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Accounting & Finance Finance and Compliance

Certificate for Module (Financial Fraud and Deepfake Detection)
證書(單元 : 金融詐騙與深偽偵測)

Course Code
FN129A
Application Code
2290-FN129A

Credit
6
Study mode
Part-time
Start Date
11 Jun 2025 (Wed)
Duration
30 hours
Language
English
Course Fee
HK$9,000
Deadline on 30 Apr 2025 (Wed)
Enquiries
2867 8392
2861 0278
Apply Now

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Highlights

This programme aims to equip students with up-to-date deepfake detection techniques to combat financial fraud. Various data sets and analytics will be examined in the context of different types of financial frauds. Students will learn how to leverage data analytics and deepfake detection to proactively mitigate fraudulent activities.
 

Programme Details

Intended Learning Outcomes

On completion of the programme, students should be able to

  1. analyse traditional techniques for financial fraud detection;
  2. evaluate different financial fraud analytics in the business context;
  3. assess deep learning techniques for deepfake detection; and
  4. explain blockchain technology to proactively mitigate fraudulent activities.
Application Code 2290-FN129A Apply Online Now
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Modules

Syllabus

1. Introduction to Financial Fraud

  • Definition and types of financial fraud
  • Historical examples of financial fraud
  • Impact of financial fraud on the economy

 

2. Traditional Techniques for Financial Fraud Detection

  • Handling of raw data sets for financial fraud detection
  • Statistical analysis and pattern recognition
  • Fraud risk assessment
  • Internal controls and audit trail
  • Data mining and predictive modeling

 

3. Fundamentals of Artificial Intelligence in Finance

  • Introduction to neural networks and model representation
  • Support vector machines
  • Unsupervised learning
  • Dimensionality reduction

 

4. Financial Fraud Analytics

  • Artificial intelligence and machine learning for fraud detection
  • Cluster analysis and neural networks
  • Text mining and social network analytics
  • Unsupervised learning analytics and fraud detection models
  • Big data analytics

 

5. Introduction to Deepfake Detection

  • Definition of deepfake and its application
  • Detection of manipulated images and videos
  • Deep learning techniques for deepfake recognition
  • Face recognition and body movement analysis

 

6. Blockchain Technology and Fraud Prevention

  • Introduction to blockchain technology
  • Applications of blockchain technology to prevent and halt fraudulent transactions
  • Audit and transparency through blockchain technology

 

7. Case Studies and Real-World Examples

  • Analysis of real-world examples of financial fraud and deepfake manipulation
  • Prevention and detection techniques for financial fraud and deepfake attacks
  • Challenges of financial fraud and deepfake detection in business context

 

Assessment and award

A "Certificate for Module (Financial Fraud and Deepfake Detection)" will be awarded within the HKU system through HKU SPACE to students who have satisfied the following criteria:

  • Achieve at least 70% attendance of the programme; and
  • Pass all the assessments

Type of Assessment

Description

Weighting

Group Presentation

One in-class case study presented by groups of 3 to 5 students (about 15 minutes each)

50%

Written Assessment

One individual written assignment to apply techniques of deepfake detection (about 1,000 words)

50%

Class Details

  Date Time
1 11 Jun 2025 (Wed)  7:00 p.m. - 10:00 p.m.
2 18 Jun 2025 (Wed)  7:00 p.m. - 10:00 p.m.
3 25 Jun 2025 (Wed)  7:00 p.m. - 10:00 p.m.
4 2 Jul 2025 (Wed)  7:00 p.m. - 10:00 p.m.
5 9 Jul 2025 (Wed)  7:00 p.m. - 10:00 p.m.
6 16 Jul 2025 (Wed)  7:00 p.m. - 10:00 p.m.
7 23 Jul 2025 (Wed)  7:00 p.m. - 10:00 p.m.
8 30 Jul  2025 (Wed)  7:00 p.m. - 10:00 p.m.
9 6 Aug 2025 (Wed)  7:00 p.m. - 10:00 p.m.
10 13 Aug 2025 (Wed)  7:00 p.m. - 10:00 p.m.

Teacher Information

Dr Albert Lam

The Chief Technology Officer and Chief Scientist at Fano Labs

Background

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 at Fano Labs, a deep-tech startup specializing in speech and language technologies. He also serves as an Adjunct Assistant Professor at HKU EEE and Geography. He is a Croucher Research Fellow. He is a member of the Expert Committee of Shenzhen Artificial Intelligence Industry Association. He is an active member of the IEEE. He is the founding Chair of the Social Media Subcommittee of the IEEE Computational Intelligence Society (CIS) and has also chaired various 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 of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Artificial Intelligence, and IEEE Transactions on Emerging Topics in Computational Intelligence. He is also a co-Editor-in-Chief of EAI Endorsed Transactions on Energy Web.

Fee

Course Fee
  • Course Fee : HK$9,000

Entry Requirements

Applicants shall hold a bachelor’s degree awarded by a recognised institution. If the degree or equivalent qualification is from an institution where the language of teaching and assessment is not English, applicants shall provide evidence of English proficiency, such as:

  1. an overall band of 6.0 or above with no subtests lower than 5.5 in the IELTS; or
  2. a score of 550 or above in the paper-based TOEFL, or a score of 213 or above in the computer-based TOEFL, or a score of 80 or above on the internet-based TOEFL; or
  3. HKALE Use of English at Grade E or above; or
  4. HKDSE Examination English Language at Level 3 or above; or
  5. equivalent qualifications.

Applicants with other qualifications will be considered on individual merit.

Apply

Online Application Apply Now

Application Form 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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