FITE7410A - Financial fraud analytics

Semester 1, 2022-23

Professors
P.S. Vivien Chan
W.H. Annie Chan
Teaching assistant
Yanan Gong
Syllabus This course aims at introducing various analytics techniques to fight against financial fraud.  These analytics techniques include, descriptive analytics, predictive analytics, and social network learning.  Various data set will also be introduced, including labeled or unlabeled data sets, and social network data set.  Students learn the fraud patterns through applying the analytics techniques in financial frauds, such as, insurance fraud, credit card fraud, etc.

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.
Introduction by Professor This course introduces basic techniques to uncover financial frauds through the use of the latest data analytics techniques. The objective of this course is not on the mathematics or theory, but on the application of analytics techniques in financial frauds. Equations and formulas would only be included when required.
Learning Outcomes
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
Pre-requisites

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)
Compatibility -
Topics covered
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
 
Assessment
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% 8 - 23 December 2022 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 are obliged to follow 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.
Course materials Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke (2015). Fraud Analytics using Descriptive, Predictive, and Social Network Techniques, 1st ed, John Wiley & Sons Inc.
Session dates
Date Time Venue Remark
Session 1 7 Sep 2022 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 2 14 Sep 2022 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 3 21 Sep 2022 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 4 28 Sep 2022 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 5 5 Oct 2022 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 6 19 Oct 2022 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 7 26 Oct 2022 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 8 2 Nov 2022 (Wed) 7:00pm - 10:00pm Online Recorded Lecture
Session 9 9 Nov 2022 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 10a 14 Nov 2022 (Mon) 7:00pm - 9:00pm KK-102 Project presentations
Session 10b 16 Nov 2022 (Wed) 5:30pm - 10:30pm MW-T5 Project presentations
CYP - Chong Yuet Ming Building KK - K.K. Leung Building MW - Meng Wah Complex
Add/drop 1 September, 2022 - 15 September, 2022
Maximum class size 150
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