Financial Technology (FinTech) and Business Analytics (with optional Placement/Project year) MSc

The aim of this module is to equip students with a comprehensive understanding of the role of Artificial Intelligence (AI) in the field of Finance and Analytics. By the end of the module, students should possess the knowledge and skills required to harness AI's potential in various financial applications, ranging from real-time market analysis and algorithmic trading to natural language processing (NLP), computer vision, and ethical considerations.

Students will be able to apply AI techniques effectively to enhance business decision-making, risk management and wealth management, while also staying aware of the evolving trends and ethical implications in AI.

The intention of this module is equip students with the skill set of analysis data to enhance the quality of financial decision making. There is solid evidence to suggest that for you to competitive as a finance, fintech, banking or accounting graduate, you must possess the skill of data story telling - analyse and interpret data to justify decision made by the organisation. It is therefore not surprising to see that jobs advertised within your course area tend to include the skill of data analysis as an essential criteria in most cases. This module seeks to address the specific need of quantitative data analysis skills captured in all subject benchmarks within the subject grouping of your course. Therefore, to help you develop these set of skills, the following indicative contents will be covered in this module. 

• Data - sources of financial data collection, instrumentation for collecting financial data, databases such as FAME-Financial Analysis Made Easy (FAME), Yahoo Finance, Investing.com, Ms Office 365 (powered by Refinitiv). 
• Techniques for cleaning data for further analysis - missing data and removing text from data in Ms Excel
• Introduction to software for analysis data - for example, MS Excel, SPSS, STATA and AMOS. The use of Analysis ToolPak and Solver in Ms Excel. 
• Descriptive statistics - measures of central tendency - arithmetic, geometric and harmonic means, median and mode. Measures of dispersion - standard deviation, variance, quartiles, coefficient of variation, covariance, skewness and kurtosis.
• Hypothesis testing, the null and alternative hypotheses, 1-tail and 2-tail, levels of significance and confidence intervals.
• Predictive analytics (inferential statistics) - forecasting techniques - correlations, regression, t-test, time series analysis (auto regressions and moving averages).
• Applications of quantitative techniques for investment appraisal analysis, portfolio optimisation, Monte Carlo simulation, currency dashboards, market risk estimation (beta).
• Data visualisation techniques using advanced Ms Excel.

The coverage of these key data analytics techniques will give you the confidence to contribute to financial decision making using available data. Note that, you will cover these topics in an experiential learning setting - full participation using your laptop or assigned computers to examine each of the topics. There is an expectation that the confidence gained through the manipulation of data with software and key statistical concepts will give you the competitive edge along your career. 

Programming skills are essential in Business Analytics as they empower professionals to manipulate and analyse data efficiently, develop sophisticated models, and extract valuable insights to make informed decisions and drive business success.

The aim of this module is to equip students with the essential knowledge and practical skills required to excel in the field of business analytics. By the end of this module, students should be able to proficiently programme in Python, apply rule-based patterns, perform data grouping and segmentation, implement data categorisation methods and models and leverage machine learning techniques to analyse and extract valuable insights from business data.

This module will prepare students to be competent and effective Business Analysts capable of addressing real-world business challenges through data-driven and technologically advanced solutions.

This module develops students' research skills via studying various research methodologies and methods including how to collect and analyse quantitative and qualitative data in accounting and finance. 

Indictive contents include:-

• Developing the research idea (case study, business development proposal and dissertation)
• Theory, literature and hypotheses 
• Research ethics in accounting and finance
• Data collection, sources, measurements, cleaning, summary (descriptive statistics)
• Big Data (4Vs - Volume, Velocity, Veracity and Variety)
• Inferential statistics (predictive analytics)
• Quantitative and qualitative research approaches
• Software - Power BI, SPSS, AMOS, Advanced Ms Excel, Stata, NVivo and others
• Specific techniques for case study, business development proposal and dissertation. 

This module involves an independent study and a research project in accordance with established research principles involving the completion of the following elements:

• Introduction to Accounting and Finance research
• Critical review of literature for an Accounting and Finance research project
• Evaluation of appropriate data collection methods
• Data analysis techniques for various research strategies
• Resource and risk planning in research
• Undertaking Accounting and Finance research

The module is your independent final project, similar to a dissertation.

In this module, you'll take the lead in creating a comprehensive business proposal. You will begin by researching the legal requirements necessary to start a business and then move on to developing its strategic direction, including defining its mission, vision, and identifying valuable market opportunities.

You are expected to incorporate financial details, exploring funding options and developing financial forecasts. Your project also involves conducting a thorough stakeholder analysis, honing your networking skills, and pitching your business idea to potential investors through the report.

This module places a strong emphasis on ethical and sustainable business practices, challenging you to incorporate these critical elements into your proposal. Through this project, you will demonstrate your ability to synthesise and apply your learning to develop a well-rounded, strategically sound business proposal based on rigorous research and comprehensive analysis. This is your opportunity to showcase your entrepreneurial skills and innovative thinking in a real-world context.

This module provides opportunities for students to solve business case studies (including live cases) and justify the solutions that have been put forward from an available pool of different solutions and scenarios. 

Indicative contents:-

• Introduction to Case Study Analysis – mini, macro, and integrated business case study
• A modelled approach to case study analysis for decision making
• Assessing the challenges within the live case study
• Problem Identification & Analysis
• Assumption setting and strategic business planning related to case study
• Objectives, strategies, action plans, by function to time scales
• Application of key quantitative and qualitative techniques
• Monitoring, review & control planning for performance
• How to write a case study report
  • Pitfalls of case study analysis