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Accounting & Finance FinTech and Financial Analytics

New Course

Certificate for Module (Financial Crimes Analytics)
證書(單元 : 金融罪行分析)

Course Code
FN160A
Application Code
2370-FN160A

Credit
6
Study mode
Part-time
Start Date
09 Feb 2026 (Mon)
Next intake(s)
Apr 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 26 Jan 2026 (Mon)
Enquiries
2867 8331 / 2867 8424
2861 0278
Apply Now

Today and Upcoming Events

17
Dec 2025
(Wed)

Any correlation between Bitcoin, Ethereum and Gold? And now Stablecoin Explained (17 Dec 2025)

Bitcoin and Ethereum are the most dominant cryptocurrencies, both accumulative account for over 90% in term of market capitalization excluding stablecoin, out of more than two thousands currently in the market. They are both in a form of digital asset that trades via various DEX or centralizated regulated trading platforms. However there are quite many key differences among them though. Bitcoin (BTC) is designed as a monetary storage (some proclaim as alternative of Gold) and medium of transaction and as an alternative to fiat currency. Ethereum (ETH), otherwise, is intended for complex smart contracts or dApps which contribute and act as key infrastructure of the emerging Web3.0 future. In light of recent rapid further innovation (like staking protocol) and adoption, the price of BTC and ETH have been risen more than double in past 12 months and also exhibited a huge volatility. The speaker will give a brief introduction of above crypto with some attention drawn to the relationship and correlation among Bitcoin, Ethereum, Gold, XRP and S&P500 – as shown in below charts. The speaker will then also talk about the latest development and impacts of the recently passed GENIUS Act in U.S. and the Stablecoins Ordinance (Cap. 656) in HK. To say, Stablecoin per se. is NOT referring the price to be fixed or stable, it’s referring to link or collateralize by some tangible asse shifting from no intrinsic value issue. At 10 Oct 2025, one of most selloff in crypto, USDe has experienced flash crash to as low as 0.65 USDEUST, per shown below. Source: Bloomberg   Language: Cantonese (Supplemented with English)

Accept New Applications for Feb 2026 intake! There will be practical classes in the computer laboratory. Contemporary knowledge of financial crime analytics and detection methods will be introduced. SAS programming for data analytics and model building will be covered. Application of SAS and related techniques to identify financial crimes will be illustrated.

Highlights

The programme aims to provide students with contemporary knowledge in financial crimes analytics and detection methods. It equips them with practical skills in Statistical Analysis System (SAS) programming to perform data analytics and build data models. Additionally, the programme demonstrates how SAS and related techniques can be applied to identify financial crimes effectively

Programme Details

On completion of the programme, students should be able to

  1. assess the nature and impacts of financial crimes, as well as identify the relevant data sources;
  2. discuss regulatory and compliance issues, and preventive measures against financial crimes;
  3. apply Statistical Analysis System (SAS) to perform data analytics and develop data models; and
  4. illustrate analytical techniques for detecting financial crimes.
Application Code 2370-FN160A Apply Online Now
Apply Online Now

Days / Time
  • Mon, Wed, 7:00pm - 10:00pm
Duration
  • 30 hours per programme
Venue
  • Hong Kong Island Learning Centre
  • Kowloon West Campus
  • Kowloon East Campus

Modules

Course Content :

(1) Introduction to financial crimes and detection  

  • Overview of financial crimes and new technologies
  • Bribery and corruption: detecting patterns of illicit payments
  • Embezzlement: recognising suspicious transactions involving the misappropriation of funds
  • Fraud: fraudulent transactions, including identity theft and insurance fraud 
  • Insider trading: identifying individuals who trade securities based on material and non-public information
  • Money laundering: detecting the placement, layering, and integration stages of illicit funds 
  • Tax evasion: identifying patterns of unreported income or tax avoidance
  • Terrorist financing: identifying transactions related to the funding of terrorist organisations
  • Regulatory frameworks and statutory compliance
  • Data sources in financial crime detection

(2) Statistical Analysis System (SAS) programming, data analytics and modelling

  • Introduction to SAS OnDemand for Academics
  • Basic SAS programming and key analytical features
  • Data cleaning and preparation
  • Data exploration and visualisation
  • Model development and validation
  • Model deployment and evaluation

(3) Financial crimes analytics and applications of SAS

  • Predictive analytics for analysing historical data to predict future risks and identify potential threats
  • Machine learning for developing models that learn from data to identify anomalies and patterns
  • Network analytics for examining the relationships between entities and transactions to uncover connections to suspicious activities
  • Dynamic segmentation for grouping clients or transactions into segments based on risk profiles to improve detection accuracy
  • Natural language generation for automating the creation of narratives for regulatory reports
  • Risk management and prevention measures for financial crimes

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 (Financial Crimes Analytics)".

Class Details

Timetable

Lecture Date Time
1 9 Feb 26 (Mon) 19:00-22:00
2 11 Feb 26 (Wed) 19:00-22:00
3 23 Feb 26 (Mon) 19:00-22:00
4 25 Feb 26 (Wed) 19:00-22:00
5 2 Mar 26 (Mon) 19:00-22:00
6 4 Mar 26 (Wed) 19:00-22:00
7 9 Mar 26 (Mon) 19:00-22:00
8 11 Mar 26 (Wed) 19:00-22:00
9 16 Mar 26 (Mon) 19:00-22:00
10 18 Mar 26 (Wed) 19:00-22:00

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

Progression Path

Teacher Information

Dr Francis Lau

Background

Dr Lau is a seasoned financial data analytics practitioner, and a professional trainer in finance-related disciplines. He has over 22 years of experience in business planning, data analytics, management information, regulatory compliance, and risk management acquired from working for multinational analytics vendors, banks, consulting firms, and universities. Dr Lau is a subject matter expert in applying data analytics to enhance business decision-making, constructing quantitative models to gauge business performance, and streamlining the management reporting processes. In addition to industry experiences, Dr Lau also has extensive exposures in developing and delivering academic and professional education programs for financial institutions, professional associations and universities. Dr Lau is a well-recognized trainer in compliance, data science, financial markets, risk management, and sustainability.

Mr Ken Choi

Background

Mr Choi has over ten years of practical working experience across start-ups, data software companies, retail banking, insurance companies, listed companies, and large multinational corporation firms in areas of AI, business intelligence, big data and machine learning. He works as a senior project data analyst in charge of business process automation and data projects powered by AI, Machine Learning and Deep Learning. Moreover, he would like to leverage the Web 3.0 technology interaction with FinTech. Furthermore,  he applies business analytics to predict the revenue growth of the corporation. He has been actively teaching and coaching in different institutes and schools over the decade. He is a professional in communicating abstract data concepts and theory to non-tech students efficiently and effectively. He holds a Bachelor (Hons) in Statistics and Operation Research from HKBU and a Master of Statistics and Risk Management from HKU. He is a certified FRM (Financial Risk Manager), 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 William Ma

Background

Mr Ma is a practitioner in data-driven solutions for business operations and user analytics. He holds a Master of Science in Computer Science (Financial Computing Stream) with Distinction from the University of Hong Kong, specialising in algorithmic trading, financial analytics, deep learning, and big data management. He completed his Bachelor of Business Administration in Professional Accounting and Information Systems with First Class Honours from The Hong Kong University of Science and Technology. Before transitioning into the business analytics field, Mr. Ma served as a senior accountant at Ernst & Young and is a Certified Public Accountant (CPA) in 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, Higher Diploma or Associate Degree awarded by a recognised institution where the language of teaching and assessment is English. Those with a background in business, accounting, finance, economics, mathematics, statistics, science, engineering, IT, computer science, legal or criminology 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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