The programme aims to provide fundamental knowledge on finance applications of blockchain technology, digital currencies, big data and financial analytics. It covers digital banking, payment systems, blockchain, cryptocurrencies, peer to peer lending, crowdfunding, quantitative methods in financial analysis and big data analytics in finance. The programme develops students with programming skills in Python and put them into practice to solve real world finance problems.

This programme helps prepare students to start their career in FinTech with our practitioner-led education in a stimulating package. Various enrichment activities such as visits and guest talks will be arranged for students which will be a useful networking platform with industry practitioners in FinTech.

Programme Features

• Taught by seasoned practitioners in FinTech
• Get latest insights from FinTech field
• Fast-track, one-year part-time programme with 6 modules
• Interactive case studies
• In-class FinTech application simulations

 

ECF on Fintech - Modular Exemption

Advanced Diploma in FinTech holders are eligible to apply for exemption on Module 1 – Technology Essentials (Core Level) and Module 4 – Fundamental Software Development (Core Level) of the Enhanced Competency Framework (ECF) - FinTech qualification.

The ECF-Fintech is a collaborative effort of the HKMA, the Hong Kong Institute of Bankers (HKIB) and the banking sector in establishing a set of common and transparent competency standards for developing a strong Fintech talent pipeline and enhancing the professional competence of existing banking practitioners who are performing functions that involve technological innovation for financial services in Hong Kong’s banking industry. This framework will facilitate banking practitioners to acquire relevant Fintech knowledge and develop professional competencies in the Fintech area more effectively. Please refer to the HKMA Circular on “Enhanced Competency Framework on Fintech” for details.

 

Lecturers

Dr Ringo Chan, Senior Programme Director, HKU SPACE

Dr. Ringo Chan has extensive experience in programme development and administrative management of both full-time and part-time programmes ranging from sub-degree to Master degree level in business and finance. He obtained his EdD from the University of Leicester. With more than 12 years of teaching experience, he has specialised in teaching investment management, global financial markets and behavioural finance. He is a financial writer at China Daily and serves as the member of advisory group of Investor Education Centre, mentor of the University of Hong Kong and honorary flight lieutenant of the Hong Kong Air Cadet Corps. His current academic research focuses on lifelong education, educational psychology and financial management. His articles have published in journals such as Active Learning in Higher Education and International Journal of Continuing Education and Lifelong Learning.

 

Dr Albert Lam, the Chief Technology Officer and Chief Scientist at Fano Labs

Albert received the BEng degree (First Class Honors) in Information Engineering from the University of Hong Kong(HKU), Hong Kong, in 2005 and he obtained the PhD degree at the Department of Electrical and Electronic Engineering of HKU in 2010. He was a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences of University of California, Berkeley. He was a Research Assistant Professor at the Department of Computer Science of Hong Kong Baptist University in 2012–15 and the Department of Electrical and Electronic Engineering (EEE) of HKU in 2015–17. He is now the Chief Technology Officer and Chief Scientist atFano Labs, a deep-tech startup specializing in speech and language technologies. He also serves at a Adjunct Assistant Professor at HKU EEE andGeograhpy. He is aCroucherresearch fellow. He is a member of the Expert Committee ofShenzhen Artificial Intelligence Industry Association. He is an active member of IEEE. He is the founding Chair of theSocial Media Subcommitteeof theIEEE Computational Intelligence Society(CIS) and has also chaired some other committees in CIS. His research interests include optimization theory and algorithms, artificial intelligence, evolutionary computation, smart grids, and smart cities. He is an Associate Editor ofIEEE Transactions on Evolutionary Computation,IEEE Transactions on Intelligent Transportation Systems,IEEE Transactions on Artificial Intelligence, andIEEE Transactions on Emerging Topics in Computational Intelligence. He is also an co-Editor-in-Chief ofEAI Endorsed Transactions on Energy Web.

 

Dr Jason Liao, Manager, FinTech Company

Dr. Jiarui Liao (Jason) obtained his Bachelor and Master degrees from Tsinghua University and PhD from HKUST. He is a Qlik Sense Data Architect with certification. He has lots of working experience and wide expertise in big data, business intelligence, machine learning and FinTech innovation. He is proficient in BI data model/dashboard development, Python programming, SQL, etc. During his past working experience, either internal or external training took a rather large portion, and he elicited favorable comments from trainees.

 

Mr Jesse Co, General Manager, Blockchain Solutions

Jesse holds the position of General Manager at Blockchain Solutions Limited ("BSL"), a HK Science and Technology Park company with a focus on blockchain technology. BSL is one of the few Hong Kong companies based on blockchain technology, with a track record in Hong Kong Government blockchain projects. BSL has been recognized by ETnet’s Fintech Awards, IFTA’s Fintech Awards, and other accolades. Prior to joining BSL, Jesse has accumulated over a decade of experience in finance and real estate. He started his career in banking, then shifted his role to corporate finance & strategy in listed companies. Also, he has held senior positions in real estate corporations and funds in Hong Kong and China. Jesse holds an MSc in Finance from HKUST, and a BA from Trinity College, USA.

 

Mr Ken Tsoi, MSc FRM

Ken has over 10 years of full time working experience across start-ups, data software companies, retail bankings, insurance companies, listed co., and large MNC FI firms in areas of AI, business intelligence, big data and machine learning. He is currently working in a Hang Seng Index listed company as a senior data expert in charging of data projects which are related on Predictive Analytics, Algorithmic Business and Neural Network. He has been teaching and coaching on different institutes and schools over the decade and is a professional on communicating abstract data concept and theory to layman and student in an efficient and effective way. He holds Bachelor (Hon) of Statistics and Operation Research in HKBU and Master of Statistics and Risk Management in HKU. He is a certified FRM (Financial Risk Management) Professional, Certified Statistical Business Analyst; Certified Predictive Modeler: Enterprise Miner; Certified Advanced Programmer; Certified Base Programmer; and Certified System Platform Administrator, all 5 Credentials were approved by SAS Institute.

 

Mr Raymond Chan, CEO & Director of Metaverse Securities Limited

Raymond Chan serves as the CEO & Director of Metaverse Securities Limited and Lion Global Financial Limited (iFund), both 9F Inc. (NASDAQ: JFU) subsidiaries and licensed corporations in Hong Kong. He was appointed as the Managing Director of 9F International in 2018 to apply for the Virtual Bank license in Hong Kong. At the same time, he also led the applications for other innovative financial licenses, including Digital Insurance in Hong Kong, Digital Wholesale Bank in Singapore and Electronic Money Institution in Lithuania. He is the founder of “inMotion”, the virtual banking services which introduced the first remote account opening in Hong Kong. In addition, he is also responsible for many “first in HK” fintech innovations including WeChat Pay Bank Account Binding, WeChat Pay Travel Insurance, CITICpay, to name but a few. These innovations have certainly led the market and shaped the trends thereafter.

Raymond has more than 25 years of working experience in the management positions of the banking covering Marketing, e-Business, Branch Network, Call Centre, Distribution Planning, Sales & Distribution (S&D), Business Supports, Operations and Technology in HSBC, Standard Chartered, ICBC (Asia) and China CITIC Bank International.

He is currently a DBA candidate of SBS Swiss Business School and possesses dual master degrees including Master of Applied Business Research from SBS Swiss Business School and MBA from Chinese University of Hong Kong. He obtained his undergraduate degree from the University of Hong Kong as BSc (Engineering). In 2020, he was selected “Leader of the Year” by IFTA.

 

Guest Speakers

Speaker Name	Title
Paul Pong	Managing Director of Pegasus Fund Managers Limited; Co-Founder & Chairman of IFTA
Wilson Kwok	Managing Director of LKKC CPA Limited; Co-Founder & Vice Chairman of IFTA
Andy Chung	Director of ESG Matters; Co-Founder of IFTA
George Lam	Chairman of Cyberport; Advisor of IFTA
Fred Ngan	Co-Founder & Co-CEO of Bowtie; Council Member of IFTA
Brian Chiang	Head of International Coverage of HSBC; Council Member of IFTA
Rogers Chan	Managing Partner of ImpactInvest; Council Member of IFTA
Ashley Khoo	Board Members of The Hong Kong Society of Financial Analysts; Council Member of IFTA
Ken Lo	Deputy Chairman of BC Group
Jason Lau, Adjunct Professor	Chief Information Security Officer of Crypto.com

 

Guest Lectures

	Dr. Tao Charm, Chairman of OpenCertHub
	Mr Adrian Lai, CEO of Liquefy
	Mr Darron Sun, Vice Chairman of IFTA
	Mr Marco Lim, Managing Partner, MaiCapital
	FinTech Guest Speaker Session 2022: Mr William Lee, Co-Founder, YAS Micro Insurance

On completion of the Programme, students should be able to:

1. Describe the general FinTech landscape and FinTech jargons in the financial service industry;
2. Apply the machine learning skills in financial analysis;
3. Write programme functions in Python and process data files in finance applications;
4. Discuss the impacts of blockchain applications in financial services;
5. Outline how big data analytics enable new financial and commercial processes.

 

Programme Structure

The programme consists of 6 modules over 3 terms within 1 year.

• Module 1: Introduction to FinTech (36 lecture hours + 6 computer lab)
• Module 2: Principles of Finance (30 lecture hours)
• Module 3: Programming for FinTech Applications (36 lecture hours + 6 computer lab)
• Module 4: Machine Learning in Finance (30 lecture hours) 
• Module 5: Blockchain Applications and FinTech (30 lecture hours)
• Module 6: Big Data Applications and Financial Analytics (30 lecture hours)

The programme covers part of the body of knowledge of Certified Financial Technologist (CFT) examination offered by the Institute of Financial Technologist of Asia (IFTA). For details, please go to: https://cftasia.org/. FinTech field trips will be organized in collaboration with the IFTA to enrich students’ learning experience in areas of big data, digital payment, blockchain, AI, crytocurrencies, cyber security, and other FinTech solutions. The field trips include FinTech company visits in Cyberport and Science Park in Hong Kong. During the field trip visits, demonstration of latest solution in FinTech and informative talks by the company representatives will be arranged.

 

Award

Upon completion of the programme, students will be awarded within the HKU system through HKU SPACE an Advanced Diploma in Financial Technology.

 

Assessment Criteria

Assessment for each module will comprise continuous assessment (assignments) and examination. All assessments will be in English.

 

Attendance Requirement

Students are required to achieve at least 70% in attendance for each module to complete the whole programme.

Application Code	2265-FN049A	Apply Online Now
Apply Online Now

Intoduction to FinTech

This module introduces the basic concepts of FinTech, covering the emerging technologies of FinTech and regulatory challenges in FinTech. It examines main FinTech landscape including digital payment, peer to peer lending, cryptocurrencies, robo-advisor and regulatory technologies.

 

Principles of Finance

This module aims to introduce basic concepts in finance and financial valuation models. It covers the main features of international stock and bond markets, fundamental features of bond analysis and trading, key features of global stock markets, equity valuation and risk management.

 

Programming for FinTech Applications

This module will introduce the basics of the Python programming environment, including fundamental Python and R programming techniques and the use for various libraries. It will cover data manipulation and cleaning techniques using the popular Python data science library.

 

Machine Learning in Finance

This module provides students with the basic machine learning techniques commonly used in finance. Students will learn how to formulate and analyse finance problems from a machine learning perspective. The end-to-end process of investigating data through different machine learning algorithms in financial decision analysis will be discussed.   

 

Blockchain Applications and FinTech

This module aims to offer students with a foundation of blockchain technology in the finance and investment fields. It gives an overview of the blockchain applications and introduces students to the regulatory issues of blockchain.

 

Big Data Applications and Financial Analytics

This course equips students with the fundamental concepts and skill to apply financial analytics in finance and investment.  Students can learn basic algorithmic and statistical techniques and hands-on application to perform big data analysis.   

Jan 2025 Intake - Tentative timetable (TBA)

Introduction to FinTech

Session	Lecture Date	Time	Remarks
1	20 Jan 2025 (Mon)	7:00 pm - 10:00 pm
2	27 Jan 2025 (Mon)	7:00 pm - 10:00 pm
3	3 Feb 2025 (Mon)	7:00 pm - 10:00 pm
4	10 Feb 2025 (Mon)	7:00 pm - 10:00 pm
5	17 Feb 2025 (Mon)	7:00 pm - 10:00 pm
6	24 Feb 2025 (Mon)	7:00 pm - 10:00 pm
7	1 Mar 2025 (Sat)	2:00 pm - 5:00 pm	Computer Lab
8	3 Mar 2025 (Mon)	7:00 pm - 10:00 pm
9	8 Mar 2025 (Sat)	2:00 pm - 5:00 pm	Computer Lab
10	10 Mar 2025 (Mon)	7:00 pm - 10:00 pm
11	17 Mar 2025 (Mon)	7:00 pm - 10:00 pm
12	24 Mar 2025 (Mon)	7:00 pm - 10:00 pm
13	31 Mar 2025 (Mon)	7:00 pm - 10:00 pm
14	7 Apr 2025 (Mon)	7:00 pm - 10:00 pm
Exam	TBA

 

Machine Learning in Finance

Session	Lecture Date	Time
1	22 Jan 2025 (Wed)	7:00 pm - 10:00 pm
2	5 Feb 2025 (Wed)	7:00 pm - 10:00 pm
3	12 Feb 2025 (Wed)	7:00 pm - 10:00 pm
4	19 Feb 2025 (Wed)	7:00 pm - 10:00 pm
5	26 Feb 2025 (Wed)	7:00 pm - 10:00 pm
6	5 Mar 2025 (Wed)	7:00 pm - 10:00 pm
7	26 Mar 2025 (Wed)	7:00 pm - 10:00 pm
8	29 Mar 2025 (Sat)	2:00 pm - 5:00 pm
9	2 Apr 2025 (Wed)	7:00 pm - 10:00 pm
10	9 Apr 2025 (Wed)	7:00 pm - 10:00 pm
Exam	TBA

 

Big Data Applications and Financial Analytics

Session	Lecture Date	Time
1	18 Jan 2025 (Sat)	2:30 pm - 5:30 pm
2	25 Jan 2025 (Sat)	2:30 pm - 5:30 pm
3	1 Feb 2025 (Sat)	2:30 pm - 5:30 pm
4	8 Feb 2025 (Sat)	2:30 pm - 5:30 pm
5	15 Feb 2025 (Sat)	2:30 pm - 5:30 pm
6	22 Feb 2025 (Sat)	2:30 pm - 5:30 pm
7	1 Mar 2025 (Sat)	2:30 pm - 5:30 pm
8	8 Mar 2025 (Sat)	2:30 pm - 5:30 pm
9	15 Mar 2025 (Sat)	2:30 pm - 5:30 pm
10	22 Mar 2025 (Sat)	2:30 pm - 5:30 pm
Exam	TBA

 

Venue: HKU SPACE Po Leung Kuk Stanley Ho Community College (HPSHCC) Campus (at Causeway Bay) or other locations in Hong Kong Island.

 

2025 Tentative Schedule

May 2025	Sep 2025	Jan 2026
Principles of Finance Programming for FinTech Applications Blockchain Applications and FinTech	Introduction to FinTech Machine Learning in Finance Big Data Applications and Financial Analytics	Programming for FinTech Applications Principles of Finance Blockchain Applications and FinTech

The above tentative timetable may be subject to change without prior notice.

Application Fee

HK$200 (non-refundable)

Course Fee

• Module 1: Introduction to FinTech $6,700
  
  Module 2: Principles of Finance $4,900
  
  Module 3: Programming for FinTech Applications $6,700
  
  Module 4: Machine Learning in Finance $4,900
  
  Module 5: Blockchain Applications and FinTech $4,900
  
  Module 6: Big Data Applications and Financial Analytics $4,900

Applicants shall have gained in the HKDSE Examination Level 2 in 5 subjects including English Language and Chinese Lauguage or equivalent.

Applicants who do not possess the above academic qualifications but are aged 21 or above with relevant work experience will be considered on individual merit.

• The CEF Institution Code of HKU SPACE is 100

CEF Courses
Machine Learning in Finance (Module from Advanced Diploma in Financial Technology)
COURSE CODE 33Z144178	FEES $4,900	ENQUIRY 2867-8312
Introduction to FinTech (Module from Advanced Diploma in Financial Technology)
COURSE CODE 33Z130223	FEES $6,700	ENQUIRY 2867-8312
Principles of Finance (Module from Advanced Diploma in Financial Technology)
COURSE CODE 33Z130231	FEES $4,900	ENQUIRY 2867-8312
Programming for FinTech Applications (Module from Advanced Diploma in Financial Technology)
COURSE CODE 33Z13024A	FEES $6,700	ENQUIRY 2867-8312
Quantitative Methods for Finance (Module from Advanced Diploma in FinTech)
COURSE CODE 33Z130258	FEES $4,500	ENQUIRY 2867-8312
Blockchain Applications and FinTech (Module from Advanced Diploma in Financial Technology)
COURSE CODE 33Z130266	FEES $4,900	ENQUIRY 2867-8312
Big Data Applications and Financial Analytics (Module from Advanced Diploma in Financial Technology)
COURSE CODE 33Z130274	FEES $4,900	ENQUIRY 2867-8312

Continuing Education Fund

More information of application procedures: https://hkuspace.hku.hk/cef/application-procedures

	Continuing Education Fund This course has been included in the list of reimbursable courses under the Continuing Education Fund.
	Continuing Education Fund Reimbursable Course (selected modules only) Some modules of this course have been included in the list of reimbursable courses under the Continuing Education Fund.
	Advanced Diploma in Financial Technology This course is recognised under the Qualifications Framework (QF Level [4])

Online Application Apply Now

Application Form Application Form

Enrolment Method

1. Please complete APPLICATION FORM and submit them in person at any of the following SPACE enrolment centre. Please refer to the SPACE enrolment centre for opening hours and address.
2. All applications must be accompanied by:

   a) Photostat copies of full educational certificates and transcripts.

   b) Testimonials or other documentary proof of the applicant's working experience.

   c) A separate non-refundable crossed cheque payable to “HKU SPACE” for HK$200 in respect of the application processing fee. Application fee and course fee can be paid by credit cards at HKU SPACE Enrolment Centres.

Note: When submitting your application in person at any of the HKU SPACE enrolment centres, please bring along the originals of your educational certificates / transcripts and documentary proof of working experience for certification at the enrolment centres. Late applications may only be considered at the discretion of the Course Director.

Payment Method

1. Cheque or Bank Draft

   Course fees can also be paid by crossed cheque or bank draft made payable to “HKU SPACE”. Please specify the programme title(s) for application, student's name, and student card no. (if applicable). You may either:

   bring the completed form(s), together with the appropriate course or application fees in the form of a cheque, and any required supporting documents to any of the HKU SPACE enrolment centres. Please refer to the SPACE enrolment centre for opening hours and address.

2. VISA/MasterCard

   Applicants may also pay the course fee by VISA or MasterCard, including “HKU SPACE MasterCard”, at any HKU SPACE enrolment centres. Holders of HKU SPACE MasterCard can enjoy a 10-month interest-free instalment for courses with a tuition fee of HK$2,000 to $40,000. The cardholder must also be the course applicant himself/herself. For enquiries, please contact our staff at any of the enrolment centres.

 

Notes

1. Fees paid are not refundable except under very exceptional circumstances, subject to the School's discretion.
2. In addition to the published fees, there may be additional costs associated with individual programmes. Please refer to the relevant course brochures or direct any enquiries of the relevant subject area for details.
3. Fees and places on courses cannot be transferred from one applicant to another. Once accepted onto a course, the student may not change to another course without approval from HKU SPACE. A processing fee of $120 will be levied on approved transfers.
4. Receipts will be issued for fees paid but HKU SPACE will not be responsible for any loss of receipt sent by mail.
5. For additional copies of receipts, please send a stamped, self-addressed envelope with a completed form and a crossed cheque for $30 per copy made payable to “HKU SPACE”. Such copies will normally only be issued at the end of a course.
6. Classes are expected to be held at HKU SPACE learning centers. The health and safety of our students is our top priority. If the situation in Hong Kong requires. HKU SPACE reserves the right to move some classes to other locations, including online teaching platforms.