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UCAS codeUCAS N325
Duration4 years full-time (5 years with a year in industry)
- Year in Industry
- Foundation Year
Actuaries evaluate and manage financial risks, particularly in the financial services industry. Our specialist courses are taught by professionally qualified actuaries and internationally renowned statisticians to make sure you're fully prepared for your career.
Our foundation year course provides an opportunity for you to develop your mathematics skills and start learning some university-level material, fully preparing you for university study before you progress onto the Actuarial Science degree.
We're fully accredited by the Institute and Faculty of Actuaries, which means that you can achieve up to six exemptions from the 13 professional examinations required to become a qualified actuary.
Why study Actuarial Science at Kent?
Been there, done that.
You’ll benefit from the extensive industrial experience of the qualified Actuaries who teach on the course.
Fully accredited by the Institute and Faculty of Actuaries (IFoA).
Develop your skills.
Learn industry standard software like PROPHET, R and Python.
Take a placement year to boost your professional skills and get paid to do it.
You’ll benefit from free membership of the Kent Maths Society and Invicta Actuarial Society.
Everything you need to know about our Actuarial Science course
How you'll study
Applications are individually considered. Please contact an Admissions Officer.
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.
24 points overall or 13 points HL including HL Maths or HL Mathematics: Analysis and Approaches at 4 or SL Maths or SL Mathematics: Analysis and Approaches at 6
The University will consider applicants holding T level qualifications in subjects closely aligned to the course.
The University will not necessarily make conditional offers to all Access candidates but will continue to assess them on an individual basis.
If we make you an offer, you will need to obtain/pass the overall Access to Higher Education Diploma and may also be required to obtain a proportion of the total level 3 credits and/or credits in particular subjects at merit grade or above.
When considering your application, we look at both your qualifications and your potential, as shown, for example, by your personal statement and the comments of your referees.
To take a foundation degree, you need to have an English language standard of 5.5 in IELTS; however please note that these requirements are subject to change. For the latest details, see www.kent.ac.uk/ems/eng-lang-reqs
What you'll study
The following modules are offered to our current students. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.
If your qualifications are not sufficient, for whatever reason, for direct entry onto a degree programme, you can apply for this programme.
If your first language is not English, the Foundation Year offers additional classes taught by staff who are specialists in teaching English as a foreign language.
Through this module, students will develop the transferable linguistic and academic skills necessary to successfully complete other modules on their programme and acquire the specific language skills that they will require when entering SMSAS and SPS Stage 1 programmes. The programme of study focuses on writing and speaking skills, enhancing academic language through classroom, homework and assessed activities. Writing skills will be used to write a technical report, interpret data and describe processes. Spoken skills will be used in presentations and seminars.
This module introduces fundamental methods needed for the study of mathematical subjects at degree level.
Trigonometry: introduction to the trigonometric functions, inverse trigonometric functions, radians, properties of sine and cosine functions, trigonometric identities, solving trigonometric equations
Geometry: circles and ellipses, triangles, SOHCAHTOA, sine and cosine rule, opposite and alternate angle theorems
Hyperbolic functions: introduction to hyperbolic functions and inverse hyperbolic functions including definitions, domains and ranges and graphs.
Complex numbers: introduction to the system of complex numbers and its geometrical interpretation.
This module introduces the students to the basics of Maple and three topics in the mathematical sciences. The precise topics will vary in any particular year. Potential topics include (for example): history and/or people active in the mathematical sciences, algorithms, engaging the public in the mathematical sciences, mathematical games. Each topic is supported by a series of workshops introducing key aspects of the topic.
Maple: the Maple environment, basic commands, basic calculus, curve sketching.
There is no specific mathematical syllabus for the topics part of the module.
Statistical techniques are a fundamental tool in being able to measure, analyse and communicate information about sets of data. Using illustrative data sets we show how statistics can be indispensable in applied sciences and other quantitative areas. This module covers the basic methods used in probability and statistics using Excel for larger data sets. A more detailed indication of the module content follows.
Sampling from populations. Data handling and analysis using Excel. Graphical representation for the interpretation of univariate and bivariate data; outliers. Sample summary statistics: mean, variance, standard deviation, median, quartiles, inter-quartile range, correlation. Probability: combinatorics, conditional probability, Bayes' Theorem. Random variables: discrete, continuous; expectation, variance, standard deviation. Discrete and continuous distributions: Binomial, discrete uniform, Normal, uniform. Sampling distributions for the mean and proportion. Hypothesis testing: one sample, mean of Normal with known variance and proportion, 1- and 2-tail. Confidence intervals: one sample, mean of Normal with known variance and population proportion
Functions: Functions, inverse functions and composite functions. Domain and range.
Elementary functions including the exponential function, the logarithm and natural logarithm functions and ax for positive real numbers a. Basic introduction to limits and continuity of a function, without epsilon-delta proofs.
The derivative: The derivative as the gradient of the tangent to the graph; interpretation of the derivative as a rate of change. The formal definition of the derivative and the calculation of simple examples from first principles. Elementary properties of the derivative, including the product rule, quotient rule and the chain rule; differentiation of inverse functions; calculating derivatives of familiar functions, including trigonometric, exponential and logarithmic functions. Applications of the derivative, including optimisation, gradients, tangents and normal. Parametric and implicit differentiation of simple functions. Additional material may include L'Hopital’s Rule and Taylor series.
Graphs: Curve sketching including maxima, minima, stationary points, points of inflection, vertical and horizontal asymptotes and simple transformations on graphs of functions. Additional material may include parametric curves and use of Maple to plot functions.
This module introduces the ideas of integration and numerical methods.
a) Integration: Integration as a limit of a sum and graphical principles of integration, derivatives, anti-derivatives and the Fundamental Theorem of Calculus (without proof), definite and indefinite integrals, integration of simple functions.
b) Methods of integration: integration by parts, integration by change of variables and by substitution, integration by partial fractions.
c) Solving first order differential equations: separable and linear first order differential equations. Construction of differential equations in context, applications of differential equations and interpretation of solutions of differential equations.
d) Maple: differentiation and integration, curve sketching, polygon plots, summations.
Additional material may include root finding using iterative methods, parametric integration, surfaces and volumes of revolution.
Students will be introduced to key mathematical skills, necessary in studying for a mathematics degree: use of the University Library and other sources to support their learning, present an argument in oral or written form, learn about staff in the School and beyond, etc. In particular, students will study various techniques of proof (by deduction, by exhaustion, by contradiction, etc.). These techniques will be illustrated through examples chosen from various areas of mathematics (and in particular co-requisite modules).
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.
To be confirmed.
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.
In this module we will study linear partial differential equations, we will explore their properties and discuss the physical interpretation of certain equations and their solutions. We will learn how to solve first order equations using the method of characteristics and second order equations using the method of separation of variables.
Introduction to linear PDEs: Review of partial differentiation; first-order linear PDEs, the heat equation, Laplace's equation and the wave equation, with simple models that lead to these equations; the superposition principle; initial and boundary conditions
Separation of variables and series solutions: The method of separation of variables; simple separable solutions of the heat equation and Laplace’s equation; Fourier series; orthogonality of the Fourier basis; examples and interpretation of solutions
Solution by characteristics: the method of characteristics for first-order linear PDEs; examples and interpretation of solutions; characteristics of the wave equation; d’Alembert’s solution, with examples; domains of influence and dependence; causality.
Formulation/Mathematical modelling of optimisation problems
Linear Optimisation: Graphical method, Simplex method, Phase I method, Dual problems,
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.
Year in industry
Students on this course can 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 put your academic skills into practice. It also gives you an idea of your career options. Recent placements have included IBM, management consultancies, government departments, actuarial firms and banks.
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.
This module will cover a number of syllabus items set out in Subject CM2 – Actuarial Mathematics 2 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.
How you'll study
Teaching and assessment
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:
- Equip students with the technical appreciation, skills and knowledge appropriate to graduates in mathematical subjects.
Develop students’ facilities of rigorous reasoning and precise expression.
Develop students’ capabilities to formulate and solve problems.
Develop in students appreciation of recent actuarial developments, and of the links between the theory of the subjects and their practical application in industry.
Develop in students a logical, mathematical approach to solving problems.
Develop in students an enhanced capacity for independent thought and work.
Ensure students are competent in the use of information technology, and are familiar with computers, together with the relevant software.
Provide students with opportunities to study advanced topics, engage in research at some level, and develop communication and personal skills.
Provide successful students with eligibility for up to 6 exemptions from examinations of the Institute and Faculty of Actuaries.
For the programme involving a year in industry, to enable students to gain awareness of the application of technical concepts in the workplace.
Knowledge and understanding
You gain knowledge and understanding of:
- Core mathematical understanding in the principles of calculus, algebra, mathematical methods, discrete mathematics, analysis and linear algebra.
- Statistical understanding in the subjects of probability and inference and time series modelling, plus specialist statistics applications in insurance.
- Information technology skills as relevant to actuaries.
- Methods and techniques appropriate to the mathematics of finance, of finance and financial reporting, and of financial economics.
- The principles of economics as relevant to actuaries.
- Methods and techniques appropriate to survival models.
- The principles of specific actuarial mathematics techniques.
- Understanding of the core areas of actuarial practice.
You gain the following intellectual abilities:
- Ability to demonstrate a reasonable understanding of the main body of knowledge for the course.
- Ability to demonstrate a reasonable level of skill in calculation and manipulation of the material written within the course and some capability to solve problems formulated within it.
- Ability to apply a range of core concepts and principles in well-defined contexts relevant to actuarial science.
- Ability to use logical argument.
- Ability to demonstrate skill in solving problems by various appropriate methods.
- Ability in relevant computer skills and usage.
- Ability to demonstrate research, presentation and report-writing skills.
- Ability to work with relatively little guidance.
You gain actuarial science skills in the following:
- Skills in the specific mathematical and statistical techniques used in actuarial science, and in their application to solving actuarial problems.
- Skills in the specific information technology and software used in actuarial science.
- Understanding of the practical applications of the course material in insurance.
- Ability to develop simple actuarial computer models to solve actuarial problems and to interpret and communicate the results.
You gain transferable skills in the following:
- Problem-solving skills, relating to qualitative and quantitative information.
- Communication skills, covering both written and oral communication.
- Numeracy and computational skills.
- Information-retrieval skills, in relation to primary and secondary information sources, including information retrieval through on-line computer searches.
- Information technology skills such as word-processing and spreadsheet use, internet communication, etc.
- Interpersonal skills, relating to the ability to interact with other people and to engage in team-working.
- Time-management and organisational skills, as evidenced by the ability to plan and implement efficient and effective modes of working.
- Study skills needed for continuing professional development.
Our Actuarial Science programme gives you exemptions from the professional exams set by the UK actuarial profession, so you'll have a head start when looking to qualify as an actuary. It's also an excellent foundation for careers in many other areas of finance and risk.
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Fees and funding
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.*
Your fee status
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 year in industry
Fees for undergraduate students are £1,385.
Fees for year abroad
Fees for undergraduate students 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.
Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.
The Kent Scholarship for Academic Excellence
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.
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.
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Ready to apply?
If you are from the UK or Ireland, you must apply for this course through UCAS. If you are not from the UK or Ireland, you can apply through UCAS or directly on our website if you have never used UCAS and you do not intend to use UCAS in the future.
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