 6.

    On successful completion of the module, you will be able to:

    • Evaluate multiple integrals, in different co-ordinate systems.
    • Perform vector algebra and calculus, including evaluations of gradient, divergence and curl  and applications of (integral) theorems linking these quantities.
    • Solve a variety of ordinary differential equations (ODEs) including linear systems of ODEs.
    • Solve systems of linear and nonlinear equations numerically.
    • Obtain eigenvalues numerically.
    • Understand and apply methods of approximation using truncated series or splines.

    Read full module description

     
  • This module develops and builds on the concepts of probability and statistical modelling studied at the previous level. The module introduces some of the major discrete and continuous statistical distributions which underpin statistical methodology and the concepts of joint distributions. The module also deals with statistical modelling and how to take data analysis beyond basic techniques. The theory and practical application involved in investigating multivariate data using statistical modelling from initial investigation through to validation of a model is investigated. Example driven practice in using industry standard statistical software for the purpose of statistical modelling and how to communicate the results of their analyses effectively and coherently will be reviewed. This module provides a sound grounding in theoretical and practical statistical analysis and forms the basis for learning more advanced multivariate methodologies later in the program. It also covers much of the material required to satisfy the IFA CT3 criteria.

    On successful completion of the module, you will be able to:

    • Distinguish between discrete and continuous random variables, calculate probabilities and moments for discrete and continuous random variables and median and mode for continuous random variables.
    • Derive moments and generating functions for discrete and continuous variables and use generating functions to derive moments and the distribution of the sum of independent random variables.
    • Derive marginal and conditional distributions from joint distributions, and distributions of functions of random variables.
    • Derive the maximum likelihood and method of moment estimators and estimates of parameters of univariate probability distributions.
    • Use regression modelling to investigate multivariate data, obtain the model of best fit and test the validity of the model.
    • Use statistical software to construct, analyse and fit regression models, interpret the output and communicate the results.

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  • The module provides a grounding in stochastic processes and survival models and their practical applications. The module builds on the probability and statistics modules by introducing a time varying element. The concepts and purpose of stochastic processes, Markov chains and Markov processes are introduced. The module goes on to define and apply survival models and estimation procedures for lifetime distributions.

    On successful completion of the module, you will be able to:

    • Describe the principles of actuarial modelling and the benefits and possible pitfalls of stochastic modelling.
    • Define and apply a Markov chain model and a Markov process model.
    • Derive and solve the Kolmogorov equations for a Markov process with time independent and time/age dependent transition intensities.
    • Formulate the model of lifetime or failure time as a random variable and calculate survival probabilities and rates of mortality.
    • Estimate mortality rates and hazard rates based on a range of modelling assumptions.
    • Test mortality experience data for consistency with a standard mortality experience or a set of graduated mortality estimates.

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  • This module covers the syllabus of the Institute and Faculty of Actuaries' Subject CT2 – 'Finance and Financial Reporting'. The module deals with the basic understanding of corporate finance including knowledge of the instruments used by companies to raise finance and manage financial risk.  In addition, the module includes the ability to interpret the accounts and financial statement of companies and financial institutions.

    On successful completion of the module, you will be able to:

    • Describe the major types of financial institutions operating in the financial markets.
    • Demonstrate a knowledge and understanding of the principal terms in use in investment and asset management and the key principles of finance.
    • Explain why companies are required to produce annual reports and accounts and the fundamental accounting concepts which should be adopted in the drawing up of company accounts.
    • Compare and contrast different types of business entities such as sole trader, partnership, limited liability partnership, and limited company and alternative methods of financing.
    • Construct and interpret simple balance sheets, income statements and cash flow statements including those of insurance company accounts, and describe the basic principles of personal and corporate taxation.
    • Demonstrate knowledge and understanding of the characteristics and use of financial instruments, the factors that affect the capital structure and dividend policy of a company, including determining a company's investment projects using the cost of capital.

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Optional sandwich year

Year 3/4

  • This module provides a grounding in mathematical techniques which can be used for pricing and valuing life insurance and pension products, with examples drawn from current professional practice. Mathematical techniques used to model and value cash flows which depend on death, survival or other uncertain risks are explained. The module goes on to define simple assurance and annuity contracts and develop practical methods of evaluating their expected values and variances. The module covers the material required for Subject CT5 of The Institute and Faculty of Actuaries.

    On successful completion of the module, you will be able to:

    • Give examples of simple assurance and annuity contracts and evaluate expected values and variances of present values of the contracts.
    • Explain and derive premiums and policy values for simple life insurance contracts and apply a profit test to a basic life insurance product.
    • Compute gross premiums and reserves of assurance and annuity contracts and apply a profit test to the product.
    • Compute probabilities of death and survival relating to two lives and evaluate expected values and variances of contracts involving two lives.
    • Show how the value of a cash flow contingent upon more than one risk may be evaluated using a multiple-state Markov Model.
    • Identify the factors which affect mortality and morbidity experience.

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  • This module consolidates and further develops the concepts previously acquired; consisting of two distinct but interrelated parts. The PDE part builds on analytical and numerical methods for solving ODEs whilst in the optimisation section the ideas of using calculus to find stationary points of functions (of one or two variables), introduced in earlier modules are generalised and extended to cases where the functions are constrained (by both equations and inequalities). An holistic approach covering both analytical and (approximate) numerical techniques is adopted throughout. This means that a wide range of PDEs covering many areas of application may be solved – and similarly a variety of calculus-based methods for finding optima is considered and their appropriateness for different situations discussed in the context of recent research in the area.

    On successful completion of the module, you will be able to:

    • Find the characteristics and classify a partial differential equation.
    • Use Fourier method of separation of variables to solve a partial differential equation.
    • Use finite difference methods for solving PDEs and understand limitations of numerical methods.
    • Find analytically the extrema of functions of two or more variables, with and without constraints.
    • Apply appropriate numerical methods to solve unconstrained and constrained optimisation problems.
    • Apply the above theory to deduce optimal strategies in a range of application areas.

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  • The aim of the module is to provide further grounding in statistical techniques of particular relevance to insurance work. The module explains how insurance and reinsurance operates and how insurance products and services are priced by considering appropriate risk measures. The main theoretical concepts of statistical inference and probability theory that were acquired in previous modules are extended and practical applications in insurance are shown. The module encompasses loss distributions, risk models, ruin theory, decision theory, Bayesian estimation and credibility theory, generalised linear models, time series models and principles of Monte Carlo simulation. The module covers the material required for Subject CT6 of The Institute and Faculty of Actuaries.

    On successful completion of the module, you will be able to:

    • Select suitable loss distributions for sets of claims to calculate probabilities and moments of loss distributions and reinsurance arrangements and construct and analyse risk models involving frequency and severity distributions.
    • Apply the concept of ruin for a risk model, including the fundamental results on the adjustment coefficient and Lundberg's inequality for evaluating ruin probabilities.
    • Calculate Bayesian estimators and derive credibility premiums in simple cases.
    • Analyse a delay (or run-off) triangle of claims for projecting the ultimate position.
    • Understand generalised linear models and their applications.
    • Analyse time series and select appropriate forecasting techniques for them.

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  • This module serves as an introduction to the mathematics and statistics of modern portfolio theory, the mathematical, stochastic and statistical models of risky assets and the theory of pricing contracts based on these assets. It is intended to cover the requirements of CT8 from the Institute and Faculty of Actuaries.

    On successful completion of the module, you will be able to:

    • Analyse the return on an asset or portfolio – as well as the mean and variance of return – and compare investment opportunities using a variety of measures of risk.
    • Identify a portfolio of assets which is optimal given a set of selection criteria.
    • Describe various models of asset returns, including the capital asset pricing model (CAPM) and perform calculations using these models.
    • Discuss the various forms of the efficient market hypothesis and their limitations.
    • Formulate models of the securities markets using discrete and continuous-time stochastic processes and partial differential equations.
    • Use and extend these models to obtain the value of options and other contingent claims on assets.

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You will have the opportunity to study a foreign language, free of charge, during your time at the University on a not-for-credit basis as part of the Kingston Language Scheme. Options currently include: Arabic, French, German, Italian, Japanese, Mandarin, Portuguese, Russian and Spanish.

Study abroad as part if your degreeMost of our undergraduate courses support studying or working abroad through the University's Study Abroad or Erasmus programme.

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Key information set

The scrolling banner(s) below display some key factual data about this course (including different course combinations or delivery modes of this course where relevant).

We aim to ensure that all courses and modules advertised are delivered. However in some cases courses and modules may not be offered. For more information about why, and when you can expect to be notified, read our Changes to Academic Provision.

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Location

This course is taught at Penrhyn Road

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Contact us

Admissions team

Location

This course is taught at Penrhyn Road

View Penrhyn Road on our Google Maps
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