Merge and acquire: GenAI in Finance
In this case study, postgraduate finance students critically and ethically used GenAI tools to analyse ASX-listed companies for private equity valuation and M&A scenarios, comparing traditional and GenAI-assisted methods through anonymized peer review and guided reflection.
Summary
This case study details a postgraduate finance teaching activity where students critically and ethically engaged with GenAI tools—such as ChatGPT, Claude, and Aila—while analysing ASX-listed companies for private equity valuation and M&A scenarios. The activity was structured to compare traditional and GenAI-assisted methods, with anonymised peer review and guided reflection on reliability, limitations, and ethical use of AI in financial analysis. Students benefited from scaffolded support, collaborative group work, and formative feedback, resulting in improved critical thinking, ethical awareness, and evaluative judgment. The activity also identified challenges like over-reliance on GenAI, leading to planned adaptations for future iterations, including enhanced training and refined assessment rubrics.
Subject information
Subject
Year and Semester used
Cohort
Activity/assessment overview
Postgraduate finance students undertook a private equity valuation and M&A analysis of ASX-listed companies using either traditional methods or GenAI tools, fostering critical thinking and ethical reflection. Anonymized submissions were peer-reviewed using a structured rubric to support objective comparison. The activity was scaffolded with academic guidance and concluded with structured reflection on the reliability, limitations, and ethical use of GenAI in financial analysis.
This activity addresses the growing need for AI literacy in finance education by developing students' ability to critically and ethically engage with GenAI tools while retaining strong disciplinary skills. It prepares students for future professional contexts where AI is increasingly integrated into financial analysis and decision-making.
Learning outcomes
Critically evaluate the validity, reliability, and limitations of AI-generated versus traditionally developed financial analyses, aligning with students’ ability to apply valuation techniques and analyse key issues in restructuring transactions.
Exercise ethical judgment and professional reflexivity in the use, attribution, and disclosure of AI-assisted work, reinforcing the broader goal of applying professional standards and decision-making in complex finance contexts.
Develop comparative insights into restructuring and valuation approaches using GenAI and traditional tools, directly supporting the intended outcomes of understanding cashflow-based and multiples-based valuation methods and strategic issues in corporate transactions.
GenAI usage
GenAI tool(s) used
Student were free to choose but needed to declare which GenAI tools they used (for example, ChatGPT, DeepSeek, Claude, Copilot, Aila).
How GenAI was integrated
GenAI was embedded through a three-phase model involving comparative analysis, peer feedback, and ethical reflection. Student groups were split: one subgroup used traditional methods, the other used GenAI tools. GenAI users documented prompts and outputs for later discussion, while submissions remained anonymous for unbiased peer review via FeedbackFruits.
Students engaged in prompt design, iterative refinement, and critical evaluation of GenAI outputs, learning to assess reliability, identify biases, and compare results against real financial data. Staff from the Academic Skills and Learning Design team provided scaffolded support through workshops on responsible GenAI use, covering prompting, source verification, transparency, and ethical considerations.
Peer feedback followed a structured rubric, fostering evaluative judgment and constructive critique. Final reflections encouraged students to consider the strengths and limitations of GenAI, reinforcing feedback literacy and ethical awareness. The focus was on process, not output quality, mirroring industry expectations where GenAI supports but does not replace human judgment.
Level of student engagement
Individual task, group work, tutor-supported, whole-cohort activity.
Students engaged through collaborative group work, with sub-groups applying either traditional or GenAI-assisted methods. This was complemented by individual reflective tasks, where students critically examined their approach and peer feedback. Engagement was further supported by academic staff, who facilitated discussions on ethical GenAI use and provided formative guidance throughout the activity.
Pedagogical approach
Teaching strategy
Project-based learning combined with authentic assessment and peer review to simulate real-world financial analysis and decision-making.
Type of activity
Authentic assessment task integrated with structured peer review and reflective practice.
Scaffolding and support provided
Students received structured guidance on responsible GenAI use through in-class discussions, detailed assessment instructions, and a workshop delivered by staff from the Academic Skills and Learning Design team. This included support on prompt development, source verification, attribution, and ethical boundaries. A structured rubric guided peer review, emphasising analytical depth and transparency. Ethical considerations and academic integrity were explicitly embedded in both the task design and classroom discussions.
Outcomes and reflections
Student response
Students engaged enthusiastically with the activity, appreciating the opportunity to explore both GenAI and traditional approaches to financial analysis. Feedback indicated high levels of participation in peer review and in-class discussions, with students valuing the structured comparison between methods. Reflective submissions revealed increased confidence in evaluating GenAI outputs, heightened awareness of ethical considerations, and a stronger understanding of how GenAI can be responsibly integrated into professional finance workflows.
Observed benefits
Student surveys and reflections highlighted several benefits, including improved critical thinking, deeper engagement with financial analysis tasks, and greater awareness of ethical GenAI use. The activity enhanced students’ ability to synthesize AI-generated insights with traditional financial data, promoting metacognitive skills and evaluative judgment. Peer review also fostered collaboration and feedback literacy, while the flexibility of AI tools supported more inclusive participation across varying skill levels.
Challenges encountered
Some students demonstrated over-reliance on GenAI outputs without adequate critical evaluation, highlighting the need for stronger prompt literacy and source verification skills. There were concerns around uneven levels of GenAI familiarity, leading to inconsistent depth in analyses. Ethical uncertainties, such as appropriate attribution, disclosure, and trust in AI-generated content, also emerged, underscoring the importance of continued academic guidance. Technical barriers, including documenting prompts and managing multiple GenAI platforms, were minor but noted.
Adaptations planned or suggested
Future iterations will include earlier and more structured training on prompt engineering, source validation, and critical assessment of GenAI outputs. A pre-activity diagnostic may be introduced to gauge students’ baseline GenAI literacy, allowing for targeted support. Rubrics will be refined to more explicitly assess ethical use and transparency in AI-assisted work. Additionally, enhanced integration with digital literacy workshops and clearer guidance on documenting GenAI engagement will be implemented to support consistency and reduce over-reliance.
Contributors
- Linh Nguyen, lecturer (Department of Finance)
- Nate Grieve, learning designer
- Timsy Gupta, learning designer