Financial Technology (FinTech) refers to a cluster of emerging innovations that have the potential to revolutionize the nature of the finance industry, enhancing the productivity of financial firms by employing data science and cyber technologies. Global investment in FinTech has been so active in recent years that the Monetary Authority of Singapore has launched an initiative, the FinTech and Innovation Group, and pledged to spend S$225 million over the next five years to develop the FinTech sector in Singapore.
Nanyang Technological University, Singapore (NTU Singapore) offers a Master of Science in FinTech (MSc in FinTech) hosted by the School of Physical and Mathematical Sciences. The curriculum is built upon data science, artificial intelligence, and information technology to provide students with the FinTech skills necessary for navigating the changing landscape of the finance industry. Strong emphasis is placed on the in-depth mastery of disruptive technologies in finance, including financial automation (e.g. robo-advisors) and financial cryptography (e.g. blockchain technology).
Click here for the MSc in FinTech programme brochure!
Programme & Curriculum Structure
The MSc in FinTech Programme is an intensive 1-year full-time or 2-year part-time programme by coursework taught in 3 trimesters per year. The curriculum consists of two tracks: Artificial Intelligence and Operations and Compliance. Upon completion of study, students will be conferred the degree of MSc in FinTech.
The programme consists a total of 33 Academic Units (AUs), with 21 AUs of compulsory modules, 6 AUs from the chosen track's electives, and 6 AUs from other electives:
| Compulsory courses |
21 AUs |
| Prescribed Electives |
6 AUs |
| Unrestricted Electives |
6 AUs |
| Total graduation requirements |
33 AUs |
Compulsory Courses
This course gives an overview of all the changes, which are happening now in the financial industry and discusses how some of the FinTech processes are being constructed. Each FinTech disruption concept is based on a mathematical of behaviour concept, which is backed by data, analysis and technology. This course goes into detail into some of these processes, so give an understanding as to what is the business model, skill, and future of FinTech in the financial services industry. It will also cover the recent progresses on FinTech development and applications. Although the topics may vary in order to keep pace with the FinTech development, they mainly involve case studies, practical challenges, trends, and opportunities in a FinTech career.
This course discusses the existing and future landscapes of FinTech in Singapore, from incumbent financial firms to FinTech startups. Both traditional and new players are working with policy-makers to define the ecosystem, to encourage innovation, adoption while maintaining regulatory oversight.
This course provides an introduction to the basic principles and theory of finance, terminology and commonly used tools. The course will specifically discuss the financial system, financial statements and financial statement analysis, time value of money, basic valuation of bonds and stocks, capital budgeting processes and techniques, and risk analysis
Probability, conditional probability; random variables, joint distributions, conditional distributions and independence; probability laws, multivariate normal distribution; order statistics; convergence concepts, the law of large numbers, central limit theorem. Estimation, Bayes estimators, interval estimation including confidence intervals, prediction intervals, Bayesian interval estimation; Hypothesis testing, likelihood ratio tests; Bayesian tests; Nonparametric methods, bootstrap.
The knowledge discovery process. Data preparation including data cleaning, outlier analysis and transformation. Statistical techniques: regression modelling, multivariate statistics, statistical inference. Supervised and unsupervised learning techniques including decision tree induction, nearest neighbour categorisation, cluster analysis, association analysis, support vector machines, Bayesian learning and neural networks. Data mining software and tools. Applications of data mining to complex data types.
This course covers basic and essential quantitative methods in finance. A number of mathematical and statistical techniques are introduced. This course emphasizes the applications of the quantitative methods in two important areas in finance: asset management and derivative pricing.
This course covers the quantitative methods to construct computer-based algorithms for automatic trading and asset management. A number of notable algorithmic trading strategies are discussed. This course also emphasizes the rationale behind the winning strategies, backtesting, automated execution and how to build robots for trading and asset management with specific goals. Moreover, the course provides a hands-on experience of implementing the financial solutions with real market data.
This is an introductory course that attempts to answer the following questions: What is blockchain? What are the building blocks of blockchain? What are smart contracts? How are existing blockchains (Bitcoin, Ethereum, Hyperledger, etc.) different from one another? What is cryptocurrency? What is asset monetization via blockchains? How to build a blockchain network? How does blockchain disrupt existing systems across different domains? What are the limitations of existing blockchains? What are the costs of incorporating blockchains? What is the strategy to enable blockchains in enterprise? What are the regulations and best practices for blockchains?
Professional consulting project mentored by experienced instructors to solve problems that are of great importance to the sponsoring companies.
Artificial Intelligence Prescribed Elective Courses
These courses introduce a number of optimization methods commonly used in operations research. Nonlinear optimization, discrete optimization, stochastic optimization, queuing theory, inventory theory, dynamic programming, simulation, applications.
Python is an easy to learn higher level scripting language that can be used across many different platforms. As such, it is a common choice to code for FinTech products. This course will train the student for programming in python, with particular focus in FinTech applications.
In this course, students will learn state-of-the-art deep learning methods for Natural language processing (NLP). Through lectures, practical assignments and projects, students will learn the necessary tricks for making their deep learning models work on practical problems. They will learn to implement, and possibly to invent their own deep learning models using available deep learning libraries.
Operations and Compliance Prescribed Elective Courses
Regulations are essential to ensure good governance in the finance industry. FinTech aiming to replace existing financial services will be subject to the same regulations. RegTech, short for regulatory technology, aims to simplify the compliance process, providing large savings in face of rising compliance costs. This course introduces the myriad of financial regulations, both for traditional financial services as well as new regulations introduced to cover novel FinTech services. The potential of RegTech for cost reduction will also be discussed.
Financial Crime Compliance and Regulatory Compliance are probably at the top of nearly every financial institution’s risk review process and have become the key strategic imperatives for all board members. This course provides a robust training in Know your customer (KYC) and Customer Due Diligence (CDD) processes by drawing on cutting-edge experience of what world’s leading financial institutions are doing, have done, and must still do. In addition, this course covers the incorporation of the new technologies into the KYC and CDD processes.
Techniques for measuring and managing the risk of trading and investment positions for positions in equities, credit, interest rates, foreign exchange, commodities, vanilla options, and exotic options; risk sensitivity reports, design of static and dynamic hedges, measure value-at-risk and stress tests; Monte Carlo simulations determining hedge effectiveness; case studies.
Unrestricted Elective Courses
These courses introduce a number of optimization methods commonly used in operations research. Nonlinear optimization, discrete optimization, stochastic optimization, queuing theory, inventory theory, dynamic programming, simulation, applications.
Many of the business systems are dynamic systems in which their states change over time. This course introduces time series models and associated methods of data analysis and inference. Topics include auto regressive (AR), moving average (MA), ARMA, and ARIMA processes, stationary and non-stationary processes, seasonal processes, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, and spectral analysis. Real-world applications for understanding characteristics of time series data in economics, finance, management and industries, and modelling and evaluating forecasts upon which decision-making would depend are emphasized with lab on using SAS.
This course explores management, organizational, and technological issues in the ways data are stored, managed and applied in businesses. Using a simulated business, the database module covers data concepts, structures, conceptual and physical design techniques, data administration and data mining. Theory and practice of database management systems are integrated through hands-on experience with the design and implementation of a business solution. By the end of the course, participants will gain critical IT skills in analysing business processes, improving these processes, developing business applications with an industry standard database and use data for business requirements.
Representation, storage, and access to very large digital document collections: issues, data structures and algorithms. Information retrieval models including Boolean, vector space and probabilistic models. Indexing and retrieval techniques. Evaluation of information retrieval systems. Text and Web mining: content, structure and usage mining. Web search: search engines, spiders, link analysis, agents. Recommender systems and intelligent information retrieval. Information extraction and integration.
Note: Prescribed Elective courses of one track could be Unrestricted Elective courses of another track. On average, full-time students take 4-5 classes a week and part-time students take 2-3 classes a week.
Academic Timeline
| Trimester 1 |
1st Half |
22 July to 1 September 2019 (6 weeks)
Recess: 2 to 9 September 2019 (1 week) |
| 2nd Half |
9 September to 27 October 2019 (7 weeks)
Recess: 28 October to 3 November 2019 (1 week) |
| Trimester 2 |
1st Half |
4 November to 15 December 2019 (6 weeks)
Recess: 16 to 29 December 2019 (2 weeks) |
| 2nd Half |
30 December 2019 to 16 February 2020 (7 weeks)
Recess: 17 February to 23 February 2020 (1 week) |
| Trimester 3 |
1st Half |
24 February to 5 April 2020 (6 weeks)
Recess: 6 April to 12 April 2020 (1 week) |
| 2nd Half |
13 April to 31 May 2020 (7 weeks)
Recess: 27 May to 21 July 2020 (8 weeks)
|
General Admission Criteria
- A good Bachelor's Degree in a relevant programme, e.g. quantitative majors, business, etc.
- A good TOEFL score (iBT = 92 or more, PBT = 580 or more, CBT = 235 or more) or IELTS score (6.5 or more) for graduates of universities in which English is not the medium of instruction.
- A good GRE or GMAT score is preferred (Graduates from autonomous universities in Singapore are exempted).
- A minimum of two years of relevant working experience is preferred.
Applicants can send their GMAT score to us with the following program code:
| School of Physical and Mathematical Sciences, Part Time |
V24-97-23
|
| School of Physical and Mathematical Sciences, Full Time |
V24-97-63
|
How to Apply
| Application |
Please check out How to apply ;
Direct link to online application (non-electronic) |
25 January 2019 to 14 April 2019 |
| |
Submission of supporting documents |
by 13 April 2019 |
| |
Payment of application fee
|
by 14 April 2019 |
| Offer and Acceptance |
Offer (1st window) |
29 April 2019 |
| |
Online appeal (1st window) |
29 April 2019 to 3 May 2019 |
| |
Acceptance of offer (1st window) |
29 April 2019 to 10 May 2019 |
| |
Offer (2nd window) |
20 May 2019 |
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Online appeal (2nd window) |
20 May 2019 to 24 May 2019 |
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Acceptance of offer (2nd window) |
20 May 2019 to 31 May 2019 |
| After Acceptance |
Matriculation |
15 July 2019 |
| |
Welcome Ceremony |
To be confirmed |
| |
Course Commencement |
22 July 2019 |
Medical Check-up
Enrolling students must go to the Medical Centre to do the medical check-up and obtain a receipt certifying that they are medically fit for studies. The medical examination clearance certification must be presented prior to matriculation.
Programme Fees
AY2019/2020
| Singapore Citizens |
S$35,000 |
| Singapore Permanent Residents |
S$45,000 |
| International Students |
S$55,000 |
All Fees are inclusive of GST.
Payment Schedule (full-time students)
| Singapore Citizens |
S$5,000 |
S$6,667 |
S$11,667 |
S$11,666 |
| Singapore Permanent Residents |
S$5,000 |
S$10,000 |
S$15,000 |
S$15,000 |
| International Students |
S$5,000 |
S$13,333 |
S$18,333 |
S$18,334
|
S$5,000 is non-refundable upon acceptance of offer, and will offset the first installment.
The MSFT programme is totally self-financed through fees collected. Service obligation option would not be extended to international students.
Note:
- Successful international applicants must show proof of having sufficient funds for the normal duration of the MSc in FinTech programme, after accepting the admission offer. Currently, the calculation of the required amount is based on S$18,000 per year for living expenses, in addition to the tuition and miscellaneous student fees.
- Fees must be paid in full before the start of the Trimester.
- Part-time students pay their fees over 6 trimesters.
Financial Assistance
The Financial Scholarship Programme (FSP) is offered by the Monetary Authority of Singapore.
Prospective Singaporean students applying for the MSc in FinTech are eligible for the Financial Scholarship Programme (FSP), offered by the Monetary Authority of Singapore. Note that the deadline for the FSP application is mid-May for company-track applicants and the end of March/April for individual-track applicants.
Click here for more information about the FSP.
Past Events
| Information and networking seminar in Singapore |
NTU Singapore |
24 January 2019 |
Information and networking seminar in Shanghai
|
Intercontinental SH Jing' An |
6 January 2019 |
Information and networking seminar in Beijing |
China World Hotel |
5 January 2019 |
| Singapore FinTech Festival |
Singapore Expo |
12-16 November 2018 |
Please follow our Facebook page @NTUMScFinTech for the details and updates of the events.
Contact Us