Master of Science in Financial Technology

Gain the FinTech skills to navigate the changing landscape of the finance industry. Financial Technology (FinTech) is a cluster of emerging innovations that have the potential to revolutionise the finance industry, enhancing the productivity of financial firms through data science and cyber technologies.

The Master of Science in FinTech is hosted by NTU's School of Physical and Mathematical Sciences. The curriculum is built on data science, artificial intelligence, and information technology, and provides students with the FinTech skills necessary to navigate the changing landscape of the finance industry. A strong emphasis is placed on the in-depth mastery of disruptive technologies in finance, including financial automation (e.g., robo-advisors), financial cryptography (e.g., blockchain technology), and digital financial services (e.g., financial inclusion).

The MSc in FinTech Programme is an intensive one-year full-time or two-year part-time programme by coursework taught in 3 trimesters per academic year. The curriculum consists of two specialisations: Intelligent Process Automation (IPA) and Digital Financial Services (DFS).​​The courses are delivered in intensive periods of 7 weeks (i.e., each trimester is split into two halves). All courses are conducted at the NTU main campus in the evenings (6:30 PM-10 PM) on weekdays or Saturdays (9:30 AM-1 PM or 2 PM-5:30 PM).

The programme consists of a total of 30 Academic Units (AU), with 12 AU of compulsory courses, 12 AU from the chosen specialisation's electives, and 6 AU from other electives:

Compulsory Courses (12 AU) 
Compulsory courses12 AU

 

Elective Courses (12+6 AU)
Prescribed Electives of the chosen specialisation12 AU
Unrestricted Electives  6 AU
Total30 AU

The requirements for graduation are:

  • ​​Successful completion of the courses of a total of 30 AU as prescribed by MSc in FinTech; and
  • Attaining a minimum CGPA of 2.50 at the completion of the programme of study.​ 

Programme Features

Applicants must select one of the following two specialisations:

  • The Intelligent Process Automation (IPA) specialisation (code: 270) is designed for students interested in the technical aspects of FinTech. Candidates opting for this specialisation should have a good bachelor’s degree in a quantitative major or a good track record in mathematics and programming (especially for non-science/engineering graduates).

  • The Digital Financial Services (DFS) specialisation (code: 271) is designed for students interested in the managerial aspects of FinTech. Candidates opting for this specialisation should have a good bachelor’s degree in a relevant programme (e.g., quantitative majors, business, etc.) or relevant working experience in the finance industry.

The specialisation is chosen at the point of application into the programme, and a change of specialisation is generally not allowed. However, candidates are allowed to submit two separate applications, one for each specialisation; in that case, the preferred specialisation should be indicated in the applications.

The practicum course, MH6838, starts in Trimester 3 and comprises either a research-based project or a self-sourced internship where students work on a professional consulting project mentored by experienced instructors to solve financial problems. The School will assist students in seeking internship opportunities. The internship companies our students were previously involved with include GIC, Grab, Julius Baer, Lumiq, DBS, OCBC, UOB, Macquarie Bank, CIMB, etc.

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Compulsory Courses

MH6800  Foundations of Statistical Modelling1.5 AU
MH6801  Introduction to ​FinTech
1.5 AU
MH6802  FinTech Ecosystem and Innovations
1.5 AU
MH6803  Python Programming
1.5 AU
MH6804  Python for Data Analysis
1.5 AU
MH6805  Machine Learning in Finance3 AU
MH6806  Principles of Finance and Risk Management
1.5 AU

Prescribed Elective Courses for Intelligent Process Automation Specialisation

MH6301  Information Retrieval and Analysis3 AU
MH6812  Advanced Natural Language Processing with Deep Learning3 AU
MH6813  Blockchain Systems I: Concepts and Principles1.5 AU
MH6814  Blockchain Systems II: Development and Engineering
1.5 AU
MH6815  Algorithmic Trading and Robo-Advisors
1.5 AU
MH6816  Introduction to Cybersecurity1.5 AU
MH6831 Quantitative Methods in Finance1.5 AU
MH6832 Reinforcement Learning for Finance1.5 AU

Prescribed Elective Courses for Digital Financial Services Specialisation

MH6331  Financial and Risk Analytics I1.5 AU
MH6332  Financial and Risk Analytics II1.5 AU
MH6821  Anti-Financial Crime and Compliance1.5 AU
MH6822  Regulatory Technology1.5 AU
MH6823  Financial Inclusion and Decentralized Finance1.5 AU
MH6824  Fundamentals of FinTech Entrepreneurship1.5 AU
MH6825 FinTech Entrepreneurial Practice1.5 AU
MH6826 Investment and Portfolio Management1.5 AU
MH6827 Financial Data Management and Business Intelligence1.5 AU
MH6833 Microeconomics and Macroeconomics1.5 AU

Unrestricted Elective Courses

Prescribed Electives of one specialisation can be Unrestricted Electives for another specialisation. Moreover, students can take the following courses as Unrestricted Electives.

MH6838 Practicum3 AU

Remarks:

  1. On average, full-time students take 4 courses a week, and part-time students take 2 courses a week. Part-time students can, at most, take 3 courses a week.

  2. A 1.5 AU course refers to a 7-week course, with each week consisting of 3.5 teaching hours, including breaks. Similarly, a 3 AU course is a 14-week course consisting of 3.5 teaching hours, including breaks.