25881 AI-integrated Sustainable Finance
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 24 credit points of completed study in spk(s): C04048 Master of Finance OR 24 credit points of completed study in spk(s): C04258 Master of Finance (Extension) OR 24 credit points of completed study in spk(s): C07021 Graduate Diploma Finance OR 24 credit points of completed study in 24Credit Points spk(s): MAJ08984 c 36cp Finance Major MBA
The lower case 'c' after the subject code indicates that the subject is a corequisite. See definitions for details.
These requisites may not apply to students in certain courses. See access conditions.
Description
With the advent of climate change, health crises, and global pollution there is a global trend and need for responsible investing and sustainable finance practices. Artificial Intelligence (AI) has tremendous potential to accelerate and industrialise evidence-based, data-driven sustainable finance, ESG-related decision making, and climate risk management. Natural language processing (NLP) and spatial AI have become leading technologies in this area, as have simulations, digital twins, virtual worlds, and omniverse. Asset managers, asset owners, banks, insurance companies, regulators, and central banks are increasingly applying ESG criteria as part of their analysis process to identify material risks and growth opportunities. Developments in AI and machine learning have led to the creation of a new type of ESG data that do not necessarily rely on information provided by companies. In this subject we review the use of AI in the ESG field: textual analysis to measure firms’ incidents or verify the credibility of companies’ concrete commitments, satellite and sensor data to analyse companies’ environmental impact or estimate physical risk exposures, machine learning to fill missing corporate data (e.g., greenhouse gas emissions). We discuss potential challenges, in terms of transparency, manipulation risks and costs associated with these new data and tools.
Subject learning objectives (SLOs)
| 1. | Examine AI technologies that facilitate ESG compliance, enhance sustainable finance practices, and support effective climate risk management in the finance industry |
|---|---|
| 2. | Evaluate and mitigate ethical risks associated with AI implementation in financial contexts |
| 3. | Analyse and solve complex finance-related problems using AI-driven approaches |
| 4. | Communicate AI-driven insights effectively to diverse stakeholders |
| 5. | Critically reflect on ethical considerations and social responsibility in AI applications for finance |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the following program learning objectives:
- Implement critical, analytical, and innovative problem-solving skills to identify and propose effective solutions to contemporary finance issues using AI technologies (1.1)
Contribution to the development of graduate attributes
The subject contributes to the aim of preparing students for fulfilling and effective careers in business and finance by bridging the gap between AI and finance, addressing the industry's emerging needs and equipping students with the necessary knowledge, skills, and ethical considerations to excel in the evolving field of AI in finance. It contributes particularly to the development of the following graduate attribute(s):
- Intellectual rigour and innovative problem solving
- Communication and collaboration
- Social responsibility and cultural awareness
- Professional and technical competence
Teaching and learning strategies
Students will have pre-readings to complete before in class lectures/seminars and discussions, including a mix of chapters, journal articles, industry reports, and press. Additional (optional) extension materials will be provided in the form of recorded videos and further readings to allow students to delve deeper into topics of particular interest. Assessments will be used for students to get feedback on their comprehension of the subject matter. Students will present their application proposals in class to obtain instant feedback and promote the exchange of ideas. Students will work in small groups on empirical exercises to encourage cohort building and peer learning.
Content (topics)
- AI and Sustainable Finance
- Natural Language Processing (NLP) in ESG
- Spatial AI and Environmental Impact Analysis
- Machine Learning for ESG data creation
- Challenges in AI-integrated ESG
- Role of Financial Institutions in AI-driven ESG
- Future Trends and Challenges
Assessment
Assessment task 1: Empirical Assignment (Group)
| Objective(s): | This addresses subject learning objective(s): 3 and 4 |
|---|---|
| Weight: | 25% |
| Length: | Students will submit Python notebook (roughly 9 hours to construct) |
| Criteria: |
|
Assessment task 2: Case Study Assignment (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 |
|---|---|
| Weight: | 30% |
| Length: | Students will submit a (maximum) 3,000-word report |
| Criteria: |
|
Assessment task 3: AI Application Proposal (Individual)*
| Intent: | Part A: Presentation Pitch (15%)* |
|---|---|
| Objective(s): | This addresses subject learning objective(s): 3, 4 and 5 This addresses program learning objectives(s): 1.1 |
| Weight: | 45% |
| Length: | Part A: Presentation Pitch - Presentation duration will be 8-10 minutes plus 5 minutes for questions. |
| Criteria: | Part A*
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. Part B
|
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.