The aim of this module is to enable a student to research, develop, analyse and present a project on a relevant AI/ML related problem in finance. The project is a key element of the MSc as it allows students to apply their knowledge with additional independent research to tackle a challenge in the financial services domain. The research and development project topic will be selected early in the programme based upon an identified real-world challenge within the financial services industry.

The project and dissertation module aims to enable students to design, plan and execute a research project in the general area of machine learning and finance. The module will require the student to choose and critique an appropriate research methodology and implementation framework for the problem chosen. The satisfactory completion of the project requires the student to demonstrate the ability to investigate the chosen topic independently by displaying a mastery of the literature and research relevant to the research topic and the ability to analyse and interpret findings of such readings and literature.

On successful completion of this module, students will be able to: 1. Present the self-directed research that they have undertaken into complex and emerging areas relevant to the project. 2. Deduce an implementation strategy encompassing the appropriate tools, frameworks and platforms. 3. Critique the value of the research and the implementation strategy adopted. 4. Develop an appropriate model and implement it on the targeted platform. 5. Organise the results in a coherent, logical manner using visualisation and structure. 6. Interpret the research results obtained and draw appropriate conclusions. 7. Summarise research and results in a high-quality document consistent with academic standards in terms of format, structure, language and referencing.

On successful completion of this module, students will be able to: 1. Recognise results that are of value / not of value to their research project. 2. Defend their research. 3. Question whether the data is representative and attempt to address any biases 4. Identify and discuss any significant ethical issues such as privacy, confidentiality, ownership, transparency and identity. 5. Defend their research, results and the approaches adopted. 6. Judge and challenge any limitations of the techniques adopted.

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A detailed project guideline will be given to students outlining the supervision, the project management and milestones, the deliverables and expectation. KNOWLEDGEABLE: Students develop knowledge and critical thinking as they put theory into practice through identifying and using the appropriate techniques and tools for their chosen project. PROACTIVE: Students make active use of data sets and frameworks, and in doing so develop confidence in managing both data and tools, and make informed decisions based on the scientific method. CREATIVE: Students develop their problem solution and visualisation, which requires them to integrate existing and module acquired knowledge. RESPONSIBLE: Students manage their own project, including submitting their problem statement, method and results for external scrutiny. COLLABORATIVE: Students engage regularly with their supervisory team, and work colleagues. ARTICULATE: Students will communicate technical and non-technical concepts of their project.