AI for Finance and Accounting
Overview
Future industry leaders will have to manage AI’s growing role in finance and accounting. This unit introduces students to the practical applications of this technology, without requiring programming experience. Students will work with supervised and unsupervised learning and gain hands-on experience with text analysis, retrieval augmented generation, and prompt engineering. They will explore fundamental use cases, utilise Large Language Models to assist in coding, implement domain-specific applications, and engage with the latest research. Students will leave the unit with a practical understanding of how AI and machine learning can be used to solve real-world problems.
Requisites
01-June-2025
Learning outcomes
Students who successfully complete this unit will be able to:
- Apply important machine learning and generative artificial intelligence methods and interpret outputs
- Analyse and apply model specification, model evaluation and diagnostic testing procedures in machine learning and artificial intelligence models for finance and accounting applications.
- Analyse and solve problems using machine learning software, large language models, and business data
- Work in groups to solve machine learning and artificial intelligence problems, synthesise solutions from multiple outputs, and clearly communicate results and interpretations
Teaching methods
All applicable locations
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
On Campus Class |
2.00 | 12 weeks | 24 |
| Online Lecture (asynchronous) | 1.00 | 12 weeks | 12 |
Unspecified Learning Activities (Independent Learning) |
9.5 | 12 weeks | 114 |
| Total | 150 |
Assessment
| Type | Task | Weighting | ULOs |
|---|---|---|---|
| Assignment 1 | Group | 20-30 % | 1,2,3 |
| Assessment | Individual | 40-60 % | 1,2,3 |
| Presentation and Report | Individual | 20-30 % | 1,2,3,4 |
Content
- Introduction to Modeling and Data Wrangling
- Cross Validation and Model Evaluation Techniques for Finance and Accounting
- Linear Models for Financial Prediction (multi-variate regression, logistic)
- Classification for Business Problems
- Neural Networks for Uncovering Patterns in Business Data
- Prompt Engineering for Finance and Accounting
- LLM Bias and Hallucination Mitigation
- Retrieval Augmented Generation
- Text Analysis for financial documents and news
- Latest developments and future opportunities
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.