Juergen H. Rahmel
The course aims to equip students with the academic and
practical knowledge about contemporary application scenarios for Generative
AI in financial services. Students will learn about the potential benefits,
ethical concerns and model risks of GenAI, the up-to-date regulatory
environment as well as the internal model risk and governance practice of
regulated financial institutions. Detailed objectives as per Topics below.
| Course Learning Outcomes | Relevant Programme Learning Outcome |
| CLO1. Students will understand the importance of data for use in (generative) AI and the practical challenges in financial institutions to leverage their existing data sources | PLO. 4, 5, 13, 14 |
| CLO2. Students will get insights into the expectations of authorities into the realization of use cases for Generative AI and understand the various ways to demonstrate regulatory compliance with Generative AI | PLO. 5, 6, 10 |
| CLO3. Students will understand the fraud and risk use cases where Generative AI will contribute to better understanding, modeling and evaluation of real-world use cases | PLO. 7, 10, 12, 13 |
| CLO4. Students will understand the ways Generative AI can assist and augment humans in their customer-facing roles | PLO. 4, 7, 8, 9, 11, 12 |
| CLO5. Students will get a deeper understanding of the legal and ethical concerns when using Generative AI in a regulated financial institution. | PLO. 6, 10, 14 |
| View Programme Learning Outcomes | |
However, some understanding of basics of Artificial Intelligence and Machine Learning will be beneficial.
| Course Content | No. of Hours | Course Learning Outcomes |
| 1. Introduction to Generative AI | 3 | CLO 1, 5 |
| 2. Data generation and augmentation in finance | 3 | CLO 1, 2, 5 |
| 3. The regulatory landscape in Generative AI | 3 | CLO 2, 3 |
| 4. Customer personalization through conversational agents | 3 | CLO 3, 4 |
| 5. Staff-augmentation with generative AI | 3 | CLO 3, 4 |
| 6. Fraud detection and risk modeling | 3 | CLO 3, 4, 5 |
| 7. Financial sentiment analysis | 3 | CLO 3, 4, 5 |
| 8. Regulatory compliance and stress testing | 3 | CLO 2, 5 |
| 9. Ethical considerations and legal challenges | 3 | CLO 1, 5 |
| 10. Future trends and challenges | 3 | CLO 1, 2 |
| Description | Type | Weighting * |
Tentative Assessment Period / Examination Period ^ |
Course Learning Outcomes |
| Assignment | Continuous Assessment | 40% | - | CLO 1-5 |
| Written exam covering all course contents | Written Examination | 60% | 8 - 27 May 2025 | CLO 1-5 |
|
* 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. |
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| Date | Time | Venue | Remark | |
| Session 1 | 20 Jan 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 2 | 27 Jan 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 3 | 10 Feb 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 4 | 17 Feb 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 5 | 24 Feb 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 6 | 3 Mar 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 7 | 17 Mar 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 8 | 24 Mar 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 9 | 31 Mar 2025 (Mon) | 7:00pm - 10:00pm | CYP-P4 | |
| Session 10 | 26 Apr 2025 (Sat) | 3:30pm - 6:30pm | CYP-P4 | |
| CYP - Chong Yuet Ming Building | ||||