FinTech with Data Analytics BSc Honours

The rapid growth of data and its increasing complexity require businesses to adopt efficient computing solutions for data management and analysis. This module equips you with essential skills to design and implement data-driven applications that support business operations, including planning, customer management, and decision-making. You'll learn to work with modern tools such as Python, SQL, and Excel for data processing, storage, and retrieval, gaining hands-on experience in solving real-world business problems.

The module provides you with knowledge and an understanding of finance, financial markets and institutions to build financial and digital literacy skills. You'll acquire knowledge of money management, debt and credit, budgeting, saving and investing. Hands-on training will be provided on the Bloomberg database and you will explore how Excel skills can be applied to solve a variety of financial problems. You will also progress to understand the role of the central banks, functions of different types of financial institutions and financial securities, namely: equity, currency, money markets and fixed income securities.

This module provides you with knowledge of the fundamental concepts and analytical tools required for exploring current practices in finance. Emphasis is placed on developing an understanding of contemporary theoretical frameworks and their applications to a broad range of business problems faced by corporations and investing organisations.

The module equips you with the necessary mathematical and numeracy skills required for this finance degree. It is designed to provide you with a sound knowledge of mathematical techniques and their applications in different areas of finance, such as investment decisions, financial markets and risk management. Topics covered include: basic algebra, functions and graphs; solving systems of equations; exponents and logarithms; financial mathematics (time value of money); linear algebra (matrices); differentiation and partial differentiation; unconstrained and constrained optimisation.

This module develops an understanding of the historical perspectives of FinTech. Theories, design and structure of each of the core elements of a FinTech macrostructure will be examined, namely: Artificial Intelligence, Blockchain, Cloud Computing and Big Data. We aim to explore the nature of the disruption to businesses from these technologies and the emergence of new business models in financial services. The study is grounded in theory, with practical examples to demonstrate the role and relevance of each element in the FinTech ecosystem.

This module introduces you to how Entrepreneurship has taken place in the FinTech sector. You will learn about the various business models that ventures in FinTechs adopt. You will study the entrepreneurial mindset, how ideas can be generated and how you can develop a suitable business model canvas with a viable value proposition. You will explore FinTech organisations, their valuations, management and control features. You will also examine how start-up FinTechs are financed from start-ups to their full Initial Public Offer (IPO) listing stages on a public stock exchange.

This module will develop a deep knowledge of well-established financial, computational and statistical theories and methods for solving a variety of business problems. Learning will lead to the synthesis of the knowledge base and skills to demonstrate how statistics, databases and software packages and libraries (Excel, Python etc) are being used to calculate, model and visualise financial data to inform business decision-making.

A study of financial market microstructure examines how markets are organised and function. The financial market theory focuses on the structure of the asset exchanges and trading platforms (including knowledge of displayed and dark pools), price discovery processes and the determinants of spreads and quotes, intra-day trading behaviour, and transaction costs. Your learning will enable you to gain insights into questions such as market efficiency, market stability, and the sources of volatility. With the knowledge and skills acquired in this module, you will be able to understand how to design efficient trading strategies and improve estimations of risk exposures.

This module provides the basic understanding and knowledge of statistical and econometrics techniques commonly used in financial markets. It aims to develop an understanding of financial theory and to teach students how to apply different financial and analytical techniques to solve a range of problems in the following contexts: financial markets, investment decision-making and risk management. Emphasis is placed on developing a working knowledge and understanding of the following methods: probability and probability distribution, summary statistics, regression analytics, time series analysis and their uses and applications in finance.

This model provides learning about the principles and applications of Cryptocurrencies and Blockchain Technology. You will be taught how to use relational and non-relational database management systems (SQL and NoSQL). The module provides hands-on skills in the areas of data acquisition, cleansing and loading. Leveraging this knowledge base, you'll move on to examine Blockchain technology and relate this learning to applications in Cryptocurrencies. Building upon this technology, you'll develop a deep and systematic understanding of the challenges and opportunities offered by three types of Cryptocurrencies, namely: Bitcoin,//Litecoin and Ethereum. You will also explore novel applications of Blockchain Technology to other use cases in Financial Services.

This module explores how to effectively manage the range of operations functions drawing from the traditional and digital perspectives. It considers the business process; the supply chain; operational strategic alignment; systems integration; and the management of information, quality, demand, capacity and resources. It covers aspects of electronic and mobile services, commerce and business; sustainability; innovation and design in products and services; and developments in technology and systems.

In this module, you'll analyse and evaluate the rapid proliferation of data, with a focus on the fundamental principles of big data management. The course will develop a deeper understanding of the key characteristics of big data – namely, volume, variety, velocity, veracity, value, and variability. The size and complexity of big data present significant challenges in storage, search, sharing, transfer, analysis, and visualisation. You'll critically examine the reliability, validity, and significance of technologies designed to address these challenges, such as SQL, Hadoop, and Apache Spark. You'll also gain an in-depth understanding of data mining and its applications – such as business intelligence and recommender systems – within a financial context.

This module provides you with advanced econometric and financial modelling techniques essential for rigorous data analysis in FinTech environments. Building on foundational statistics, you'll master sophisticated econometric methods, including panel data analysis, advanced cross-sectional models, instrumental variables, difference-in-differences, and causal inference techniques.

The module emphasises the practical application of advanced econometric methods to address real-world FinTech research questions, policy evaluation, and business strategy decisions using cross-sectional and panel datasets. You'll learn to handle complex financial datasets, address endogeneity issues, and conduct robust empirical analysis using appropriate statistical software. Topics include event studies, structural modelling, and advanced cross-sectional econometrics, specifically tailored for financial technology applications.

The module provides you with learning in advanced mathematical and statistical models for analysing large datasets within business and finance contexts. Techniques taught in this module will enable you to critically analyse and evaluate the reliability and validity of information and data. You will learn how to select from a range of mathematical models to forecast future trends and optimise decisions for business and finance. The use of Python and/or R programming languages constitutes an integral part of the course, allowing you to gain hands-on experience in data analysis and the use of modern computational tools to determine patterns and predict future outcomes and trends. Predictive methods will include time series analysis, regression analysis, forecasting techniques, simulation, and data mining.

The module is designed to provide a comprehensive and detailed knowledge of investments and an understanding of modern portfolio theory. Particular emphasis on the module is to develop a critical understanding of the nature and organisation of common financial investments, namely money market instruments, equities and government and corporate bonds. You will acquire critical skills that will allow you to select securities and assets for inclusion in portfolios. The module will also provide details on investment objectives and professional codes of practice.

This module covers some of the main analytical models used in Machine Learning (ML) applications. The ML methods will include regression, classification, and clustering, as they are commonly used in supervised and unsupervised learning systems. You will learn how FinTech is evolving and how analytical techniques emerging from the decision sciences are being adapted to solve a variety of financial and business problems. Python-based coding, scripts and libraries will be used extensively to develop higher-order analytical skills and to integrate workplace skills required for careers in the FinTech and financial services sectors.

Digital Analytics is a fast developing area within many organisations, where there is a great need to understand and analyse the online/offline behaviour of consumers, markets and competitors and data generated from various business processes. In this module, you'll explore social media analytics, web analytics (WA) and business intelligence (BI).

In this module, you will develop a good understanding of global banking in the twenty-first century. In particular, you will be exposed to the types of banking beyond personal banking that encompass wholesale and investment banking. You will also explore international risk in banking as well as global banking regulations.