Loughborough University
Leicestershire, UK
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Loughborough University

Programme Specifications

Programme Specification

MSc Digital Finance

Academic Year: 2020/21

This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if full advantage is taken of the learning opportunities that are provided.

This specification applies to delivery of the programme in the Academic Year indicated above. Prospective students reviewing this information for a later year of study should be aware that these details are subject to change as outlined in our Terms and Conditions of Study.

This specification should be read in conjunction with:

  • Summary
  • Aims
  • Learning outcomes
  • Structure
  • Progression & weighting

Programme summary

Awarding body/institution Loughborough University
Teaching institution (if different)
Owning school/department Loughborough University in London
Details of accreditation by a professional/statutory body
Final award MSc, (PGDip & PGCert as exit awards only)
Programme title Digital Finance
Programme code LLPT68 (FT) /LLPT69 (PT)
Length of programme Full-time: 1 year; Part-time: typically 2 years.
UCAS code N/a
Admissions criteria
Date at which the programme specification was published Thu, 25 Jun 2020 17:14:50 BST

1. Programme Aims

This programme aims to provide students with:

  • a comprehensive understanding of digital finance principles, and develop their skills to address associated challenges related to digital financial markets;
  • key employment skills in digital financial services, such as FinTech, blockchain, information systems, security, and artificial intelligence;
  • an overarching view of the context in which today’s digital finance businesses operate;
  •  the knowledge and expertise to create and develop innovative digital financial services using advanced digital technologies.

2. Relevant subject benchmark statements and other external reference points used to inform programme outcomes:

  • UK Quality Code for Higher Education, The Quality Assurance Agency for Higher Education, April 2012, especially Part A: Setting and maintaining academic standards:
    • The Frameworks for Higher Education Qualifications in England, Wales and Northern Ireland (FHEQ), the QAA, August 2008
    • Master’s Degree Characteristics, the QAA, March 2010
    • The Higher Education Credit Framework for England, the QAA, August 2008
  • The Quality Code, Part B: Assuring and enhancing academic quality
    • Chapter B1: Programme Design, Development and Approval
    • Chapter B3: Learning and Teaching
    • Chapter B4: Enabling student development and achievement
    • Chapter B6: Assessment of students
  • Master’s Degree Subject Benchmark for Business and Management, the QAA, 2015
  • Master's Degree Subject Benchmark for Engineering, the QAA, 2015

3. Programme Learning Outcomes

3.1 Knowledge and Understanding

On successful completion of this programme, students should be able to demonstrate a comprehensive knowledge and understanding of…

K1: Current debates and opportunities within the global digital finance landscape

K2: Practices in which digital tools and channels are used in finance related services

K3: How advanced financial technologies, including blockchain and cryptocurrencies, are applied in digital economies

K4: How Artificial Intelligence and data analytics improve market predictions and secure transactions in FinTech applications

3.2 Skills and other attributes

a. Subject-specific cognitive skills:

On successful completion of this programme, students should be able to…

C1: Appraise and critically assess modern and emerging digital finance concepts and develop new skills to apply them in real-world scenarios
C2: Identify, determine, and critically reflect on the ways to apply tools such as blockchain, cyber security, data analytics and machine learning for developing digital finance applications
C3: Independently formulate credible solutions in response to dynamically evolving digital finance markets
C4: Compare the applications of various market research methodologies in the digital finance industries

b. Subject-specific practical skills:

On successful completion of this programme, students should be able to…

P1: Integrate a range of digital tools and techniques, and research methods to apply in the area of digital finance
P2: Formulate, test and simulate digital financial business scenarios  
P3: Utilise widely used programming and simulation tools in solving data analytics problems and creating machine learning and AI solutions for digital finance applications.
P4: Employ a range of cyber security measures to address the security challenges facing the evolving FinTech landscape

c. Key transferable skills:

On successful completion of this programme, students should be able to…

T1: Demonstrate skills in analysing information with attention to details, including critical analysis of relevant work
T2: Communicate complex concepts to expert and non-expert audiences effectively
T3: Work independently or in groups to successfully complete time limited projects
T4: Apply technical knowledge and skill in developing their own ideas related to the concepts of the subject matter

4. Programme structure

Semester 1

Compulsory Module (15 credits)





Finance Principles



Optional Modules (Students should select modules totalling 45 credits)





Principles of Data Science



Information Management



Digital Application Development



Statistical Methods in Finance


Semester 2

Compulsory Module (30 credits)





Collaborative Project



Financial Technologies



Optional Modules (Students should select modules totalling 30 credits)





Cloud Applications and Services



Digital Technologies for Market Analysis



Information Systems Security



Advanced Big Data Analytics



Gaming Technologies and Systems



Strategy and Planning


Semester 3

Compulsory Module (60 credits)








* LLP121 Principles of Data Science is a prerequisite for LLP122 Advanced Big Data Analytics. Students wishing to take LLP122 Advanced Big Data Analytics must take LLP121 Principles of Data Science.

5. Criteria for Progression and Degree Award

In order to be eligible for the award, candidates must satisfy the requirements of regulation XXI.

6. Relative Weighting of Parts of the Programme for the Purposes of Final Degree Classification

Not Applicable.

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