Master of Science - Financial Technology (Computer Science)

To be competitive in the modern financial industry, it is not enough for a scientist to know how to write software, nor is it enough that one can mathematically analyze markets based on simplifying assumption by ignoring the computation required. The University of Essex MSc Financial Technology (Computer Science) course give students the understanding of the specifics and the intricacies of financial and economics markets, the ability to apply advanced computing methods and the ability to design and program solutions that successfully exploit the synergy between finance, economics, and computation. The course will cover areas including:

• financial markets • big data analytics • blockchain • AI • python programming'

This course will give students both the theoretical and technical skills that are specific for the finance industry, as well as transferable skills such as the ability to develop and present an argument, and the ability to work independently and in groups. The MSc Financial Technology suite of courses has three unique variants for students to explore so they can tailor their Masters in financial technology to suit their interests and goals. In addition to the MSc Financial Technology (Computer Science) course, there is also the option of MSc Financial Technology (Finance) and MSc Financial Technology (Economics), giving students the chance to specialize in their preferred area of study.

This course is taught in collaboration with the Centre for Computational Finance and Economic Agents (CCFEA) within the School of Computer Science and Electronic Engineering, the Essex Finance Centre (EFiC) within Essex Business School, and the Department of Economics, all of which have an international reputation.

The University of Essex brings together experts with both academic and industrial expertise in the financial and IT sectors. The staff are currently researching the development of real-time trading platforms, new financial econometric models for real-time data, the use of artificially intelligent agents in the study of risk and market-based institutions, operational aspects of financial markets, financial engineering, portfolio and risk management, among others.