Master of Science in Digital Financial Technology

Financial technology (abbreviated as FinTech) refers to a broad spectrum of the technology and innovation that aims to compete with traditional business models of financial services. Due to advances in cloud computing, data analytics, and artificial intelligence technologies, FinTech applications grew explosively in the last decade. To meet the surging demand for high quality FinTech talent in Singapore, NUS School of Computing introduces the new flagship graduate programme in FinTech, in collaboration with NUS Business School and Asian Institute of Digital Finance (AIDF) at NUS. This MSc programme will be managed by AIDF.

The School of Computing will be introducing the Master of Science in Digital Financial Technology (MSc DFinTech). This new programme is designed primarily for students who plan to work in the financial institutions or FinTech firms as (AI) software developer, data scientists, FinTech security specialists, or financial quantitative analysts. In addition to help build a strong foundation in computing and finance, this programme offers elective modules that cover deep computing and finance expertise to help prepare graduates for future career challenges in the FinTech sector.

Structure of Programme

This programme is a Master’s degree by coursework programme. Students are required to pass the requirement of 40 modular credits (equivalent to 10 modules), together with an additional 12 modular credits (MC) of bridging modules, meeting the following requirements:

  • 28MC of essential modules
  • 12MC of elective modules
  • 12MC capstone project

28MC essential computing modules

16 modular credits used to strengthen computing and finance foundations of MSc DFinTech students

12 modular credits used to strengthen FinTech foundations of MSc DFinTech students

12 MC elective modules

12 modular credits chosen from modules offered by the School of Computing

12MC capstone project

12 modular credits (spanning across 2 semesters)


The list of essential modules covers the following topics:

  • FT5001 (2MC) Fintech Innovations for Consumers
  • FT5002 (2MC) Digital Transformation at Financial Institutions
  • FT5003 (2MC) Blockchain Innovations
  • FT5004 (2MC) Programming for Blockchain Applications
  • FT5005 (4MC) Machine Learning for Finance
  • BMF5321 (4MC) Financial Modelling
  • BMF5322D (4MC) Introduction of Finance
  • IT5001 (4MC) Software Development Fundamentals
  • IT5003 (4MC) Data Structures and Algorithms

FT5001 to FT5005 are new modules designed to instil core FinTech competencies covering Artificial Intelligence, Blockchain, and Data Analytics. Among the 28MC essential modules, BMF5321 and BMF5322D cover the basics of finance and are offered by the NUS Business School. IT5001 and IT5003 cover the basics of computing. The objective is to ensure all students graduate with solid training in both computing and finance foundation.

Elective modules offered by the School of Computing cover areas such as computing systems, cybersecurity, AI, data analytics, and enterprise IT. Students can also take electives in risk management and investment from the NUS Business School. The elective modules are listed in Annex A.

A two-semester capstone project is designed to help students pick up in-depth skills and knowledge in a focused area via experiential learning. Students can choose either an academic research project under the supervision of world-class scholars at NUS, or a FinTech internship that will allow graduating students to obtain industry work experience that supports both practical skills and self-directed learning.

To meet the graduation requirement, students are allowed a maximum of two level 4000 modules, with the remaining modules at level 5000 or above. Note that admission to the SoC master’s degree programme is on a competitive basis and there is no guarantee of admission.

Modules Offered under MSc DFinTech

The details of the core and elective modules are listed in Annex A.

Duration of Programme

Students will be admitted to the full-time programme only. The normal candidature period for full-time students is 1.5 years. The maximum candidature is 3 years.


Full-time students can have a maximum and minimum workload of 20 and 12 modular credits, respectively per semester.

Sample Course Plan

For the full-time programme (normal candidature period of over 3 semesters), we recommend either of the following two course plans:


Semester 1
(14 MC)

Semester 2
(14 MC)

Semester 3
3 modules (12MC)

Capstone Project (12 MC) (spans over Semester 2 and 3)



Semester 1
(18 MC)

Semester 2
 (14 MC)

Semester 3
2 modules (8MC)

Capstone Project (12 MC) (spans over Semester 2 and 3)

Generally, full-time students have some flexibility to complete more or less modules, as long as they do not exceed the maximum or minimum full-time workload of 20 and 12 modular credits (MC) respectively per semester. Note that industry capstone project would include a full-time internship attachment of at least 4 months either during the 3rd semester or immediately after it. Where possible, this full-time internship attachment would be organised during the May to August period.

Continuation/Graduation Requirements

The MSc DFinTech programme uses the Cumulative Average Point (CAP) as a criterion for continuation and graduation. The University sets the minimum standards and specific programmes may implement stricter or additional requirements. Students may refer to the information on the University’s continuation requirements and duration of the programme.

In order to graduate from the MSc DFinTech programme, students are required to fulfil the programme requirement of 40 MC together with the additional 12 MC of bridging modules, and also achieve a minimum final CAP of 3.0.