Economics and FinTech* BSc (Hons)

This module is designed to build upon the principles of microeconomics and macroeconomics introduced at Level 4 by developing a deeper understanding of economic theory and its application to contemporary economic and policy issues. You will critically evaluate economic models, analyse real-world economic problems, and apply economic reasoning to assess government policies and business decisions in national and international contexts. The module places greater emphasis on analytical thinking, quantitative reasoning, and evidence-based policy evaluation, preparing you for advanced study in economics, finance, and related disciplines.

Microeconomics content includes:

• Consumer choice and utility maximisation.

• Producer behaviour, production, costs, and profit maximisation.

• Market structures, competition, monopoly, and strategic behaviour.

• Game theory and firms' strategic decision-making.

• Labour markets, factor pricing, and income determination.

• General equilibrium and welfare economics.

• Externalities, public goods, asymmetric information, and market failure.

• Government intervention, regulation, taxation, and competition policy.

Macroeconomics content includes:

• National income determination and macroeconomic equilibrium.

• Inflation, unemployment, and economic growth.

• Business cycles and economic fluctuations.

• Monetary systems and financial markets.

• Fiscal policy, monetary policy, and macroeconomic stabilisation.

• Exchange rates, international trade, and open-economy macroeconomics.

• Public debt, government expenditure, and long-run fiscal sustainability.

• Contemporary macroeconomic challenges, including financial crises, inflationary pressures, inequality, and sustainable economic growth.

Content relating to the application of the above includes:

• Critical evaluation of competing economic theories and policy perspectives.

• Application of economic models to contemporary economic, financial, and public policy issues.

• Analysis of government interventions and their implications for markets, businesses, and society.

• Evaluation of the economic impacts of financial crises, globalisation, technological change, and digital transformation.

• Interpretation of empirical evidence and economic data to support policy analysis and decision-making.

• Development of analytical, quantitative, and communication skills through problem-solving, case studies, and policy discussions.

This module builds upon earlier modules in business, data analysis, or related disciplines with a view to introducing you to topics that both advance their understanding of modern technological applications and have practical use across various industry sectors. The module will be looking at contemporary, real-world applications of artificial intelligence and will try to include employer participation to keep the learning practical.

The module will include:

• AI, Big Data, and Business Datasets: Exploring how large-scale datasets are collected and analysed using AI.

• Agent-Based Modelling and Business Situation Simulation: Simulating real-world scenarios to see how different factors interact.

• Computer Vision for Trend Analysis: Using visual data and image recognition to identify market trends.

• Generative AI for Market Intelligence and Research: Applying generative tools to gather insights and conduct market research.

• Sentiment Analysis: Analysing text to understand market moods and public sentiment.

• Robotic Process Automation (RPA) in Operations: Learning how to automate everyday business tasks and workflow operations.

This module provides essential data science skills and applications tailored for undergraduate economics students. It introduces foundational and advanced econometric techniques, building on students' statistical skills and proficiency with software applications. The module focuses primarily on supervised learning, including running regressions using Ordinary Least Squares (OLS) and maximum likelihood estimation. To address different economic questions, students will complete regression analyses using cross-sectional, time-series, and panel data. The module also covers the use of instrumental variables (IV) and quasi-experimental approaches, integrated within Stata and RStudio software. Additionally, students will be introduced to other key components of data science, such as unsupervised learning, with brief explorations in Python and Google Colab.

This module seeks to: 

1. Provide students with an analytical framework for understanding complex global financial markets
2. Explore the key drivers of financial market activity (in debt, equity and FX markets)
3. Analyse available risk management strategies for market participants.  
4. Understand the role of central banks and other market regulators. 

It will cover topics such as, but not limited to:

1. The importance of financial markets in the economy through the efficient allocation of capital 
2. Financial intermediation and the evaluation of key driving factors shaping the financial markets
3. Critical evaluation of risk management strategies (liquidity, interest rate and credit risk) within financial institutions and the effect these have on profitability
4. Monetary policy, and the links to economics and financial markets. 
5. Regulation of financial markets and institutions and management of financial crises.

This module is designed to enhance student’s professional skills in a real-world job setting and is structured to bridge theory and practice, featuring engagement with industry through guest lectures, business networking and practical case study applications. It will enhance student’s practical skills and broaden their professional networks, aligning with the university's goal of preparing students for impactful graduate careers.

It will provide learners with hands-on experience of real-world scenarios in the finance sector and help them understand business expectations on them as professionals, build confidence, and prepare them for the world of work.   

The module will also enhance students’ knowledge of the graduate employment market focussing them on developing their employability skills and build a comprehensive toolkit of skills and knowledge to enable them to stand out from the crowd and achieve success in finding graduate employment.

This module provides a structured, university-level work placement for 5 weeks as one continuous block / period with a placement provider (i.e. a local employer from the private, public, or charitable sector). It is designed to enhance your professional skills in a real-world job setting.  

The placement can either be organised by you or with support from university staff.  

All work placements within this module must be university-level; this means:  

• Undertaking high-skilled work commensurate with level 5 study (e.g. report writing, attending meetings, delivering presentations, producing spreadsheets, writing content on webpages, social media, marketing services/products etc)  

• Physically placed (albeit part of it can be hybrid) within an employer setting in one continuous block / period for 5 weeks for a minimum of 140-147 hours over the course of the entire work placement 

• Where applicable, your existing part-time employer can be approached/used as the placement provider, if the high-skilled work criterion above is fulfilled for the full duration of the placement.   

• All quality assurances/agreements provided by the University are adhered to, by you and the employer.  

The work placement context may not necessarily, reflect your degree discipline per se, but rather, it will give you an enriched experience to enhance your professional skills in a real-world job setting. 

This module is designed for Level 5 students of all disciplines interested in Teaching English to Speakers of Other Languages. You will develop your language awareness and acquire a basic understanding of the communicative approach to teaching English as a Foreign Language. You will also be introduced to a range of techniques for lesson planning, teaching practise and assessment. This knowledge will be applied practically in the planning of interactive learning activities and lesson structures, utilising the loop input approach. You will also be acquainted with a variety of language learning resources. As this is a practical and developmental course, you will be required to use the techniques taught in planning and delivering short lessons, which you will reflect on and develop for future sessions.

The module will provide the opportunity to further develop your language skills, building on your previous learning at advanced level. The second half of the module includes a placement abroad or, alternatively, a project on a sustainability issue in a target language country. The first half of the module will prepare you for placements abroad where appropriate as well as a deeper understanding of sustainability in target language contexts. Students of more than one language may take one language in the first half of the module and spend their time abroad developing a different language. 

The module will provide the opportunity to further develop your language skills, building on your previous learning at beginner level. The first half of the module includes intensive taught sessions in interactive workshop mode which will prepare you for placements abroad or self-directed language development. The second half of the module includes a placement abroad or, alternatively, a project on a cultural issue in a target language country. Students of more than one language may take one language in the first half of the module and spend their time abroad developing a different language. 

• The multiple facets of global citizenship
• Ethical engagement and practice
• The United Nations Sustainable Development Goals
• Cross-cultural issues and sensitivity
• Intercultural communication
• Culture shock
• Cultural adjustment
• Self- assessment of needs: identification of the range of transferable skills, competencies and attitudes employees need and employers expect graduates to possess-with a strong focus on understanding the intercultural competencies (ICC) needed to live and work abroad.
• Critical analysis/evaluation of individual requirements in relation to culture/cultural adjustment/culture shock/visas/medical.
• Critical analysis/evaluation of skills already acquired in relation to key skills related to ICC.
• Devising strategies to improve one’s own prospects of working abroad in the future.
• Devising an action plan to address gaps in transferable skills based on organisational analysis and sector opportunities.

This module builds upon earlier modules in microeconomic and macroeconomic theory with a view to introducing you to topics that both advance their understanding of the foundations of economic analysis and have applications across a range of social science and management disciplines, and developing their criticality.  The module will be looking at contemporary applications and will try to include employer participation.

At the micro-level, the module will include:

• Market power and pricing strategies

• Investment, time and insurance

• Risk and uncertainty analysis

• Asymmetric information

• Behavioural and experimental economics

At the macro-level, the module will include:

• Trade-off between inflation and unemployment

• Dynamic aggregate supply and demand

• Understanding consumer behaviour

• The theory of investment

• Stabilization policies

• Government debt and budget deficits

• Macroeconomics and the global financial crisis

Applied Econometrics develops advanced quantitative skills for analysing economic and financial data, with a particular emphasis on financial technology (fintech), digital finance, and modern empirical research methods. The module combines econometric theory with practical applications, enabling you to evaluate contemporary issues in financial economics using real-world data and rigorous causal inference techniques.

The module explores how econometric methods can be used to investigate key questions in fintech and financial markets, including digital payments, mobile banking, peer-to-peer lending, financial inclusion, digital currencies, platform finance, artificial intelligence in finance, financial innovation, credit markets, and the economic impacts of emerging financial technologies. Through these applications, you will learn how empirical evidence can be used to inform business strategy, financial regulation, and public policy.

A central feature of the module is the identification of causal relationships rather than simple statistical associations. You will be introduced to modern causal inference methodologies widely used in economics and finance, including panel data models, fixed-effects estimation, instrumental variables, difference-in-differences, regression discontinuity designs, matching methods, and event-study approaches. The module emphasises the assumptions underlying each identification strategy and develops your ability to critically evaluate empirical research and interpret causal evidence.

Alongside methodological training, you will develop practical skills in managing and analysing large-scale datasets using professional statistical software. They will learn data preparation, model estimation, diagnostic testing, interpretation of empirical results, and effective communication of quantitative findings through applied research projects. By integrating contemporary fintech applications with state-of-the-art econometric techniques, the module equips you with the analytical and empirical skills required for careers in finance, fintech, consulting, policy analysis, and economic research.

This module builds upon earlier modules in programming, data analytics, and foundational finance with a view to introducing you to cutting-edge tech topics that both advance their understanding of modern financial systems and have direct applications across digital platforms, quantitative analysis, and tech-driven decision-making. The module will focus heavily on contemporary, practical applications of AI and data science, and will actively try to include industry and employer participation.

The module will include:

• Deep Learning for Intelligent Prediction – Utilising advanced techniques to forecast large-scale business trends and market movements.

• Data Mining and Structural Pattern Recognition – Identifying hidden regimes and structural shifts within massive business datasets.

• Algorithmic Dynamic Modelling for Digital Economies – Simulating, testing, and building systems for fast-evolving digital markets and web-based platforms.

• Reinforcement Learning in Decision-Making – Training intelligent AI agents to execute optimal strategies and automated financial choices.

• Vibe Coding and Quantitative Behavioural Analysis – Using rapid, LLM-assisted development (vibe coding) to track, model, and capitalise on market sentiment and user behaviour.

This module is going to focus on primary, secondary and mixed data and how using data analysis can support decision making. It will equip students with essential skills to analyse, interpret, and present data effectively in professional settings. Equip students with the relevant practical skills through the application of advanced Ms Excel techniques and SPSS usage, to analyse variables to address decisions. Provide students with the critical skills required to collect and analyse data; and its presentation by means of relevant methods. 

Data - databases (such as FAME, Yahoo Finance and Investing.com), types of financial data with emphasis on big data, ethics in data handling, annual reports and other source. Data in SPSS - Entering data into SPSS, measuring and setting variables. 

Data mining - Data Visualisation Techniques in Excel-creation of dashboards using pivot tables, lookups and logical statements, use financial ratios to conduct performance analysis of companies with data on available databases.

Data Collection - Exploring techniques and tools for primary data collection and methods to analyse for understanding and presentation. Transferring and exporting collected data into Excel and SPSS for data analysis. 

Financial Statistics in Excel - use the 'Analysis ToolPak' -  and SPSS, descriptive statistics (mean, median, mode, standard deviation and variance), Anova (single factor, 'with and without' replication), correlations, regressions, T-Test, and Excel solver (for linear programming).

Financial Markets - market sensitivity (beta estimation), portfolio diversification (variance and covariance matrix), market reaction to announcement (event study-single and multiple events), market efficiency (use time series models such auto-regression and moving average concepts).

The module is designed to enable students to develop and apply business research and analytical skills that will enhance their employability and rapid progression to management positions in the workplace. It requires them to: 

• Undertake a research project that is 'scoped and framed' within a required degree programme of study.
• Design and conduct appropriate in-depth research in an elected area of study.
• Acquire, consolidate and apply theoretical knowledge, methodologies, and research approaches in a ‘real world’ environment.
• Develop and utilise skills in critical investigation; analysis and synthesis of evidence; reflection and autonomous learning.