If you’d like a high-paying career in finance or insurance… If you’re good at mathematics, enjoy problem-solving and are interested in financial matters… you should consider studying actuarial science.
Actuaries evaluate and manage financial risks, particularly in the financial services industry. Our specialist programmes are taught by professionally qualified actuaries and internationally renowned statisticians to ensure you are fully prepared for your career.
Our Year in Industry programme enables you to gain paid industry experience in addition to being taught by our professionally qualified actuaries and internationally-renowned statisticians to ensure you are fully prepared for your future career.
You’ll immerse yourself in the mathematics, statistics, finance and economics that you need to qualify as an actuary. You’ll also develop skills that are highly valued by employers, including data science, coding in R and PROPHET, financial modelling and commercial awareness.
If you do not have the qualifications for direct entry to this programme, you may be able to take a four-year degree with a foundation year. For more details see Actuarial Science with a Foundation Year.
My work ethic has greatly improved. The year gave me an idea of what I want to focus on when I graduate.
Pako Lekoko - Actuarial Science with a Year in Industry
The University will consider applications from students offering a wide range of qualifications. All applications are assessed on an individual basis but some of our typical requirements are listed below. Students offering qualifications not listed are welcome to contact our Admissions Team for further advice. Please also see our general entry requirements.
AAB including Maths at A but excluding Use of Maths.
If taking both A level Mathematics and A level Further Mathematics:
ABB including Maths at A and Further Maths at B but excluding Use of Maths.
The University welcomes applications from Access to Higher Education Diploma candidates for consideration. A typical offer may require you to obtain a proportion of Level 3 credits in relevant subjects at merit grade or above.
The University will consider applicants holding BTEC National Diploma and Extended National Diploma Qualifications (QCF; NQF; OCR) on a case-by-case basis. Please contact us for further advice on your individual circumstances.
30 points overall or 15 points at HL including Mathematics or Mathematics: Analysis and Approaches 6 at HL
N/A
The University will consider applicants holding T level qualifications in subjects closely aligned to the course.
If you are an international student, visit our International Student website for further information about entry requirements for your country, including details of the International Foundation Programmes. Please note that international fee-paying students who require a Student visa cannot undertake a part-time programme due to visa restrictions.
Please note that meeting the typical offer/minimum requirement does not guarantee that you will receive an offer.
Please see our English language entry requirements web page.
Please note that if you do not meet our English language requirements, we offer a number of 'pre-sessional' courses in English for Academic Purposes. You attend these courses before starting your degree programme.
Duration: 4 years
The following modules are indicative of those offered on this programme. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.
On most programmes, you study a combination of compulsory and optional modules. You may also be able to take ‘elective’ modules from other programmes so you can customise your programme and explore other subjects that interest you.
The aim of this module is to introduce students to core economic principles and how these could be used in a business environment to understand economic behaviour and aid decision making, and to provide a coherent coverage of economic concepts and principles. Indicative topics covered by the module include the working of competitive markets, market price and output determination, decisions made by consumers on allocating their budget and by producers on price and output, and different types of market structures and the implication of each for social welfare, the working of the economic system, governments' macroeconomic objectives, unemployment, inflation, economic growth, international trade and financial systems and financial crises.
This module will cover a number of syllabus items set out in Subject CB2 – Business Economics published by the Institute and Faculty of Actuaries.
The aim of this module is to provide a grounding in the principles of modelling as applied to financial mathematics – focusing particularly on deterministic models which can be used to model and value known cashflows. Indicative topics covered by the module include data and basics of modelling, theory of interest rates, equation of value and its applications. This module will cover a number of syllabus items set out in Subject CM1 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.
The aim of the module is to give students an understanding of the types of work undertaken within the actuarial profession, and a basic grounding in the core skills required by actuaries.
Indicative topics covered by the module include an overview of the actuarial profession, an introduction to Microsoft Excel, an introduction to interest rates and cash flow models. This module will cover a number of syllabus items set out in Subject CM1 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.
This module serves as an introduction to algebraic methods and linear algebra methods. These are central in modern mathematics, having found applications in many other sciences and also in our everyday life.
Indicative module content:
Basic set theory, Functions and Relations, Systems of linear equations and Gaussian elimination, Matrices and Determinants, Vector spaces and Linear Transformations, Diagonalisation, Orthogonality.
This module introduces widely-used mathematical methods for functions of a single variable. The emphasis is on the practical use of these methods; key theorems are stated but not proved at this stage.
Basic notation for sets and number systems including complex numbers (a+ib representation only). Standard functions: trig functions, polynomials, rational functions, exponentials and logarithms.
Single variable calculus: Differentiation, including product and chain rules; Fundamental Theorem of Calculus (statement only), elementary integrals, change of variables, integration by parts, differentiation of integrals with variable limits.
Curve sketching: graphs of elementary functions, maxima, minima and points of inflection, asymptotes.
Algebra of matrices and vectors; addition, multiplication, transposes, inner-products.
Row reduced echelon form, solving linear systems (homogeneous and inhomogeneous).
Inverse of a matrix.
This module introduces widely-used mathematical methods for vectors and functions of two or more variables. The emphasis is on the practical use of these methods; key theorems are stated but not proved at this stage. Tutorials and Maple worksheets will be used to support taught material.
Vectors: Cartesian coordinates; vector algebra; scalar, vector and triple products (and geometric interpretation); straight lines and planes expressed as vector equations; parametrized curves; differentiation of vector-valued functions of a scalar variable; tangent vectors; vector fields (with everyday examples)
Partial differentiation: Functions of two variables; partial differentiation (including the chain rule and change of variables); maxima, minima and saddle points; Lagrange multipliers
Integration in two dimensions: Double integrals in Cartesian coordinates; plane polar coordinates; change of variables for double integrals; line integrals; Green's theorem (statement – justification on rectangular domains only).
Introduction to Probability. Concepts of events and sample space. Set theoretic description of probability, axioms of probability, interpretations of probability (objective and subjective probability).
Theory for unstructured sample spaces. Addition law for mutually exclusive events. Conditional probability. Independence. Law of total probability. Bayes' theorem. Permutations and combinations. Inclusion-Exclusion formula.
Discrete random variables. Concept of random variable (r.v.) and their distribution. Discrete r.v.: Probability function (p.f.). (Cumulative) distribution function (c.d.f.). Mean and variance of a discrete r.v. Examples: Binomial, Poisson, Geometric.
Continuous random variables. Probability density function; mean and variance; exponential, uniform and normal distributions; normal approximations: standardisation of the normal and use of tables. Transformation of a single r.v.
Joint distributions. Discrete r.v.'s; independent random variables; expectation and its application.
Generating functions. Idea of generating functions. Probability generating functions (pgfs) and moment generating functions (mgfs). Finding moments from pgfs and mgfs. Sums of independent random variables.
Laws of Large Numbers. Weak law of large numbers. Central Limit Theorem.
Introduction to R and investigating data sets. Basic use of R (Input and manipulation of data). Graphical representations of data. Numerical summaries of data.
Sampling and sampling distributions. ?² distribution. t-distribution. F-distribution. Definition of sampling distribution. Standard error. Sampling distribution of sample mean (for arbitrary distributions) and sample variance (for normal distribution) .
Point estimation. Principles. Unbiased estimators. Bias, Likelihood estimation for samples of discrete r.v.s
Interval estimation. Concept. One-sided/two-sided confidence intervals. Examples for population mean, population variance (with normal data) and proportion.
Hypothesis testing. Concept. Type I and II errors, size, p-values and power function. One-sample test, two sample test and paired sample test. Examples for population mean and population variance for normal data. Testing hypotheses for a proportion with large n. Link between hypothesis test and confidence interval. Goodness-of-fit testing.
Association between variables. Product moment and rank correlation coefficients. Two-way contingency tables. ?² test of independence.
The aim of this module is to provide a grounding in the principles of modelling as applied to actuarial work – focusing particularly on deterministic models which can be used to model and value cashflows which are dependent on death, survival, or other uncertain risks. Indicative topics covered by the module include equations of value and its applications, single decrement models, multiple decrement and multiple life models. This module will cover a number of syllabus items set out in Subject CM1 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.
The aim of this module is to provide a basic understanding of corporate finance including a knowledge of the instruments used by companies to raise finance and manage financial risk. Indicative topics covered by the module include corporate governance and organisation, taxation, dividend policy, how corporates are financed, and evaluating projects. This module will cover a number of syllabus items set out in Subject CB1 – Business Finance published by the Institute and Faculty of Actuaries.
The aim of this module is to provide the ability to construct and interpret the accounts and financial statements of companies and financial institutions, to construct management information and to evaluate working capital.
This module will cover a number of syllabus items set out in Subject CB1 – Business Finance published by the Institute and Faculty of Actuaries.
Constructing suitable models for data is a key part of statistics. For example, we might want to model the yield of a chemical process in terms of the temperature and pressure of the process. Even if the temperature and pressure are fixed, there will be variation in the yield which motivates the use of a statistical model which includes a random component. In this module, students study linear regression models (including estimation from data and drawing of conclusions), the use of likelihood to estimate models and its application in simple stochastic models. Both theoretical and practical aspects are covered, including the use of R.
Probability: Joint distributions of two or more discrete or continuous random variables. Marginal and conditional distributions. Independence. Properties of expectation, variance, covariance and correlation. Poisson process and its application. Sums of random variables with a random number of terms.
Transformations of random variables: Various methods for obtaining the distribution of a function of a random variable —method of distribution functions, method of transformations, method of generating functions. Method of transformations for several variables. Convolutions. Approximate method for transformations.
Sampling distributions: Sampling distributions related to the Normal distribution — distribution of sample mean and sample variance; independence of sample mean and variance; the t distribution in one- and two-sample problems.
Statistical inference: Basic ideas of inference — point and interval estimation, hypothesis testing.
Point estimation: Methods of comparing estimators — bias, variance, mean square error, consistency, efficiency. Method of moments estimation. The likelihood and log-likelihood functions. Maximum likelihood estimation.
Hypothesis testing: Basic ideas of hypothesis testing — null and alternative hypotheses; simple and composite hypotheses; one and two-sided alternatives; critical regions; types of error; size and power. Neyman-Pearson lemma. Simple null hypothesis versus composite alternative. Power functions. Locally and uniformly most powerful tests.
Composite null hypotheses. The maximum likelihood ratio test.
Interval estimation: Confidence limits and intervals. Intervals related to sampling from the Normal distribution. The method of pivotal functions. Confidence intervals based on the large sample distribution of the maximum likelihood estimator – Fisher information, Cramer-Rao lower bound. Relationship with hypothesis tests. Likelihood-based intervals.
This module covers aspects of Statistics which are particularly relevant to insurance. Some topics (such as risk theory and credibility theory) have been developed specifically for actuarial use. Other areas (such as Bayesian Statistics) have been developed in other contexts but now find applications in actuarial fields. Indicative topics covered by the module include Bayesian Statistics; Loss Distributions; Reinsurance and Ruin; Credibility Theory; Risk Models; Ruin Theory; Generalised Linear Models; Extreme Value Theory. This module will cover a number of syllabus items set out in Subjects CS1 and CS2 – Actuarial Statistics published by the Institute and Faculty of Actuaries.
This module builds on MAST4014 Calculus and Differential Equations. We will cover advanced methods for solving ordinary differential equations and introduce, and learn to solve, partial differential equations. We will explore the properties of these differential equations and discuss the physical interpretation of certain equations and their solutions.
Introduction to ODEs: review of ordinary differentiation; what is an ODE; qualitative methods for ODEs; stationary points and stability.
Series methods for ODEs: solution methods for Cauchy-Euler equations; properties of power series; the Frobenius method.
Introduction to linear PDEs: review of partial differentiation; first and second order linear and partial differential equations; simple models that lead to the heat equation, Laplace's equation and the wave equation; the principle of superposition; initial and boundary conditions.
Fourier series: properties of Fourier series, derivation of coefficients, orthogonality of the Fourier basis.
Separable PDEs: The method of separation of variables; simple separable solutions of the heat equation and Laplace’s equation; examples and interpretation of solutions.
Formulation/Mathematical modelling of optimisation problems
Linear Optimisation: Graphical method, Simplex method, Phase I method, Dual problems,
Transportation problem.
Non-linear Optimisation: Unconstrained one dimensional problems, Unconstrained high dimensional problems, Constrained optimisation.
This module is designed to provide students with an introduction to the use of data analytics tools on large data sets including the analysis of text data. The module will begin by discussing the principles of text-mining and big data. The module will then discuss the techniques that can be used to explore large data sets (including pre-processing and cleaning) and the use of multivariate statistical techniques for supervised and unsupervised learning. The module will conclude by considering several data mining techniques.
Syllabus: What is "big data"? What is text mining? Exploratory data analysis for large datasets, and pre-processing and cleaning; Multivariate statistical analysis (both unsupervised, e.g. factor analysis or principle component analysis, and supervised, e.g. linear discriminant analysis); Data security; Data mining including techniques such as classification trees, neural networks, clustering, text analysis or network analysis.
You spend a year working in industry between Stages 2 and 3. We can offer help and advice in finding a placement. This greatly enhances your CV and gives you the opportunity to apply your academic skills in a practical context. It also gives you an idea of your career
options. Recent placements have included IBM, management consultancies, government departments, actuarial firms and banks.
Students spend a year (minimum 44 weeks) working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier stages of their degree course.
The work they do is entirely under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team . This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.
Participation in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on students progressing satisfactorily from Stage 2 of their studies.
Students who do not obtain a placement will be required to transfer to the appropriate course without a Year in Industry.
Students spend a year (minimum 44 weeks) doing paid work in an organisation outside the University, in an industrial, commercial, public sector, or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier stages of their degree course.
The Assessments required for this module should provide evidence of the subject specific and generic learning outcomes, and of reflection by the student on them as an independent learner.
The placement work they do is entirely under the direction of their industrial supervisor, but support is provided by the University. This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of this module.
Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided by the University. It is also dependent on students progressing satisfactorily from Stage 2 of their studies.
Students who do not obtain a placement will be required to transfer to the appropriate course without a Year in Industry.
The aim of this module is to provide a grounding in mathematical and statistical modelling techniques that are of particular relevance to survival analysis and their application to actuarial work.
Calculations in life assurance, pensions and health insurance require reliable estimates of transition intensities/survival rates. This module covers the estimation of these intensities and the graduation of these estimates so they can be used reliably by insurance companies and pension schemes. The syllabus also includes the study of various other survival models, and an introduction to machine learning. This module will cover a number of syllabus items set out in Subject CS2 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.
The aim of this module is to provide a grounding in the principles of modelling as applied to actuarial work – focusing particularly on deterministic models which can be used to model and value cashflows which are dependent on death, survival, or other uncertain risks. Indicative topics covered by the module include equations of value and its applications, single decrement models, multiple decrement and multiple life models, pricing and reserving. This module will cover a number of syllabus items set out in Subject CM1 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.
The aim of this module is to provide a grounding in the principles of modelling as applied to actuarial work – focusing particularly on stochastic asset liability models. These skills are also required to communicate with other financial professionals and to critically evaluate modern financial theories.
Indicative topics covered by the module include theories of financial market behaviour, measures of investment risk, stochastic investment return models, asset valuations, and liability valuations.
The additional 4 contact hours for level 7 students will be devoted to applications of the principles of financial economics and asset and liability modelling to complex financial instruments.
This module will cover a number of syllabus items set out in Subject CM2 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.
The aim of this module is to provide a grounding in the principles of modelling as applied to actuarial work – focusing particularly on the valuation of financial derivatives. These skills are also required to communicate with other financial professionals and to critically evaluate modern financial theories.
Indicative topics covered by the module include theories of stochastic investment return models and option theory.
This module will cover a number of syllabus items set out in Subject CM2 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.
This module is split into two parts:
1. An introduction to the practical experience of working with the financial software package, PROPHET, which is used by commercial companies worldwide for profit testing, valuation and model office work. The syllabus includes: overview of the uses and applications of PROPHET, introduction on how to use the software, setting up and performing a profit test for a product , analysing and checking the cash flow results obtained for reasonableness, using the edit facility on input files, performing sensitivity tests , creating a new product using an empty workspace by selecting the appropriate indicators and variables for that product and setting up the various input files, debugging errors in the setting up of the new product, performing a profit test for the new product and analysing the results.
2. An introduction to financial modelling techniques on spreadsheets which will focus on documenting the process of model design and communicating the model's results. The module enables students to prepare, analyse and summarise data, develop simple financial and actuarial spreadsheet models to solve financial and actuarial problems, and apply, interpret and communicate the results of such models.
The module will give students an understanding of the practical application of the techniques they learn in the BSc in Actuarial Science. It brings together skills from other modules, and ensures that students have the necessary entry-level skills and knowledge to join the actuarial profession or to embark on related careers, and also provides a platform for ongoing professional development. The syllabus is dynamic, changing regularly to reflect current practice and trends.
Introduction: Principles and examples of stochastic modelling, types of stochastic process, Markov property and Markov processes, short-term and long-run properties. Applications in various research areas.
Random walks: The simple random walk. Walk with two absorbing barriers. First–step decomposition technique. Probabilities of absorption. Duration of walk. Application of results to other simple random walks. General random walks. Applications.
Discrete time Markov chains: n–step transition probabilities. Chapman-Kolmogorov equations. Classification of states. Equilibrium and stationary distribution. Mean recurrence times. Simple estimation of transition probabilities. Time inhomogeneous chains. Elementary renewal theory. Simulations. Applications.
Continuous time Markov chains: Transition probability functions. Generator matrix. Kolmogorov forward and backward equations. Poisson process. Birth and death processes. Time inhomogeneous chains. Renewal processes. Applications.
Queues and branching processes: Properties of queues - arrivals, service time, length of the queue, waiting times, busy periods. The single-server queue and its stationary behaviour. Queues with several servers. Branching processes. Applications.
In addition, level 7 students will study more complex queuing systems and continuous-time branching processes.
This module will cover a number of syllabus items set out in Subject CS2 published by the Institute and Faculty of Actuaries. This is a dynamic syllabus, changing regularly to reflect current practice.
Stationary Time Series: Stationarity, autocovariance and autocorrelation functions, partial autocorrelation functions, ARMA processes.
ARIMA Model Building and Testing: estimation, Box-Jenkins, criteria for choosing between models, diagnostic tests for residuals of a time series after estimation.
Forecasting: Holt-Winters, Box-Jenkins, prediction bounds.
Testing for Trends and Unit Roots: Dickey-Fuller, ADF, structural change, trend-stationarity vs difference stationarity.
Seasonality and Volatility: ARCH, GARCH, ML estimation.
Multiequation Time Series Models: transfer function models, vector autoregressive moving average (VARM(p,q)) models, impulse responses.
Spectral Analysis: spectral distribution and density functions, linear filters, estimation in the frequency domain, periodogram.
Simulation: generation of pseudo-random numbers, random variate generation by the inverse transform, acceptance rejection. Normal random variate generation: design issues and sensitivity analysis.
This module will cover a number of syllabus items set out in Subject CS2 published by the Institute and Faculty of Actuaries. This is a dynamic syllabus, changing regularly to reflect current practice.
The 2022/23 annual tuition fees for this course are:
For details of when and how to pay fees and charges, please see our Student Finance Guide.
For students continuing on this programme, fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.*
The University will assess your fee status as part of the application process. If you are uncertain about your fee status you may wish to seek advice from UKCISA before applying.
Fees for Home undergraduates are £1,385.
Fees for Home undergraduates are £1,385.
Students studying abroad for less than one academic year will pay full fees according to their fee status.
Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.
Search scholarshipsKent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.
You may be eligible for government finance to help pay for the costs of studying. See the Government's student finance website.
Scholarships are available for excellence in academic performance, sport and music and are awarded on merit. For further information on the range of awards available and to make an application see our scholarships website.
At Kent we recognise, encourage and reward excellence. We have created the Kent Scholarship for Academic Excellence.
The scholarship will be awarded to any applicant who achieves a minimum of A*AA over three A levels, or the equivalent qualifications (including BTEC and IB) as specified on our scholarships pages.
Most of the teaching is by lectures and examples classes. At Stage 1, you can go to regular supervised classes where you can get help and advice on the way you approach problems. Modules that include programming or working with computer software packages usually involve practical sessions.
Each year, there are a number of special lectures by visiting actuaries from external organisations, to which all students are invited. These lectures help to bridge the gap between actuarial theory and its practical applications.
The course provides practical experience of working with PROPHET, a market-leading actuarial software package used by commercial companies worldwide for profit testing, valuation and model office work.
Modules are assessed by end-of-year examinations, or by a combination of coursework and examinations.
For a student studying full time, each academic year of the programme will comprise 1200 learning hours which include both direct contact hours and private study hours. The precise breakdown of hours will be subject dependent and will vary according to modules. Please refer to the individual module details under Course Structure.
Methods of assessment will vary according to subject specialism and individual modules. Please refer to the individual module details under Course Structure.
We aim to help students develop:
In addition, the Year in Industry enables students to gain awareness of the application of technical concepts in the workplace.
You gain knowledge and understanding of:
You gain the following intellectual abilities:
You gain actuarial-science skills in the following:
You gain transferable skills in the following:
Mathematics at Kent was ranked 19th for student satisfaction in The Complete University Guide 2023.
The Actuarial Science programme allows you to gain exemptions from the professional examinations set by the UK actuarial profession, so our graduates have a head start when looking to qualify as actuaries. It also provides an excellent foundation for careers in many other areas of finance and risk.
Recent graduates have gone on to work in:
The University has a friendly Careers and Employability Service, which can give you advice on how to:
You graduate with an excellent grounding in the fundamental concepts and principles of actuarial science, together with practical experience in the use of industry-standard actuarial software.
To help you appeal to employers, you also learn key transferable skills that are essential for all graduates. These include the ability to:
You can also gain extra skills by signing up for one of our Kent Extra activities, such as learning a language or volunteering.
This course page is for the 2022/23 academic year. Please visit the current online prospectus for a list of undergraduate courses we offer.
T: +44 (0)1227 823254
E: internationalstudent@kent.ac.uk
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