Dr. P.S. Vivien Chan
Ms. W.H. Annie Chan
Dr. K.P. Chow
Miss Ao Shen
Key topics include: Handling of raw data sets for fraud detection; Applications of descriptive analytics, predictive analytics and social network analytics to construct fraud detection models; Financial Fraud Analytics challenges and issues when applied in business context.
Required to have basic knowledge about statistics concepts.
| Course Learning Outcomes | Relevant Programme Learning Outcome |
| CLO1. Able to explain the most commonly used data analytics techniques for fraud detection. | PLO.4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 |
| CLO2. Able to apply the fraud detection data analytics knowledge learned in this course to financial frauds. | PLO.4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 |
| View Programme Learning Outcomes | |
Required to have basic knowledge about statistics concepts:
- descriptive statistics (including,
means, standard deviation, correlation,
confidence intervals, hypothesis
testing);
- data handling (including, use of
Excel, SQL)
- data visualization (including, bar
plots, pie charts, histograms, etc)
| Course Content | No. of Hours | Course Learning Outcomes |
| Introduction to financial fraud detection, prevention and analytics | 3 | CLO1, CLO2 |
| Handling of raw data sets for fraud detection (including, techniques for sampling, handling missing values, outliers, etc) | 3 | CLO1, CLO2 |
| Employing supervised learning analytics to construct fraud detection models | 9 | CLO1, CLO2 |
| Employing unsupervised learning analytics to construct fraud detection models | 6 | CLO1, CLO2 |
| Employing social network analytics to construct fraud detection models | 3 | CLO1, CLO2 |
| Challenges when applying fraud detection model in business context, and financial fraud analytics in a broader perspective | 6 | CLO1, CLO2 |
| Description | Type | Weighting * | Examination Period ^ | Course Learning Outcomes |
| Assignments and/or Project | Continuous Assessment | 60% | - | CLO1, CLO2 |
| Written exam covers all contents taught | Written Examination | 40% | May 10 to May 29, 2021 | CLO1, CLO2 |
|
* The weighting of coursework and
examination marks is subject to approval
^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office. Students must oblige to the examination schedule. Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period. Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering. |
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| Date | Time | Venue | Remark | |
| Session 1 | 29 Jan 2021 (Fri) | 10:30am - 1:30pm | GH-WGW | Hybrid |
| Session 2 | 5 Feb 2021 (Fri) | 10:30am - 1:30pm | GH-WGW | Hybrid |
| Session 3 | 26 Feb 2021 (Fri) | 10:30am - 1:30pm | MT-T2 | Hybrid |
| Session 4 | 5 Mar 2021 (Fri) | 10:30am - 1:30pm | GH-WGW | Hybrid |
| Session 5 | 19 Mar 2021 (Fri) | 10:30am - 1:30pm | GH-WGW | Hybrid |
| Session 6 | 26 Mar 2021 (Fri) | 10:30am - 1:30pm | GH-WGW | Hybrid |
| Session 7 | 9 Apr 2021 (Fri) | 10:30am - 1:30pm | GH-WGW | Hybrid |
| Session 8 | 16 Apr 2021 (Fri) | 10:30am - 1:30pm | GH-WGW | Hybrid |
| Session 9 | 23 Apr 2021 (Fri) | 10:30am - 1:30pm | GH-WGW | Hybrid |
| Session 10 | 30 Apr 2021 (Fri) | 10:30am - 1:30pm | Online | Zoom |
| GH - Graduate House | ||||