MN32193: FinTech
[Page last updated: 23 May 2025]
| Academic Year: | 2025/26 |
| Owning Department/School: | School of Management |
| Credits: | 10 [equivalent to 20 CATS credits] |
| Notional Study Hours: | 200 |
| Level: | Honours (FHEQ level 6) |
| Period: |
- Semester 2
|
| Assessment Summary: | CWOI 40%, CWRG 60% |
| Assessment Detail: |
- Group report (CWRG 60%)
- individual presentation (CWOI 40%)
|
| Supplementary Assessment: |
- Like-for-like reassessment (where allowed by programme regulations)
|
| Requisites: |
Before taking this module you must take MN22176 OR take MN20310
|
| Learning Outcomes: |
By the end of the unit, you will be able to:
- Explain the crucial role of innovation and technology in financial institutions
- Formulate an understanding about blockchain technology
- Explain the main challenges facing financial establishments in adopting new financial technologies
- Learn and implement machine learning (ML) and artificial intelligence (AI) techniques in practical financial applications
- Use and implement ML and AI techniques for asset return prediction and advanced portfolio management
|
| Synopsis: | You will develop a variety of skills from finance, data science, and programming. These skills are necessary to equip you for rising challenges faced in financial institutions.
You will explore topics such as:
1. Machine Learning
2. Artificial Intelligence
3. Blockchain & Financial Technology
4. Asset Return Prediction via Machine Learning
5. Advanced Portfolio Management with Machine Learning
|
| Content: | As new technologies such as artificial intelligence, machine learning, big data and blockchain have been applied to many areas in finance (financial forecasting, advanced portfolio management, risk management, etc.), there is an increasing demand for finance professionals with the skills and knowledge related to fintech.
Key elements to be included:
- Introduction and History of Fintech
- Fintech, blockchain technology and financial services
- Challenges of financial technology
- Machine learning (ML) and artificial intelligence (AI) techniques (shrinkage and selection operators, deep learning) and their practical implementation
- Implementing ML & AI techniques for asset return prediction
- ML & AI techniques for advanced portfolio management
|
| Course availability: |
MN32193 is Optional on the following courses:
School of Management
- UMMN-AFB10 : BSc(Hons) Accounting and Finance (Year 3)
- UMMN-AKB02 : BSc(Hons) Accounting and Finance with Year long work placement (Year 4)
|
Notes: - This unit catalogue is applicable for the 2025/26 academic year only. Students continuing their studies into 2026/27 and beyond should not assume that this unit will be available in future years in the format displayed here for 2025/26.
- 好色tv and units are subject to change in accordance with normal University procedures.
- Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
- Find out more about these and other important University terms and conditions here.
|
