Actuarial Science

Financial Mathematics with a Year in Industry - BSc (Hons)

UCAS code NG31

2018

The degree consists of compulsory modules, especially in the first two years, and some optional modules to suit your interests and goals. The year in industry takes place between the second and final years of full-time study and counts towards the final degree result.

2018

Overview

The School of Mathematics, Statistics and Actuarial Science provides a supportive learning environment with high contact hours per module. Our academic staff are available to advise and support you throughout your studies, helping you to take responsibility for your own learning.

Our degree programme

The Financial Mathematics programme provides a thorough grounding in the mathematical concepts, tools and skills needed to understand financial decision making. It offers you the opportunity to study financial theory and applications built on rigorous foundations within a friendly and highly successful department.

You gain access to knowledge and capabilities that are valued by financial sector employers, corporations and postgraduate academic programmes. In combination with specialised finance and economics topics, the programme incorporates core mathematical principles, probability and inference, and a range of statistical concepts and techniques. In addition, you have a chance to develop communication and personal skills.

Student view

Macaulee shares his experiences studying BSc Financial Mathematics at Kent. 


Year in industry

You spend a year working in industry between Stages 2 and 3. We can offer help and advice in finding a placement. This greatly enhances your CV and gives you the opportunity to apply your academic skills in a practical context. It also gives you an idea of your career options. Recent placements have included IBM, management consultancies, government departments, insurance firms and banks.

This degree is also available as a three-year course without a year in industry. For more details, see Financial Mathematics

Foundation year

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

Study resources

You have access to a range of professional mathematical and statistical software such as:

  • Maple
  • MATLAB
  • Minitab.

Our staff use these packages in their teaching and research.

Extra activities

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.

Independent rankings

Of Mathematics and Statistics students who graduated from Kent in 2016, over 96% 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.

TEF Gold logo

Course structure

The course structure below gives a flavour of the modules that will be available to you and provides details of the content of this programme. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation. Most programmes will require you to study a combination of compulsory and optional modules.

Stage 1

Modules may include Credits

Increasingly data are collected to inform future decisions, varying from which websites people access on a regular basis to how patients respond to new drugs, to how the stock market responds to global events, or to how animals move around their local habitat. Therefore, most professionals will need to extract useful information from data and to manage and present data in their working lives.  This module explores some of the basic concepts of statistics, from data summarisation to the main methods of statistical inference. The techniques that are discussed can be used in their own right for simple statistical analyses, but serve as an important foundation for later, more advanced, modules. The statistical computing package R is used throughout the module for data analysis. The syllabus includes: an introduction to R and investigating data sets, sampling and sampling distributions, point and interval estimation, hypothesis testing, association between variables.

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15

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.

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15

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.

Topics covered will include:

Basic set theory: introduction to sets, operations on sets (union, intersection, Cartesian product, complement), basic counting (inclusion-exclusion for 2 sets).

Functions and Relations: injective, surjective, bijective functions. Permutations, sign of a permutation. The Pigeonhole Principle. Cardinality of sets. Binomial coefficients, Binomial Theorem. Equivalence relations and partitions.

Systems of linear equations and Gaussian elimination: operations on systems of equations, echelon form, rank, consistency, homogeneous and non-homogeneous systems.

Matrices: operations, invertible matrices, trace, transpose.

Determinants: definition, properties and criterion for a matrix to be invertible.

Vector spaces: linearly independent and spanning sets, bases, dimension, subspaces.

Linear Transformations: Definition. Matrix of a Linear Transformation. Change of Basis.

Diagonalisation: Eigenvalues and Eigenvectors, invariant spaces, sufficient conditions.

Bilinear forms: inner products, norms, Cauchy-Schwarz inequality.

Orthonormal systems: the Gram-Schmidt process.

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15

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, asymptotes

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15

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)

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15

Supply and Demand: Introduction to microeconomics, applying supply and demand, elasticity

Consumer Theory: Preferences and utility, budget constraints, deriving demand curves, applying consumer theory: labour

Producer Theory: Introduction to producer theory, productivity and costs, competition

Welfare Economics: Principles of welfare economics

Monopoly and Oligopoly: Monopoly, oligopoly

Intermediate Topics: Factor markets, international trade, uncertainty, capital supply and demand

Equity and Efficiency: Equity and efficiency, government redistribution policy

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15

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.

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15

Topics covered will include:

Real Numbers: Rational and real numbers, absolute value and metric structure on the real numbers, induction, countability and uncountability, 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, O and o notation, limits using Taylor series.

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15

Stage 2

Modules may include Credits

This module examines recent developments and methodologies in economics and the links between the theory and practical application. Micro- and macroeconomic models of economic behaviour are developed and analysed.  The syllabus includes: consumer demand, firms and supply; uncertainty and assets; macroeconomic measures; developments in growth theory; borrowing, lending and the inter-temporal budget constraint, consumption and investment theory, fiscal and monetary policy.

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15

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.

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15

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.

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15

This module is a pre-requisite for many of the other statistics modules at Stages 2, 3 and 4, but it can equally well be studied as a module in its own right, extending the ideas of probability and statistics met at Stage 1 and providing practice with the mathematical skills learned in MA348 and MA349. It starts by revising the idea of a probability distribution for one or more random variables and looks at different methods to derive the distribution of a function of random variables. These techniques are then used to prove some of the results underpinning the hypothesis test and confidence interval calculations met at Stage 1, such as for the t-test or the F-test. With these tools to hand, the module moves on to look at how to fit models (probability distributions) to sets of data. A standard technique, the method of maximum likelihood, is used to fit the model to the data to obtain point estimates of the model parameters and to construct hypothesis tests and confidence intervals for these parameters. Outline Syllabus includes: Joint, marginal and conditional distributions of discrete and continuous random variables; Transformations of random variables; Sampling distributions; Point and interval estimation; Properties of estimators; Maximum likelihood; Hypothesis testing; Neyman-Pearson lemma; Maximum likelihood ratio test.

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15

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.

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15

Many problems in finance can be seen as an optimisation subject to some condition. For example, investors usually hold shares in different companies but the total number of shares that can be held is limited by the available funds. Finding the numbers of shares which maximizes the return on the investment whilst respecting the limit on funds is a problem of optimisation (of the return) subject to a condition (the total funds). In this module you learn a range of techniques to solve optimisations subject to conditions. Both theoretical and practical aspects will be covered. Outline of syllabus: Modelling linear programming applications; Graphical method; Simplex method; dual problems; duality theorem; application of duality; complementarity; sensitivity analysis; dual simplex.

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15

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.

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15

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.

Marks on this module can count towards exemption from the professional examination CT6 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.

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15

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.

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15

Year in industry

The year in industry included in this programme provides you with the opportunity to gain valuable work experience. We can help you to find a placement and support you while you are there.

Modules may include Credits

Stage 3

Modules may include Credits

This module introduces the main features of basic financial derivative contracts and develops pricing techniques. Principle of no-arbitrage, or absence of risk-free arbitrage opportunities, is applied to determine prices of derivative contracts, within the framework of binomial tree and geometric Brownian motion models. The interplay between pricing and hedging strategies, along with risk management principles, are emphasized to explain the mechanisms behind derivative instruments. Models of interest rate and credit risk are also discussed in this context. Outline syllabus includes: An introduction to derivatives, binomial tree model, Black-Scholes option pricing formula, Greeks and derivative risk management, interest rate models, credit risk models.

Marks on this module can count towards exemption from the professional examination CT8 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.

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15

A stochastic process is a process developing in time according to probability rules, for example, models for reserves in insurance companies, queue formation, the behaviour of a population of bacteria, and the persistence (or otherwise) of an unusual surname through successive generations.The syllabus will include coverage of a wide variety of stochastic processes and their applications: Markov chains; processes in continuous-time such as the Poisson process, the birth and death process and queues.

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.

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15

A time series is a collection of observations made sequentially in time. Examples occur in a variety of fields, ranging from economics to engineering, and methods of analysing time series constitute an important area of statistics. This module focuses initially on various time series models, including some recent developments, and provides modern statistical tools for their analysis. The second part of the module covers extensively simulation methods. These methods are becoming increasingly important tools as simulation models can be easily designed and run on modern PCs. Various practical examples are considered to help students tackle the analysis of real data.The syllabus includes: Difference equations, Stationary Time Series: ARMA process. Nonstationary Processes: ARIMA Model Building and Testing: Estimation, Box Jenkins, Criteria for choosing between models, Diagnostic tests.Forecasting: 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: Spectral Analysis. Generation of pseudo – random numbers, simulation methods: inverse transform and acceptance-rejection, design issues and sensitivity analysis.

Marks on this module can count towards exemption from the professional examination CT6 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.

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15

The aim of this module is to equip students with the skills needed to communicate mathematics effectively to the world. This module is supported by a series of workshops covering various forms of written and oral communication. Each student will choose a topic in mathematics, statistics or financial mathematics from a published list on which to base their three coursework assessments which include a scientific writing assessment and an oral presentation.

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15

Financial Mathematicians employ a wide range of skills when collaborating on work-related projects. This module is designed to give students the opportunity to experience what it is like to work on such a project, and to develop the team-working, communication, time management and problem-solving skills that are vital in the workplace. Students are arranged into small teams, with each team working together under the guidance of a supervisor to produce a single written report, worth 50% of the total module mark. In addition, each student will submit project-related coursework, and coursework related to the Key Skills workshops attended in the Autumn Term. Each of these coursework elements will contribute a further 25% to the total module mark. The syllabus is determined by the topics offered by supervisors. A range of topics will be available, with many replicating the 'real-world' work that Financial Mathematicians undertake in their professional lives.

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15

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.

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30

Recently some quite novel applications have been found for "Discrete Mathematics", as opposed to the “Continuous Mathematics” based on the Differential and Integral Calculus. Thus methods for the encoding of information in order to safeguard against eavesdropping or distortion by noise, for example in online banking and digital television, have involved using some basic results from abstract algebra. This module will provide a self-contained introduction to this general area and will cover most of the following topics: (a) Modular arithmetic, polynomials and finite fields: Applications to orthogonal Latin squares, cryptography, “coin-tossing over a telephone”, linear feedback shift registers and m-sequences. (b) Error correcting codes: Binary block, linear and cyclic codes including repetition, parity-check, Hamming, simplex, Reed-Muller, BCH, Golay codes; channel capacity; Maximum likelihood, nearest neighbour, syndrome and algebraic decoding.

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15

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.

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15

Systems of polynomial equations arise naturally in many applications of mathematics. This module focuses on methods for solving such systems and understanding the solutions sets. The key abstract concept is an ideal in a commutative ring and the fundamental computational concept is Buchberger's algorithm for computing a Groebner basis for an ideal in a polynomial ring. The syllabus includes: multivariate polynomials, Hilbert's Basis Theorem, monomial orders, division algorithms, Groebner bases, Hilbert's Nullstellensatz, elimination theory, linear equations over systems of polynomials, and syzygies.

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15

This module considers statistical analysis when we observe multiple characteristics on an experimental unit. For example, a sample of students' marks on several exams or the genders, ages and blood pressures of a group of patients. We are particularly interested in understanding the relationships between the characteristics and differences between experimental units. Outline syllabus includes: measure of dependence, principal component analysis, factor analysis, canonical correlation analysis, hypothesis testing, discriminant analysis, clustering, scaling.

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15

This applied statistics module focusses on problems that occur in the fields of ecology, biology, genetics and psychology. Motivated by real examples, you will learn how to define and fit stochastic models to the data. In more complex situations this will mean using optimisation routines in MATLAB to obtain maximum likelihood estimates for the parameters. You will also learn how construct, fit and evaluate such stochastic models. Outline Syllabus includes: Function optimisation. Basic likelihood tools. Fundamental features of modelling.  Model selection. The EM algorithm. Simulation techniques. Generalised linear models.

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15

Analysis of variance is a fundamentally important method for the statistical analysis of data. It is used widely in biological, medical, psychological, sociological and industrial research when we wish to compare more than two treatments at once. In analysing experimental data, the appropriate form of analysis of variance is determined by the design of the experiment, and we shall therefore discuss some aspects of experimental design in this module. Lectures are supplemented by computing classes which explore the analysis of variance facilities of the statistical package R. Syllabus: One-way ANOVA (fixed effects model); alternative models; least squares estimation; expectations of mean squares; distributional results; ANOVA table; follow-up analysis; multiple comparisons; least significant difference; confidence intervals; contrasts; orthogonal polynomials; checking assumptions; residual plots; Bartlett's test; transformations; one-way ANOVA (random effects model); types of experiment; experimental and observational units; treatment structure; randomisation; replication; blocking; the size of an experiment; two-way ANOVA; the randomised complete block design; two-way layout with interaction; the general linear model; matrix formulation; models of full rank; constraints; motivations for using least squares; properties of estimators; model partitions; extra sum of squares principle; orthogonality; multiple regression; polynomial regression; comparison of regression lines; analysis of covariance; balanced incomplete block designs; Latin square designs; Youden rectangles; factorial experiments; main effects and interactions.

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15

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.

Programme aims

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).

Learning outcomes

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.

Intellectual skills

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.

Subject-specific skills

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.

Transferable skills

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.

Careers

The practical experience you gain with this year in industry option gives you a real advantage in the graduate job market. Through your studies, you also 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 from the School 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.

Entry requirements

Home/EU students

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
A level

AAB including Mathematics grade A (not Use of Mathematics). Either General Studies or Critical Thinking (but not both) can be accepted against the requirements.

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.

International Baccalaureate

34 points overall or 17 points at HL with Mathematics 6 at HL

International students

The University welcomes applications from international students. Our international recruitment team can guide you on entry requirements. See our International Student website for further information about entry requirements for your country.

If you need to increase your level of qualification ready for undergraduate study, we offer a number of International Foundation Programmes.

Meet our staff in your country

For more advice about applying to Kent, you can meet our staff at a range of international events.

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.

Fees

The 2018/19 annual tuition fees for this programme are:

UK/EU Overseas
Full-time £9250 £15200

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

For 2018/19 entrants, the standard year in industry fee for home, EU and international students is £1,385

Fees for Year Abroad

UK, EU and international students on an approved year abroad for the full 2018/19 academic year pay £1,385 for that year. 

Students studying abroad for less than one academic year will pay full fees according to their fee status. 

General additional costs

Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.

Funding

University funding

Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details. 

Government funding

You may be eligible for government finance to help pay for the costs of studying. See the Government's student finance website.

Scholarships

General scholarships

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. 

For 2018/19 entry, 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 information@kent.ac.uk.