Many optimisation problems in business and industry can be expressed in the form of a linear programming problem and this is even more apparent with increasing reliance on Artificial Intelligence and Machine Learning. Businesses and industry use linear programming to determine what to make in order to maximise their profits, Amazon use it to schedule your parcels for delivery and it is also used widely in Game Theory: you can use it to beat your friends at rock-paper-scissors and other games!

In this module, we will study the theoretical background behind the linear programming methods, learn how to express real-world questions as linear programming problems and solve them by hand and using computer programs. We will also explore some other optimisation methods used in AI and Machine Learning. Topics may include:

• Canonical forms of linear programming problems.
• Theoretical considerations: relevant results from set theory and geometry.
• Integer linear programming.
• Solutions of linear programming models: Simplex and dual simplex methods, Pivot algorithm, and computer-based techniques.
• Degeneracy, cycling, duality.
• Use of a mathematical computer software package, for example, Python, Matlab, Excel etc.
• Application to logistics in transportation and assignment problems, VAM and the Hungarian algorithm.
• Game theory: zero-sum matrix games, multi-phase games.
• Non-linear optimisation techniques in Machine Learning

Stochastic processes serve as essential mathematical models for systems and phenomena exhibiting apparent randomness. Examples encompass diverse scenarios, such as the growth of a bacterial population, fluctuations in electrical current due to thermal noise, or the motion of gas molecules. The applications of stochastic processes span various disciplines, including biology, chemistry, ecology, neuroscience, physics, image processing, signal processing, and financial markets. In order to understand such random behaviour, we introduce and study Markov chains, random walks, Brownian motion and stochastic differential equations. Through these topics, students will not only establish a robust foundation in the principles of stochastic processes but will also gain valuable insights into their diverse applications across numerous domains. The module's goal is to equip students with analytical tools essential for comprehending and modelling complex uncertainties, thereby enhancing their capacity to address real-world challenges in mathematics, statistics, and related fields.

Topics covered include:

• Brief review of Probability Theory via a Measure Theory approach.
• Martingales: Basic definitions, filtrations, stopping times.
• Doob's Martingale inequalities and Convergence Theorem.
• Markov Chains.
• Moment generating functions.
• Characteristic functions.
• Probability generating functions.

The intention of this module is to equip you with the key digital skills required to enhance the understanding of key concepts covered on this course. There will be special emphasis on the operationalisation of the concepts at the work environment, that is, how can technology be used to efficiently implement and enhance the level of productivity of the organisation. The operationalisation agenda required to be actioned from the perspective of the finance manager in using key technologies will the direction of the module. The expectation is that you will develop a broad and holistic understanding of the finance functions in the digital world. You will familiarise yourself with how technologies links various operations of the business activities of an organisation to enhance its value creation. To achieve the intentions of the module, the following indicative contents will covered:

• Data analytics (sources of data, descriptive analytics, predictive analytics and prescriptive analytics)
• Data visualisation techniques.
• Accounting software and information systems.
• Artificial intelligence and automation.
• Emerging technologies within accounting and finance
• Cybersecurity within accounting, finance and banking
• Ms Excel for accounting and finance (Analysis ToolPak and Solver, Pivot Tables-including Power Pivot and Power Query, logical functions - lookups, text functions and financial maths with Ms Excel)
• Decision trees and experimentation

The coverage of the above contents will provide a certain degree of mastery in the handling of technologies you require to become a competitive graduate. The value associated with the use of technology to implement key concepts within the accounting, finance and banking courses is immeasurable. For example, the provision of an experiential learning environment will enhance your employability skills thus making you a competitive graduate. 

This module aims to gain understating of credit risk and sustainability in the banking context. It includes understanding of financial difficulties for customers and clients leading to collections and recoveries and losses, and demonstrates the importance of policies and procedures and the PRA requirements on capital adequacy and liquidity for banks. It also covers the growing importance of the societal purpose and practices of sustainability in the banking industry including description of how the types of sustainability today change the way we operate in banking and identifying the key performances indicators in banking that reflect sustainability.

Students will be able to achieve the following learning outcomes by the end of this module:-

1. Understand how credit arrears are formed and the wider impact on the profit and loss account
2. Critically evaluate the regulatory framework within the banking sector and understand how losses, capital adequacy and liquidity have been determined to ensure compliance with PRA regulations.
3. Understand the importance of sustainability in banking including culture, conduct and ethics. 
4. Analyse the relationships between businesses and stakeholders with regards to sustainable practice
5. Interpret the principles and types of sustainability and assess how sustainability is measured within banking organisations.

Indictive contents:-

• Financial analysis of credit losses.
• Understanding arrears and losses.
• Collections and recoveries practices.
• Policies and procedures.
• Financial conduct authority policies and regulations in banking and finance including PRA, capital adequacy and stress testing.
• Why sustainable finance, ethics and conduct are important in banking.
• Business and its stakeholders.
• Principles of sustainability.
• The types of sustainability (human, social, economic, environmental).
• Finance practices and KPIs reflecting sustainability.
• Industry case studies.

This is an experiential learning opportunity that incorporates, 20 teaching contact hours/lectures to prepare for the150 contract hours where L5 students can use all their skills learned to date on an actual real-world (external business) client driven project, working in teams and produce an artefact. 

Students are also expected to undertake around 30 hours of self study.

This module not only gives them enhanced skills but the opportunity to work for a real client thus giving them a valuable CV and LInkedIn entry as work experience that can contribute to their employability portfolio. 

Students will collaborate in teams and produce full client documentation alongside a reflection of their expereince and this all give some much needed contemplation of their skills to date and how to use them. 

This module provides a structured, university-level work placement for 4, 5 or 7 weeks as one continuous block / period with a placement provider (i.e. industry apprioprate 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 4, 5 or 7 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.