Course Overview

Generative AI for Finance : Artificial Intelligence (AI) tools designed for accounting and finance are transforming daily workflows across reporting, variance analysis, tax research, and data processing.

Ignoring Generative AI capabilities and their impact on operational productivity is no longer a viable option for modern finance teams.  

This 2-day practical workshop is structured specifically as an introductory-to-intermediate public programme tailored for corporate accounting and finance professionals. The course provides a hands-on sandbox environment using leading free-access Generative AI platforms (including ChatGPT, Google Gemini, Anthropic Claude, and Microsoft Copilot).

Participants will work directly with realistic, anonymized finance files to learn how accessible AI tools can reduce repetitive manual workloads, shorten turnaround times, and establish repeatable productivity gains.  

In addition to practical skill-building, this public programme equips decision-makers and finance practitioners with the knowledge required to evaluate paid enterprise AI subscriptions (such as Copilot for Microsoft 365, ChatGPT Team/Enterprise, or Claude for Business) before committing corporate budgets.

This course focuses on AI-assisted human copilot workflow using Gen AI to draft, clean, analyze, and summarize under direct human supervision.

It does not cover fully autonomous agentic execution, robotic process automation (RPA), complex software engineering, or API integrations.

Scope Guidelines & Key Program Caveats

To ensure clear expectations and maximum training efficacy, this Generative AI for Finance Productivity assumes participants are new or early-stage users of Generative AI tools.  

Skill Level: No prior experience with programming, Python, or advanced AI techniques is required.

• Free-Access Tools & Usage/Token Limits: Practical exercises utilize freely accessible web interface versions of major AI platforms (ChatGPT Free, Claude Free, Gemini Free, Microsoft Copilot Free). Free-tier tools are subject to vendor-imposed rate limits, token caps, and temporary usage throttling during peak hours.
• Account Registrations: Participants should register for free accounts on OpenAI, Anthropic Claude, Google, and Microsoft prior to Day 1.
• Instructions on managing these limits effectively are embedded in the curriculum.   Human-in-the-Loop Assistance vs. Autonomous Robotics:
• Organizational Sandbox Focus: Exercises are designed around anonymized sample datasets to teach data sanitization and privacy controls before teams handle proprietary corporate data
• Tools: Laptops and keyboard. For corporate laptops, please consult your IT departments to enable free access to generative AI apps.

Course Objective

On completion of the programme, participants will be able to:-

1. Evaluate Generative AI Tools Strategically: Differentiate the strengths, limitations, token constraints, and cost structures of ChatGPT, Gemini, Claude, and Copilot to select the right tool for specific finance tasks.
2. Execute Data Sanitization & Control: Apply data masking, anonymization techniques, and verification frameworks to safeguard confidential corporate financial data prior to AI prompting.
3. Master Finance Prompt Engineering: Craft structured, multi-step prompt templates using the Role-Task-Context-Format framework to perform data cleaning, invoice processing, and financial statement analysis.
4. Automate Management Narrative & Analysis: Utilize AI to draft preliminary budget variance commentaries, treasury consolidation summaries, and financial ratio performance reviews.
5. Accelerate Technical Research: Prompt Gen AI tools to synthesize complex accounting standards, tax legislation, and legal contract clauses into executive summaries.
6. Build Custom Prompt Libraries & AI Helpers: Design custom prompt templates and lightweight AI assistants (e.g., Custom GPTs/Projects) for recurring departmental workflows.
7. Formulate an Enterprise AI Roadmap: Assess paid subscription tiers versus free tiers and draft internal AI usage guidelines to prevent data leaks and AI hallucinations

Methodology

Where appropriate, activities will include the following:

• Lecture Notes Deck
• Pre-Post Test and Gamified Spot Quizzes
• Hands-on case based and practical exercises using and anonymised datasets
• Question and answer sessions.

Who Should Attend

Accountants, finance professionals who are open and wants to discover and potentially leverage Generative Artificial Intelligence (AI) version tools for tasks productivity improvement.

Course Content

Module 1: How AI can be leveraged in Accounting and Finance

• Key concepts simplified: Machine Learning, Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI..
• Dos and Don’ts in crafting prompts for generating output for AI Tools.
• Example of use cases for accounting and finance.
• Setting your own departmental finance use cases goals for AI usage.
• AI tools Free vs Pro: feature comparison relevant to finance work
• Setting and personalizing Gen AI safely in a corporate or SME environment
• Which tool to use for different Finance tasks; data privacy, masking, AI limitations and validation.
• Understanding tool limitations: Token limits, rate caps, context windows, and hallucination risks.

Module 2: Prompt Engineering techniques

• The anatomy of a great finance prompt: Role, Task, Context and Format
• Prompt chaining for multi-step accounting workflows
• Negative prompting: excluding irrelevant or harmful outputs
• Configuring Gen AI in agentic mode for certain generative AI tools (CoPilot, Gemini, Claude, ChatGPT)
• Building reusable prompt templates for various accounting and finance tasks
• Iterating and refining outputs — giving productive feedback to Generative AI to ‘train’ your AI.
• Managing free-tier token limitations: Techniques for chunking large financial text and datasets

Module 3: Practical session – Prompt crafting Part 1

• Sanitise a sample Finance dataset and test selected AI tools on common task
• Analysis and Interpretation of sample financial statements financial health and performance.
• Analyse mock budget/actual data and identify key movements, risks and actions
• Formatting sample and cleaning excel data using customized generative AI tools in agentic mode.

Module 4: Practical sessions – Prompt crafting Part 2

• Treasury cash and facility analysis – Consolidate sample bank balances and compare multi-bank term sheets
• Review sample invoices/supporting documents and analyse an AR ageing dataset

Module 5: Accounting, Legal Contracts, Taxation Technical Standards Research Partner

• Prompting basics to research and summarise complex accounting, legal technical standards and taxation rules.
• Contract Summarization: Extracting key financial clauses, payment terms, penalties, and renewal dates from legal contracts

Module 6: Creating your first simple AI agents, AI chatbot and Customised GPT and roadmap

• Set your department goal on task mini automation
• Decide which workflows should be AI driven and which tasks requires human intervention
• Craft your tasks customized prompt
• Building Custom GPTs / Claude Projects / Copilot Prompts using system instructions and reference materials.
• Test your AI agent, rank and group presentation

Module 6: Integrating AI automation opportunity map

• Identify what must change to make a process repeatable: source data, digitisation, structure, rules, integration and human controls.
• Setting AI usage policy ensuring data privacy, reducing AI hallucinations.
• Tips on developing AI reusable and customized prompt and evaluation of AI agents performance for department use as AI technology changes.