MSc Financial Technology (Finance)

University of Essex

Your dissertation is the culmination of your Masters programme, where you apply the knowledge gained on your course to produce a 10,000 word independent piece of work. You generate an original idea, analyse literature and current knowledge and produce a coherent argument that shows your ability to relate theory to practice. Your dissertation will help you develop vital skills for the workplace, as well as the ability to analyse real problems through an academic lens. You're assigned a supervisor for the duration of your project.

Equip yourself with principles of allocation and mechanism design from an operational perspective. Auction design and market microstructure of the stock market, liquidity provision in electronic financial markets such as dark pools, and capital adequacy of centralized clearing platforms are some of the specific applications that will be studied in the first part of this module. During the second part, you will be introduced to complexity economics of self-organisation, network modules, and strategic proteanism. Finally, you'll use network models to study economic interactions.

Big data - where datasets are so large they cannot be processed using traditional techniques – is useful to financial organisations. This module explores how to analyse big data and covers areas such as predictive analytics, risk modelling and corporate finance. You also learn about the application of data analytics in high frequency finance, fraud and personal finance.

Financial technology (FinTech) is rapidly changing the financial services industries. The module provides an introduction to FinTech by analysing its impact on major banking and financial activities. Specifically, the module illustrates the effect of FinTech on lending activities (P2P lending, crowdfunding) and credit evaluation tools (big data, machine learning and artificial intelligence tools), investment activities (trading and robo-advising), payments (crypto currencies and new payment tools), insurance (InsureTech). In final, the module analyses the topic of ethics and regulation of FinTech.

This module provides you with an understanding of non-time-series data analytic approaches in finance. It covers methods for cross-sectional, panel and qualitative analysis and their applications whereby all topics are illustrated with relevant examples. Cross-sectional data are organised over individual groups (eg households, firms or countries) and have no time dimension. They may include discontinuous data (eg binary), qualitative or categorical data and are essentially non-numerical. Examples include: survey responses, textual analysis of social media or interviews. Panel data or longitudinal data are multi-dimensional data streams involving measurements over time. As such, panel data consists of researcher's observations of numerous phenomena that were collected over several time periods for the same group of units or entities. For example, a panel data set may be one that follows a given sample of individuals over time and records observations or information on each individual in the sample. The nature and advantages of panel data has led to numerous applications in finance and economics research.

This module covers topics in mathematics that are required in Masters-level finance courses at the University of Essex. You focus on the basics of linear algebra, differential calculus including optimisation and dynamics.

Build the academic, research and professional skills needed to succeed at postgraduate level and beyond. This module supports your transition to master’s study by developing independent learning, research and critical thinking skills, alongside ethical and effective use of AI as a learning tool. You’ll also enhance your employability and professional practice, gaining the confidence and capabilities required for graduate-level roles and advanced academic work.