FinTech with Business Analytics MSc

This module offers a detailed coverage of global financial markets, institutions and their services and securities. In conjunction with the theory developed in the taught module, you will be able to gain extensive exposure to the Bloomberg-based financial markets facility available in the Financial Markets Suite (FMS). The module provides practical financial market skills to complement the theory delivered in classes. Having established an overview of the financial environment, the module considers the role of key central banks and monetary policies in the price development of various financial markets. The role of the regulatory bodies is also considered together with the role of major institutional investors in the financial services sector.

This module will give you a deep understanding of how conventional and big data sets can be used with a variety of standard industry-based mathematical and statistical data analytic tools to areas of business and financial management. You will learn both quantitative and qualitative methods, starting from descriptive statistics to the development of learning in advanced statistical analytic methods for effective business and financial decision-making. The techniques in this module will build the knowledge required for predictive analytics work as you progress to other analytic modules in this degree. You will learn how to use statistical packages (SPSS/SAS) and to select from the range of analytic techniques to analyse current data accessible via Bloomberg or Data Stream.

This module builds on the basic programming elements and analytical techniques required for using computational methods in finance. You will develop advanced learning of software (Python) and learn how to become effective in solving specialised and complex problems in finance, by using traditional and novel methods. You will build core blocks of programming logic and establish use of computer models for finance applications. You will work on projects to utilize the open source software tool Python to undertake a variety of tasks and projects to make effective decisions in finance and accept responsibility for actions. You will learn how to apply numerical analysis and programming in Python mainly to solve a wide range of pricing and risk management problems under simulated conditions. You will be introduced to some powerful libraries in Python Scientific Manipulations (SciPy); Data Structures (NumPy); Graphics (Matplotlib); Data Analysis (Pandas) etc.

This module will provide you with a rigorous introduction to the key aspects of FinTech and will help you understand the transformational impact of this on the global banking sector. You will develop a detailed understanding of the technological developments in the banking sector and the opportunities and challenges created by these from bank payment processing to Algorithmic trading in hedge funds. In the module, you will learn about FinTech and cover topics such as crypto-currencies, block chain technology and artificial intelligence.

Students will be taught the theory behind Blockchain that will enable them to develop a deep understanding and learning about the design rationale of Blockchain technology, its emerging platforms and applications. You will learn how to flexibly and creatively apply knowledge of Blockchain to identify current and novel approaches, identify its limitations and develop critical responses to find new opportunities enabled by its applications, particularly as it relates to finance. You will design and undertake substantial investigations to enable you to enhance existing BC technology and to address issues when applying blockchains. The original design of blockchain focused on the cryptocurrency “Bitcoin”. Users now find many applications that are not just confined to cryptocurrency markets. You will gain deep understanding of how traditional and new types of cryptocurrencies are created, transacted and stored safely.

Machine Learning and Artificial Intelligence are going to change the speed and effectiveness of decision-making process in financial institutions. You will learn how to evaluate the impact of AI and ML in asset management and wealth management. You will build a solid foundation in AI, Big data and machine learning, which will allow you to make better decisions by using these novel techniques. You will gain detailed knowledge and advanced learning of a range of AI/ML techniques to solve problems or to undertake projects in capital markets, asset portfolio and risk management contexts.

This module will build on the learning acquired from Business Analytics to use advanced statistical and machine learning methods to predict future or unknown outcomes in finance. You will be taught about data mining methods and decision-support for complex tasks in a variety of financial services contexts (e.g. corporate finance, forecasting in capital markets, credit risk, fraud detection and in asset management). The module is also designed to develop models for research and enquiry. We will emphasise importance of deeper learning of this subject area by adopting a series of sound methodological steps and to provide them with an artillery of modelling and prediction methodologies with hands-on experience in applying them in complex and unpredictable and/or specialised financial services contexts.

Project (FinTech with Business Analytics) 
This module will give you the basis for applying theories studied in different modules and undertaking intensive research in a chosen area. The Research Methods learning is embedded in each core module delivered on the degree.

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Work Placement Project 
This core-option module is only for students who have secured work placement with an external organisation that is in financial services or has interests broadly related to this subject area and level of study. You will have an opportunity to get valuable career skills to make you work-ready for a future in this exciting world of finance. Further, this experience will enable you to build your network and gain real-life experience in the field. The maximum duration of this placement is 12 weeks over the Summer period (semester 3) from June to end of August. Placements have to be secured by students independently by week 8 in the first semester of this 12-month programme of study. The University’s Careers and Employability Service may be able to support students in their search and application process. All placements secured by students will need to be approved by your Course Leader. There will be a cut-off point for taking this module and those who can’t secure placement with an employer will automatically be offered an alternative Core-option Research Project.

The module discusses how to manage the volume, velocity and variety of Big Data, SQL and noSQL databases, and touches on issues related to data governance and data quality, including regulatory challenges.

This module will give you a rigorous introduction to high-frequency finance and empirical market microstructure of electronic financial markets. It will examine the problems and data sets that arise in financial industries and the models and business requirements of financial businesses. The module includes hands-on practical analysis of financial industry-specific analytic data sets using the methodologies learned earlier in the programme.