Topics include data analysis, the principles of actuarial modelling, the description of financial transactions; the understanding of real and nominal interest rates, the time value of money, the present value and accumulated value for a given cashflow, the term structure of interest rates; the duration, convexity and immunisation of cashflows; the equation of value and its usage to solve various practical problems, project appraisals.

Topics include: measures of investment risk, portfolio theory, models of asset returns, asset liability modelling, equilibrium models, the efficient markets hypothesis, stochastic models of security prices, and Brownian Motion and its application.

This subject aims to provide students with grounding in advanced financial mathematics, covering option pricing under the binomial model; risk‐neutral pricing of derivative securities; Brownian motion; introduction to Itô΄ formula and SDEs; stochas􀆟 asset models; Black‐Scholes model; arbitrage and hedging; interest‐rate models; actuarial applications and simple models for credit risk.

Topics include survival models concepts; estimation procedures for lifetime distributions; multiple state models; multiple decrements; binomial and Poisson model of mortality; actuarial applications of continuous‐time and discrete‐time Markov processes; exact and census methods for estimating transition intensities based on age.

This subject examines auditing and assurance services at an advanced level. It is designed to facilitate detailed analysis of complex situations auditors face in contemporary practice. Instruction is carried out by industry practitioners. Topics covered include the use of data analytics, engagement risk and practice management, and auditing asset and liability valuations. Students will have the opportunity to deepen their understanding of contemporary assurance practices.

Topics include distributions of accumulations and present values; stochastic interest rate models; time series models; an introduction to ruin theory; claim run-off triangles; stochastic simulation.

This subject has two primary aims:

To provide fundamental principles of actuarial modelling.

To discuss techniques used to model and value cashflows dependent on death, survival, or other uncertain risks.

Topics include loss distribution with and without risk sharing; collective risk model, calculation of moments and moment generating function of aggregate claims, recursion formulae, effect of reinsurance; individual risk model, recursion formulae and approximations; copulas; extreme value theorems; time series.

This subject has two main objectives. Firstly, it is designed to enable students to have the ability to analysis and interpret the financial statements of companies and financial institutions. Secondly, to provide a basic understanding of corporate finance including a knowledge of the instrument used by companies to raise finance and manage financial risks.

This subject aims to further develop students’ knowledge of modern analytical tools and techniques, including GLM, shrinkage techniques (e.g., LASSO and ridge regression), tree-based methods (e.g., random forests and GBM) and neural networks. It also teaches students to connect data analytics work to the actuarial control cycle and real-world business environments. Effective communication of findings to a range of business decision making audiences is also stressed.

Topics include insurance markets and products; underwriting and risk assessment; policy design; actuarial modelling; actuarial assumptions and feedback; reserving methods.

This subject introduces core economic principles and how these can be used in a business environment to help decision making and behaviour. It provides the fundamental concepts of microeconomics that explain how economic agents make decisions and how these decisions interact. It explores the principles underlying macroeconomics that explain how the economic system works, where it fails and how decisions taken by economic agents affect the economic system.

This subject aims to provide students with basic training on modern data analytics methods, which include linear regression, classification, resampling methods, spline-based methods, generalised additive models and tree-based methods. This subject focuses on applying the above methods to modelling non-life insurance claims frequency and severity.

Topics include multiple linear regression; Spearman´s and Kendall´s measures of correlation; principal component analysis; generalised linear models; bootstrap method; Bayesian statistics; credibility theory.

Topics include assessment of solvency; analysis of experience; analysis of surplus; actuarial techniques in the wider fields; and an introduction to professionalism.