The individual coursework is one of the two component of the module mark and its weight is 60% of the total mark. Coursework must be at least 35%. The individual report should be based on a case study on relevant topic.
The intended learning outcomes are that in your coursework you should be able to:
1. Demonstrate a deep and systematic understanding of a wide range of current AI methods, tools and their application in financial services.
2. Display mastery of current AI developments in the financial services, employing advanced skills to conduct research in these areas.
3. Critically develop responses to the ethical implications and practices of AI in different areas of the economy, government and society.
4. Flexibly and creatively solve complex business and financial problems by critically selecting and evaluating appropriate AI methods.
5. Autonomously use Python programming software to process, analyse and extract meaning from natural language, images and numerical data for a critical analysis and evaluation of outcomes and insights. Marking Criteria/Rubric The maximum total marks for this presentation is . On Moodle, you will find an Excel file (“Rubric”) under the Assessment Tab which details the criterion that will be used to mark your submission.
Students are advised to consult the University Regulations 1 and their course handbooks regarding the penalties for and definition of plagiarism, which essentially is the deliberate and substantial insertion in your own work of material from someone else e.g. a published source such as a book or article, or simply another students piece of work, without acknowledging the extent or source of the quotation. Coursework Submission Deadline: See Moodle Submission Link. Expected return of feedback and marked work: 14 working days from the deadline Details of the task
• Case studies focus on real-world problems and decisions companies face.
• You should consider what a case study is when choosing the subject of your individual report. For example, you can develop further the problems or decisions covered in the proof-of-concept developed on M176LON - Risk and RegTech, or you can use any other case study in the financial services.
• You should focus on the five intended learning outcomes when writing your individual report and cover them all in your individual reports.
• You should clearly identify where these five intended learning outcomes feed into your report as you will be marked against these criteria (see “Rubric” in the assessment section), which are aligned with the Coventry University Education Strategy 2015-2021. Some general guidelines on how to align the five intended learning outcomes with the individual report:
1. After identifying a real-world problem or decision a financial services company would have to tackle as your case study, you should demonstrate your understanding of AI methods and tools (big data, blockchain, machine learning) and their application in financial services, based on the lectures and seminars materials, and your research.
2. Your individual report should also display mastery of current AI developments in the financial services where your approach should be correctly contextualised, based on the lectures and seminars, and you are encouraged to access current research in these areas and move towards greater independent learning.
3. AI can have ethical implications and practices in different areas of the economy, government and society, and you should demonstrate your understanding about the need to adopt responsible and ethical practices and how you do it in your individual report.
4.Your case study should demonstrate originality and self-direction in solving complex business and financial problems, and you should critically select, evaluate and justify the most appropriate AI method (or methods) to your particular case study (for example, implementation of an algorithm using supervised, unsupervised or reinforcement learning).
5. Lastly, you should use Python and the algorithms covered in the seminars and in your research for a critical analysis and evaluation of outcomes and insights regarding your case study. For example, you should use a simple supervised, unsupervised or reinforcement learning algorithm to help tackling the specific real-world problem identified in your case study