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Accounting & Finance FinTech and Financial Analytics

Certificate for Module (Applications of GenAI and DL in Finance and Business)
證書(單元 : 生成式人工智能及深度學習於金融與商業的應用)

Course Code
FN164A
Application Code
2375-FN164A

Credit
6
Study mode
Part-time
Start Date
18 Mar 2026 (Wed)
Next intake(s)
May 2026
Duration
30 hours
Language
English
Course Fee
Course Fee: HK$10,500 per programme (* course fees are subject to change without prior notice)
Deadline on 04 Mar 2026 (Wed)
Enquiries
2867 8331 / 2867 8424
2861 0278
Apply Now

Today and Upcoming Events

Accept new applications for Mar 26 intake! There will be practical classes in the computer laboratory.

Highlights

The programme aims to provide students with contemporary knowledge of generative artificial intelligence (GenAI) and deep learning (DL). It equips students with an essential understanding of neural networks, generative models, reinforcement learning, federated learning, natural language processing, and large language models. The programme also covers the use of computational tools and software for GenAI and DL, and explores issues in GenAI and DL from finance and business perspectives.

Programme Details

On completion of the programme, students should be able to

  1. evaluate various models of generative artificial intelligence (GenAI) and deep learning (DL), and explore their applications in finance and business;
  2. critically examine the trends, challenges, and opportunities of GenAI and DL in the commercial world;
  3. apply computational tools and software for GenAI and DL to enhance finance and business operations; and
  4. discuss practical applications of GenAI and DL in finance and business contexts.
Application Code 2375-FN164A Apply Online Now
Apply Online Now

Days / Time
  • Mon, Wed, Fri, 6:45pm - 10:05pm
Duration
  • 30 hours per programme
Venue
  • Hong Kong Island Learning Centre
  • Kowloon West Campus
  • Kowloon East Campus

Modules

Course Content :

(1) Principles of generative artificial intelligence (GenAI) and deep learning (DL)

  • Overview of AI development and artificial general intelligence (AGI)
  • Ecosystem of financial technology (FinTech) and AI
  • DL and neural networks: convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), transformers, large language models (LLMs), and small language models (SLMs)
  • Generative models overview: generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models and autoregressive models
  • Reinforcement learning (RL): Q-learning, and policy gradients
  • Natural language processing (NLP): sentiment analysis, text generation, and embeddings
  • General applications of GenAI and DL: research and analysis, intelligent systems, multilingual translation, cognitive and computational linguistics, speech and conversation analysis
  • Explainability and interpretability of GenAI and DL models in financial decision-making

(2) Introduction of computational tools and software for GenAI and DL

  • Basic Python programming
  • Python libraries for AI and DL algorithms
  • Computational tools and software for GenAI
  • Prompt engineering and GenAI
  • Low-code and no-code AI solutions
  • Model deployment and integration with business systems

(3) Applications of GenAI and DL in finance and business

  • Financial forecasting and financial analysis
  • Data augmentation for imbalance datasets and fraud detection with generative adversarial networks (GANs)
  • Cybersecurity, AI deepfake detection and financial crimes prevention
  • Human-computer interaction and autonomous business operations
  • Synthetic financial data generation, simulation and backtesting
  • Algorithmic trading with RL
  • Credit scoring and risk modelling
  • Portfolio optimisation with AI and DL
  • Automated financial summarisation and financial reporting
  • Regulatory technology (RegTech), ESG reporting, and regulatory compliance
  • Insurance technology (InsurTech) and wealth technology (WealthTech) powered by GenAI
  • GenAI for customer behavior simulation
  • Chatbots for banking and finance: voice-based financial assistants and virtual financial advisors with LLMs
  • Sentiment analysis and market prediction
  • Use of digital twins and simulation environments for business strategy testing

(4) Emerging trends and issues in GenAI and DL for finance and business

  • Challenges, opportunities and risks of GenAI and DL
  • AI Ethics and GenAI issues: hallucinations, bias and fairness
  • AI and robotics for business
  • AI agents for finance and business
  • Multimodal AI for finance
  • Quantum machine learning and DL in FinTech
  • Federated learning for privacy-preserving AI
  • Ambient intelligence and immersive AI for business
  • Web 4.0 and AI for financial innovations
  • Regulatory issues of GenAI in business and finance
  • Green AI and the environmental sustainability of model training and deployment

Assessment method: In-class Exercise + Group Project Presentation

 

Award

Upon successful completion of the programme, students who have passed the assessments with attendance no less than 70% will be awarded within the HKU system through HKU SPACE a "Certificate for Module (Applications of Generative Artificial Intelligence and Deep Learning in Finance and Business)".

Class Details

Timetable

Lecture

Date

Time

1

18 Mar 26 (Wed)

18:45-22:05

2

23 Mar 26 (Mon)

18:45-22:05

3

25 Mar 26 (Wed)

18:45-22:05

4

30 Mar 26 (Mon)

18:45-22:05

5

1 Apr 26 (Wed)

18:45-22:05

6

8 Apr 26 (Wed)

18:45-22:05

7

10 Apr 26 (Fri)

18:45-22:05

8

13 Apr 26 (Mon)

18:45-22:05

9

15 Apr 26 (Wed)

18:45-22:05

Remarks: Tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers 

Progression Path

Teacher Information

Dr. Simon Yiu

Background

Simon is the IT Department Head of a financial institution in Hong Kong, has handled many FinTech initiatives and projects, such as Algo trading, finance big data analytics, Robo-advisors and so on. Before that, he also worked for an AI, and Machine learning startup as co-founder and CTO which was located at a Hong Kong Science Park and participated in the University-organized Entrepreneurship Center in 2010, focusing on AI, Machine Learning, Big Data analytics and Natural language processing. Furthermore, he has hands-on programming experience in FinTech areas for over 10 years. Simon earned a Doctoral Degree in Business Administration from the City University of Hong Kong and a Master’s Degree in Data Science and Business Statistics from The Chinese University of Hong Kong.

Fee

Application Fee

HK$150

Course Fee
  • Course Fee: HK$10,500 per programme (* course fees are subject to change without prior notice)

Entry Requirements

Applicants should hold an Advanced Diploma, a Higher Diploma or an Associate Degree awarded by a recognised institution where the language of teaching and assessment is English. Those with a business, accounting, finance, economics, mathematics, statistics, science, engineering, IT or computer science background would have an advantage.

Applicants with other equivalent qualifications will be considered on individual merit.

**Please upload copy of HKID and proof of degree while applying online

Apply

Online Application Apply Now

Application Form Download Application Form

Enrolment Method
Payment Method
1. Cash, EPS, WeChat Pay Or Alipay

Course fees can be paid by cash, EPS, WeChat Pay or Alipay at any HKU SPACE Enrolment Centres.

2. 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 and applicant’s name. 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;
  • or mail the above documents to any of the HKU SPACE Enrolment Centres, specifying “Course Application” on the envelope. HKU SPACE will not be responsible for any loss of personal information and payment sent by mail.
3. VISA/Mastercard

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

4. Online Payment

Online application / enrolment is offered for most open admission courses (enrolled on first come, first served basis) and selected award-bearing programmes. Application fees and course fees of these programmes/courses can be settled by using "PPS by Internet" (not available via mobile phones), VISA or Mastercard. In addition to the aforesaid online payment channels, new and continuing students of award-bearing programmes with available online service, they may also pay their course fees by Online WeChat Pay, Online Alipay or Faster Payment System (FPS). Please refer to Enrolment Methods - Online Enrolment  for details.

Notes

  • If the programme/course is starting within five working days, application by post is not recommended to avoid any delays. Applicants are advised to enrol in person at HKU SPACE Enrolment Centres and avoid making cheque payment under this circumstance.

  • Fees paid are not refundable except under very exceptional circumstances (e.g. course cancellation due to insufficient enrolment), subject to the School’s discretion. In exceptional cases where a refund is approved, fees paid by cash, EPS, WeChat Pay, Alipay, cheque, FPS or PPS by Internet will be reimbursed by a cheque, and fees paid by credit card will be reimbursed to the credit card account used for payment. 

  • 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 to the relevant programme team for details.
  • Fees and places on courses cannot be transferrable 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 HK$120 will be levied on each approved transfer.
  • HKU SPACE will not be responsible for any loss of payment, receipt, or personal information sent by mail.
  • For payment certification, please submit a completed form, a sufficiently stamped and self-addressed envelope, and a crossed cheque for HK$30 per copy made payable to “HKU SPACE” to any of our enrolment centres.
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