Undergraduate BSc (Hons)
Actuarial Science
with a Year in Industry
Take a giant step towards a well-paid and fulfilling career in financial risk management.
Take a giant step towards a well-paid and fulfilling career in financial risk management.
Think you've found the right course? We still have spots available through Clearing. Apply now to secure your place and join our dynamic and welcoming community at Kent this September.
Apply nowActuaries 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 year in industry course 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.
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.
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).
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.
At Kent, you’re more than your grades. We look at each student’s circumstances as a whole before deciding whether to make an offer to study here. We also take this flexible approach when we receive your exam results.
Check our Clearing vacancy list or call us now +44 (0)1227 768896 to find out if we have a course that’s right for you. See our Clearing website for more details on how Clearing works at Kent.
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.
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.
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 RSA Group, Lloyds and the Bank of England.
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.
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.
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:
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. You can achieve exemptions from six of the thirteen professional examinations required to become a qualified actuary: CB1, CB2, CM1, CM2, CS1 and CS2.
You could then continue your studies with our MSc Applied Actuarial Science to also achieve exemptions in CP1, CP2, CP3, and two SP subjects.
You may also want to consider our 2-year International Master's in Applied Actuarial Science, where you can achieve exemptions from CB1, CB2, CM1, CM2, CS1, CS2, CP1, CP2, CP3, and two SP subjects.
The 2024/25 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 undergraduate students are £1,850.
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.
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.
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