Our modern world is heavily reliant on financial markets. Financial institutions depend on skilled individuals to manage their portfolios, applying mathematical modelling, statistical analysis and the problem-solving know-how of mathematics graduates.
Our specialist Financial Mathematics programmes combine the in-house expertise of our internationally-renowned mathematicians, statisticians and actuaries to ensure you are fully prepared for your future career. You will be encouraged to fulfil your potential whilst studying in our friendly and dynamic school based in the multi-award-winning Sibson Building.
Our degree programme
To help bridge the gap between school and university, you’ll attend small group tutorials in Stage 1, where you can practice the new mathematics you’ll be learning, ask questions and work with other students to find solutions. You’ll study a mixture of pure and applied mathematics, statistics and economics, providing you with a solid foundation for your later studies.
In Stage 2, you study some core modules which build upon the material learnt at Stage 1. You also start to tailor your degree to your interests through our range of optional modules, continuing to explore the areas you enjoy into Stage 3.
Macaulee shares his experiences studying BSc Financial Mathematics at Kent.
Year in industry
If you want to gain paid industry experience as part of your degree programme, our popular Financial Mathematics with a Year in Industry.
If you decide to take the Year in Industry, our in-house Placements Team will support you in developing the skills and knowledge needed to successfully secure a placement through a specialist programme of workshops and events.
If your grades do not qualify you for direct entry to this programme, you may be able to take a four-year degree with a foundation year. For more details see Mathematics including a Foundation Year.
You have access to a range of professional mathematical and statistical software such as:
Our staff use these packages in their teaching and research.
The School of Mathematics and Actuarial Science Student Society is run by students. It aims to improve the student experience for its members, socially and academically. In previous years the Society has organised:
- talks and workshops
- extra revision sessions
- socials and networking events.
- seminars and workshops employability events.
The School of Mathematics, Statistics and Actuarial Science also puts on regular events that you are welcome to attend. In the past, these have included:
- seminars and workshops
- employability events.
Mathematics at Kent scored 91.5 out of 100 in The Complete University Guide 2019.
In the National Student Survey 2018, over 87% of final-year Mathematics and Statistics students who completed the survey, were satisfied with the overall quality of their course.
Of Mathematics and Statistics students who graduated from Kent in 2017 and completed a national survey, over 95% were in work or further study within six months (DLHE).
Teaching Excellence Framework
Based on the evidence available, the TEF Panel judged that the University of Kent delivers consistently outstanding teaching, learning and outcomes for its students. It is of the highest quality found in the UK.
Please see the University of Kent's Statement of Findings for more information.
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.
|Compulsory modules currently include||Credits|
MA306 - Statistics
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.View full module details
MA345 - Introduction to Finance
Time value of money: Basic concepts, Compounding to determine future values, Inflation,
Financial valuation and cash flow analysis: Discounting, Interest rates and time requirements, Future and Present value. Project Evaluation
Characteristics of different financial securities: Debt capital, bonds and stocks, valuation of bonds and stocks
Terminology in finance: Securities markets, primary market, secondary securities markets, the role of the various financial markets.View full module details
MA347 - Linear Mathematics
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.View full module details
MA348 - Mathematical Methods 1
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. Tutorials and Maple worksheets will be used to support taught material.
Complex numbers: Complex arithmetic, the complex conjugate, the Argand diagram, de Moivre's Theorem, modulus-argument form; elementary functions
Polynomials: Fundamental Theorem of Algebra (statement only), roots, factorization, rational functions, partial fractions
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
Scalar ordinary differential equations (ODEs): definition; methods for first-order ODEs; principle of superposition for linear ODEs; particular integrals; second-order linear ODEs with constant coefficients; initial-value problems
Curve sketching: graphs of elementary functions, maxima, minima and points of inflection, asymptotesView full module details
MA349 - Mathematical Methods 2
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)View full module details
MA350 - Microeconomics for Financial Mathematicians
The aim of this module is to introduce students to the basic tools used by economists to study resource allocation, price formation and decision making. The module focuses on classical theories of consumer and producer behaviour and on the theory of competitive equilibrium.
The module starts with an analysis of the optimisation problems of price-taking consumers and firms. We then analyse market interaction and the formation of prices in the framework of perfect competition. We will introduce decision making under uncertainty and apply it to problems in finance. We will conclude with a brief introduction to game theory.View full module details
MA351 - Probability
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.View full module details
MA352 - Real Analysis 1
Real Numbers: Rational and real numbers, absolute value and metric structure on the real numbers, induction, infimum and supremum.
Limits of Sequences: Sequences, definition of convergence, epsilon terminology, uniqueness, algebra of limits, comparison principles, standard limits, subsequences and non-existence of limits, convergence to infinity.
Completeness Properties: Cantor's Intersection Theorem, limit points, Bolzano-Weierstrass theorem, Cauchy sequences.
Continuity of Functions: Functions and basic definitions, limits of functions, continuity and epsilon terminology, sequential continuity, Intermediate Value Theorem.
Differentiation: Definition of the derivative, product rule, quotient rule and chain rule, derivatives and local properties, Mean Value Theorem, L'Hospital's Rule.
Taylor Approximation: Taylor's Theorem, remainder term, Taylor series, standard examples, limits using Taylor series.View full module details
|Compulsory modules currently include||Credits|
MA5501 - Applied Statistical Modelling 1
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, we study how suitable models can be constructed, how to fit them to data and how suitable conclusions can be drawn. Both theoretical and practical aspects are covered, including the use of R.View full module details
MA5505 - Linear Partial Differential Equations
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.View full module details
MA5506 - Macroeconomics for Financial Mathematicians
The aim of this module is to introduce students to a set of tools that they can use to analyse macroeconomic issues in the short run and long run, and to help them understand which modelling techniques are appropriate for particular macroeconomic problems.
This module enhances the students' ability to understand the determinants of key macroeconomic variables including output, inflation, interest rates and exchange rates. Students’ technical skills are developed and they are expected to apply their knowledge to real-world policymaking and link it to financial issues.View full module details
MA5507 - Mathematical Statistics
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.View full module details
MA5509 - Numerical Methods
This module is an introduction to the methods, tools and ideas of numerical computation. In mathematics, one often encounters standard problems for which there are no easily obtainable explicit solutions, given by a closed formula. Examples might be the task of determining the value of a particular integral, finding the roots of a certain non-linear equation or approximating the solution of a given differential equation. Different methods are presented for solving such problems on a modern computer, together with their applicability and error analysis. A significant part of the module is devoted to programming these methods and running them in MATLAB.
Introduction: Importance of numerical methods; short description of flops, round-off error, conditioning
Solution of linear and non-linear equations: bisection, Newton-Raphson, fixed point iteration
Interpolation and polynomial approximation: Taylor polynomials, Lagrange interpolation, divided differences, splines
Numerical integration: Newton-Cotes rules, Gaussian rules
Numerical differentiation: finite differences
Introduction to initial value problems for ODEs: Euler methods, trapezoidal method, Runge-Kutta methods.View full module details
MA5511 - Optimisation with Financial Applications
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.View full module details
|Optional modules may include||Credits|
MA5512 - Ordinary Differential Equations
This module introduces the basic ideas to solve certain ordinary differential equations, like first order scalar equations, second order linear equations and systems of linear equations. It mainly considers their qualitative and analytical aspects. Outline syllabus includes: First-order scalar ODEs; Second-order scalar linear ODEs; Existence and Uniqueness of Solutions; Autonomous systems of two linear first-order ODEs.View full module details
MA501 - Statistics for Insurance
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. Stochastic processes of events such as accidents, together with the financial flow of their payouts underpin much of the work. Since the earliest games of chance, the probability of ruin has been a topic of interest. Outline Syllabus includes: Decision Theory; Bayesian Statistics; Loss Distributions; Reinsurance; Credibility Theory; Empirical Bayes Credibility theory; Risk Models; Ruin Theory; Generalised Linear Models; Run-off Triangles.View full module details
MA517 - Corporate Finance for Financial Mathematics
This module introduces and explores a range of topics relating to corporate finance which are fundamental to understanding why and how companies raise money to start a business or expand an existing one. The module covers the different ways that the money can be raised, for example from a bank or through a stocks and shares market, and the interest rate or investment return that an investor will expect to receive from a company in order to provide the money required. This is a very practical module to the extent that it will help students develop business awareness in the field of company finance. Reference will often be made to actual happenings in the financial markets in support of the material covered.View full module details
|Compulsory modules currently include||Credits|
MA535 - Portfolio Theory and Asset Pricing Models
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 published by the Institute and Faculty of Actuaries.View full module details
MA636 - Stochastic Processes
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.
Marks on this module can count towards exemption from the professional examination CT4 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.View full module details
MA6529 - Statistical Learning
Multivariate normal distribution, Inference from multivariate normal samples, principal component analysis, mixture models, factor analysis, clustering methods, discrimination and classification, graphical models, the use of appropriate software.View full module details
MA6534 - Derivative Markets
Futures Markets: Mechanics, Hedging strategies
Interest rates: Type of rates, LIBOR, Repo, Spot, Forward
Determination of Forward and Future Prices: Short and long positions, forward prices
Interest Rate Derivatives and Swaps
Option Markets: Mechanics, Properties, trading strategiesView full module details
MA6540 - Financial Econometrics
Overview of statistical methods. Stationary time series. Autocovariance and autocorrelation functions. Partial autocorrelation functions. ARMA processes. ARIMA model building, testing and estimation. Criteria for choosing between models. Forecasting. Cointegration. Prediction bounds. Asset return and risk. Term structure of interest rates. Distributional properties of asset returns. Testing for CAPM. Testing random walk hypothesis and predicting asset return. Sharpe ratio and efficient portfolio. Cross-section modelling and GMM. Estimate multifactor models. Financial applications of AR, MA, and ARMA. ARCH and GARCH models. Volatility processes. Simple applications of these techniques using R.View full module details
|Optional modules may include||Credits|
MA6544 - Nonlinear Systems and Applications
• Scalar autonomous nonlinear first-order ODEs. Review of steady states and their stability; the slope fields and phase lines.
• Autonomous systems of two nonlinear first-order ODEs. The phase plane; Equilibra and nullclines; Linearisation about equilibra; Stability analysis; Constructing phase portraits; Applications. Nondimensionalisation.
• Stability, instability and limit cycles. Liapunov functions and Liapunov's theorem; periodic solutions and limit cycles; Bendixson's Negative Criterion; The Dulac criterion; the Poincare-Bendixson theorem; Examples.
• Dynamics of first order difference equations. Linear first order difference equations; Simple models and cobwebbing: a graphical procedure of solution; Equilibrium points and their stability; Periodic solutions and cycles. The discrete logistic model and bifurcations.View full module details
MA771 - Computational Statistics
Statistics methods contribute significantly to areas such as biology, ecology, sociology and economics. The real data collected does not always follow standard statistical models. This module looks at modern statistical models and methods that can be utilised for such data, making use of R programs to execute these methods.
Indicative module content: Motivating examples; model fitting through maximum likelihood for specific examples; function optimization methods; profile likelihood; score tests; Wald tests; confidence interval construction; latent variable models; EM algorithm; mixture models; simulation methods; importance sampling; kernel density estimation; Monte Carlo inference; bootstrap; permutation tests; R programs.View full module details
MA6503 - Communicating Mathematics
There is no specific mathematical syllabus for this module; students will chose a topic in mathematics, statistics or financial mathematics from a published list on which to base their coursework assessments (different topics for levels 6 and 7). The coursework is supported by a series of workshops covering various forms of written and oral communication. These may include critically evaluating the following: a research article in mathematics, statistics or finance; a survey or magazine article aimed at a scientifically-literate but non-specialist audience; a mathematical biography; a poster presentation of a mathematical topic; a curriculum vitae; an oral presentation with slides or board; a video or podcast on a mathematical topic. Guidance will be given on typesetting mathematics using LaTeX.View full module details
MA6504 - Discovering and Communicating Mathematics
There is no specific mathematical syllabus for this module. Students will study a topic in mathematics or statistics, either individually or within a small group, and produce an individual or group project on the topic as well as individual coursework assignments. Projects will be chosen from published lists of individual and of group projects. The coursework and project-work are supported by a series of workshops covering various forms of written and oral communication and by supervision from an academic member of staff.
The workshops may include critically evaluating the following: a research article in mathematics or statistics; a survey or magazine article aimed at a scientifically-literate but non-specialist audience; a mathematical biography; a poster presentation of a mathematical topic; a curriculum vitae; an oral presentation with slides or board; a video or podcast on a mathematical topic. Guidance will be given on typesetting mathematics using LaTeX.View full module details
MA6512 - Applied Statistical Modelling 2
This is a practical module to develop the skills required by a professional statistician (report writing, consultancy, presentation, wider appreciation of assumptions underlying methods, selection and application of analysis method, researching methods).
Software: R, SPSS and Excel (where appropriate/possible). Report writing in Word. PowerPoint for presentations.
• Presentation of data
• Report writing and presentation skills
• Hypothesis testing: formulating questions, converting to hypotheses, parametric and non-parametric methods and their assumptions, selection of appropriate method, application and reporting. Use of resources to explore and apply additional tests. Parametric and non-parametric tests include, but are not limited to, t-tests, likelihood ratio tests, score tests, Wald test, chi-squared tests, Mann Whitney U-test, Wilcoxon signed rank test, McNemar's test.
• Linear and Generalised Linear Models: simple linear and multiple regression, ANOVA and ANCOVA, understanding the limitations of linear regression, generalised linear models, selecting the appropriate distribution for the data set, understanding the difference between fixed and random effects, fitting models with random effects, model selection.
• Consultancy skills: group work exercise(s)View full module details
MA6518 - Games and Strategy
In this module we study the fundamental concepts and results in game theory. We start by analysing combinatorial games, and discuss game trees, winning strategies, and the classification of positions in so called impartial combinatorial games. We then move on to discuss two-player zero-sum games and introduce security levels, pure and mixed strategies, and prove the famous von Neumann Minimax Theorem. We will see how to solve zero-sum two player games using domination and discuss a general method based on linear programming. Subsequently we analyse arbitrary sum two-player games and discuss utility, best responses, Nash equilibria, and the Nash Equilibrium Theorem. The final part of the module is devoted to multi-player games and cooperation; we analyse coalitions, the core of the game, and the Shapley value.View full module details
MA537 - Mathematics of Financial Derivatives
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.View full module details
MA538 - Applied Bayesian Modelling
The origins of Bayesian inference lie in Bayes' Theorem for density functions; the likelihood function and the prior distribution combine to provide a posterior distribution which reflects beliefs about an unknown parameter based on the data and prior beliefs. Statistical inference is determined solely by the posterior distribution. So, for example, an estimate of the parameter could be the mean value of the posterior distribution. This module will provide a full description of Bayesian analysis and cover popular models, such as the normal distribution. Initially, the flavour will be one of describing the Bayesian counterparts to well known classical procedures such as hypothesis testing and confidence intervals. Outline Syllabus includes: Bayes Theorem for density functions; Exchangeability; Choice of priors; Conjugate models; Predictive distribution; Bayes estimates; Sampling density functions; Gibbs samplers; OpenBUGS; Bayesian hierarchical models; Applications of hierarchical models; Bayesian model choice.View full module details
MA587 - Numerical Solution of Differential Equations
Most differential equations which arise from physical systems cannot be solved explicitly in closed form, and thus numerical solutions are an invaluable way to obtain information about the underlying physical system. The first half of the module is concerned with ordinary differential equations. Several different numerical methods are introduced and error growth is studied. Both initial value and boundary value problems are investigated. The second half of the module deals with the numerical solution of partial differential equations. The syllabus includes: initial value problems for ordinary differential equations; Taylor methods; Runge-Kutta methods; multistep methods; error bounds and stability; boundary value problems for ordinary differential equations; finite difference schemes; difference schemes for partial differential equations; iterative methods; stability analysis.View full module details
Teaching and assessment
Teaching amounts to approximately 16 hours of lectures and classes per week. Modules that involve programming or working with computer software packages usually include practical sessions.
The majority of Stage 1 modules are assessed by end-of-year examinations. Many Stage 2 and 3 modules include coursework which normally counts for 20% of the final assessment. Both Stage 2 and 3 marks count towards your final degree result.
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.
The programme aims to:
- instil in students the technical appreciation, skills and knowledge required by graduates in financial mathematics
- develop students’ abilities for rigorous reasoning and precise expression, and formulate and solve problems relevant to financial mathematics
- encourage an appreciation of recent developments in financial mathematics, and the links between theory and practical application
- encourage a logical, mathematical approach to solving problems
- develop an enhanced capacity for independent thought and work
- ensure students are competent in the use of information technology, and are familiar with computers and the relevant software
- provide opportunities to study advanced topics in financial mathematics, engage in research, and develop communication and personal skills
- instil awareness of the application of technical concepts in the workplace (for students undertaking a year in industry).
Knowledge and understanding
You gain knowledge and understanding of:
- core mathematical skills in the principles of calculus, algebra, mathematical methods, discrete mathematics, analysis and linear algebra
- statistical aspects of probability and inference
- information technology skills relevant to mathematicians
- methods and techniques appropriate to financial mathematics
- logical mathematical argument and deductive reasoning.
You develop intellectual skills in the following areas:
- the ability to demonstrate a reasonable understanding of knowledge in financial mathematics
- calculation and manipulation of the material within the programme
- application of concepts and principles in various contexts relevant to financial mathematics
- a capacity for logical argument
- problem solving by various methods
- computer skills
- the capacity to work with relatively little guidance.
You gain subject-specific skills to:
- demonstrate knowledge of key mathematical concepts and topics, both explicitly and by applying them to the solution of problems
- comprehend problems, abstract the essentials of problems and formulate them mathematically and in symbolic form to facilitate their analysis and solution
- use computational and more general IT facilities as an aid to mathematical processes
- present mathematical arguments and the conclusions from them with clarity and accuracy.
You gain transferable skills in the following:
- problem-solving, in relation to qualitative and quantitative information
- effective communication
- numeracy and computational abilities
- information retrieval in relation to primary and secondary information sources, including online computer searches
- computer skills, such as word-processing and spreadsheet use, internet communication
- time-management and organisational skills: the ability to plan and implement efficient and effective modes of working
- continuing professional development.
Through your studies, you acquire many transferable skills including the ability to deal with challenging ideas, think critically, write well and present your ideas clearly, all of which are considered essential by graduate employers.
Recent graduates have gone into careers in medical statistics, the pharmaceutical industry, the aerospace industry, software development, teaching, actuarial work, Civil Service statistics, chartered accountancy, the oil industry and postgraduate research.
The University will consider applications from students offering a wide range of qualifications. Typical requirements are listed below. Students offering alternative qualifications should contact us for further advice.
It is not possible to offer places to all students who meet this typical offer/minimum requirement.
New GCSE grades
If you’ve taken exams under the new GCSE grading system, please see our conversion table to convert your GCSE grades.
|Qualification||Typical offer/minimum requirement|
ABB including an A in Mathematics. Use of Maths A level is not accepted as a required subject. Only one of General Studies or Critical Thinking can count as a third A level.
If taking both A level Mathematics and A level Further Mathematics:
ABC including Mathematics at grade A and Further Mathematics at grade C. Use of Maths A level is not accepted as a required subject. Only one of General Studies or Critical Thinking can count as a third A level.
|Access to HE Diploma||
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.
|BTEC Level 3 Extended Diploma (formerly BTEC National Diploma)||
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.
34 points overall or 16 points at HL including Mathematics 6 at HL
The University receives applications from over 140 different nationalities and consequently will consider applications from prospective students offering a wide range of international qualifications. Our International Development Office will be happy to advise prospective students on entry requirements. See our International Student website for further information about our country-specific requirements.
Please note that if you need to increase your level of qualification ready for undergraduate study, the School of Mathematics, Statistics and Actuarial Science offers a foundation year.
English Language Requirements
Please see our English language entry requirements web page.
Please note that if you are required to meet an English language condition, we offer a number of 'pre-sessional' courses in English for Academic Purposes. You attend these courses before starting your degree programme.
General entry requirements
Please also see our general entry requirements.
The 2020/21 tuition fees have not yet been set. As a guide only, the 2019/20 annual tuition fees for this programme are:
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.
General additional costs
Kent 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.
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 AAA over three A levels, or the equivalent qualifications (including BTEC and IB) as specified on our scholarships pages.
The scholarship is also extended to those who achieve AAB at A level (or specified equivalents) where one of the subjects is either mathematics or a modern foreign language. Please review the eligibility criteria.
The Key Information Set (KIS) data is compiled by UNISTATS and draws from a variety of sources which includes the National Student Survey and the Higher Education Statistical Agency. The data for assessment and contact hours is compiled from the most populous modules (to the total of 120 credits for an academic session) for this particular degree programme.
Depending on module selection, there may be some variation between the KIS data and an individual's experience. For further information on how the KIS data is compiled please see the UNISTATS website.
If you have any queries about a particular programme, please contact email@example.com.