Advanced Financial Technology
This module builds on BEEM061 Fundamentals of Financial Technology, but goes deeper into exploring how blockchain, big data, machine learning and AI are re-shaping Finance. With direct input from industry practitioners, you will examine the economics and environmental impact of bitcoin mining, appraise the merits and drawbacks of existing blockchains, and examine in depth how bitcoin works by studying the bitcoin whitepaper and its protocol. New use cases that allow secure identity management and micropayments will also be introduced. On completion of this module, you will be highly proficient at critically evaluating current and future developments in FinTech, and highly competent in the practical aspects of programming in useful languages like Python.
Full module specification
|Module title:||Advanced Financial Technology|
|Duration of module:||
Duration (weeks) - term 2: |
This module aims to consolidate the analytical, technical and strategic foundation laid out on the BEEM061 Fundamentals of Financial Technology module. You will continue to an advanced level of understanding how bitcoin, and hence all blockchain and smart contract applications work. The theory behind how these systems work will continue to be applied in real case studies, and you will be trained into a competent multi-disciplinary FinTech specialist. By the end of this module you will have acquired all of the knowledge and skills needed to succeed in the industry, whether as an entrepreneur creating a startup, or as a member of a FinTech team in an existing company.
ILO: Module-specific skills
- 1. examine case study approaches to how FinTech businesses work;
- 2. critically review and discuss a range of academic and non-academic papers reflecting on FinTech.
ILO: Discipline-specific skills
- 3. analyse the techniques employed in FinTech.
ILO: Personal and key skills
- 4. demonstrate analytical and critical thinking skills;
- 5. demonstrate digital literacy and basic programming skills.
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Scheduled Learning and Teaching Activity||22||22 x 1 hour lectures|
|Scheduled Learning and Teaching Activity||5||5 x 1 hour tutorials|
|Guided Independent study||123||Reading, question practice, and tutorial preparation|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Participation in Group Work||Seminars and 20 minute report back||1-5||In-Class Verbal Feedback|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Mid Term Exam||20||50 minutes||5||Verbal feedback & Indicative Answers|
|Assignment||80||2500 words||1-4||Written feedback|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Mid Term Exam (20%)||Exam (50 minutes, 20%)||5||Referral/Deferral Period|
|Assignment (80%)||Assignment (2,500 words, 80%)||1-4||Referral/Deferral Period|
- New Developments in FinTech
- The Bitcoin whitepaper & protocol
- Big Data, AI & Machine Learning
- Intermediate Programming
Indicative learning resources - Basic reading
Gupta, P & Tham, T.M. (2018). FinTech: The New DNA of Financial Services. Walter de Gruyter Inc, Boston/Berlin.
Module has an active ELE page?
Indicative learning resources - Web based and electronic resources
ELE – College to provide hyperlink to appropriate pages
Last revision date