Applied Generative Artificial Intelligence (AI) for Accounting and Finance
About This Course
Most accounting professionals using Generative Artificial Intelligence (GenAI) lack a principled approach to tool selection. They rely on familiar platforms without assessing whether these tools are appropriate for the task, or whether sensitive financial data should be shared with cloud-based systems at all.
The course addresses this gap through a structured, practice-oriented approach. Participants will develop the judgement to select and apply the right tools across a broader ecosystem beyond ChatGPT and Copilot, including Claude, Gemini, Grok, NotebookLM, and open-source models running locally via Ollama. The focus shifts from ""what can Artificial Intelligence (AI) do?"" to ""which tool is appropriate for this task, and does the data allow it?""
This distinction is critical in professional settings. Cloud-based tools trasmit data externally, which may not be suitable for client financials, confidential reports, or information governed by the Personal Data Protection Act (PDPA) and professional standards. Participants will learn to assess data sensitivity, apply appropriate safeguards, and leverage locally-run open-source models when data cannot leave the organisation's environment.
This course equips accounting and finance professionals with practical capabilities to confidently select and apply GenAI tools across accounting workflows, balancing performance, risk, and compliance in real-world contexts.
What You'll Learn
- Distinguish between different Generative Artificial Intelligence (GenAI) tools (Claude, Gemini, Grok, open-source models, Notebook LM, among others besides ChatGPT and Copilot) and their relative strengths for different accounting tasks
- Apply browser-based Artificial Intelligence (AI) tools to research, knowledge synthesis, and framework development tasks, including researching tax law, understanding accounting standards, drafting procedure templates, and synthesising regulatory guidance
- Assess data sensitivity and select appropriate GenAI tools based on whether data can be shared with cloud platforms or must remain within the organisation's environment
- Use open-source models running locally through Ollama for confidential accounting data that cannot be uploaded to cloud services
- Construct effective prompts for research and knowledge tasks using cloud-based tools
- Critically evaluate AI-generated outputs for accuracy and appropriateness to the task at hand
- Design a multi-tool approach to a complex accounting problem, justifying tool selection based on data sensitivity and task requirements
Entry Requirements
Participants must bring their own personal laptops where they can install their own apps or Chrome apps without administrator restriction. Laptops should have at least 8GB RAM to be able to run open-source models locally