Master of Science in Digital Financial Technology
Financial technology (abbreviated as FinTech) refers to a broad spectrum of the technologies and innovations that are being used to improve and automate the delivery of financial services. The FinTech industry has grown explosively in the last decade, with the advances being made in cloud computing, data analytics and artificial intelligence (AI). To meet surging demand for high quality FinTech talent in Singapore and globally, this new flagship graduate programme in FinTech is designed to help students build a strong foundation in computing and finance, and features a range of elective modules organised along three tracks: Computing Technologies, Financial Data Analytics and Intelligence, and Digital Financial Transactions and Risk Management.
The Master of Science in Digital Financial Technology (MSc DFinTech) is designed primarily to help prepare graduates for challenging but rewarding careers as AI software developers, data scientists, FinTech security specialists, financial quantitative analysts and other similar professions in financial institutions or FinTech firms.
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
The MSc DFinTech 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 52MC programme requirements:
| 16 MC used to strengthen computing and finance foundations of MSc DFinTech students | |
| 12 MC used to strengthen FinTech foundations of MSc DFinTech students | |
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| 12 MC chosen from modules offered by the School of Computing or Business School |
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| 12 MC (spanning across 2 semesters) |
28MC of Core/Essential Modules
FT5001 to FT5005 are new modules designed to instill 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.
This two-semester Capstone Project is designed to aid students to be equipped with in-depth skills and knowledge in a focused area through 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.
Duration of Programme
The normal candidature periods for full-time and part-time students are 1.5 and 2.5 years respectively. The maximum candidature is 3 years for all students.
Workload
We expect the majority of the candidates for MSc DFinTech to be full-time students that can have a maximum and minimum workload of 20 and 12 modular credits, respectively per semester. Where permitted, the maximum and minimum workload for part-time candidates shall be 12 and 4 modular credits respectively, per semester.
Course Plan
The normal candidature periods for full-time and part-time programmes are 3 and 5 semesters respectively. We recommend the following course plans for full-time and part-time programmes:
| Course Plan 1 Full-Time) | Semester 1 | Semester 2 | Semester 3 |
| Capstone Project (12 MC) (spans over Semester 2 and 3) | |||
| Course Plan 2 (Full-Time) | Semester 1 | Semester 2 | Semester 3 |
| Capstone Project (12 MC) (spans over Semester 2 and 3) | |||
| Course Plan 3 (Full-Time) | Semester 1 | Semester 2 | Semester 3 |
| Capstone Project (12 MC) (spans over Semester 2 and 3) | |||
The default plan recommended for full-time students is Course Plan 1. Students can choose 1 extra elective in either Semester 1 or 2 and take lesser modules in Semester 3.
Full-time students have flexibility to plan their modules as long as they do not exceed the full-time workload of 20 (maximum) and 12 (minimum) MC respectively per semester. The Industry Capstone Project includes a full-time internship attachment of at least 4 months which is likely to take place between May to Oct.
Part-time students are allowed to enroll a minimum of 4MC or take up to 12MC (maximum) per semester, and are expected to complete the programme within the normal candidature period of 2.5 years. Students may have the flexibility to plan their modules as long as they do not exceed the maximum workload. When planning for their workload, students should take into consideration their academic research project workload.
| Sample Course (Part-Time) | Semester 1 | Semester 2 | Semester 3 | Semester 4 |
| Capstone Project (12 MC) (spans over Semester 2 and 3) | ||||
| Sample Course (Part-Time) | Semester 1 | Semester 2 | Semester 3 | Semester 4 | Semester 5 |
| Capstone Project (12 MC) (spans over Semester 4 and 5) | |||||
Continuation/Graduation Requirements
The MSc DFinTech programme uses the Cumulative Average Point (CAP) as a criterion for continuation and graduation. 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 40MC together with the additional 12 MC of bridging modules, meeting a total of 52MC programme requirements, as well as to achieve a minimum final CAP of 3.0.
Students are allowed a maximum of two level 4000 modules, with the remaining modules at level 5000 or above.
To apply, please click here.