Course Details
A 2-day course, 6 hours per day (12 hours in total), delivered as a hands-on workshop with a capstone project. Beginner to Intermediate level, no AI background required. Important note: this course aims to build AI skills for data analysis, not to give investment advice, recommendations or solicitation; investment decisions are the learner's own responsibility. Learners take home an analysis prompt library, an analysis assistant (Custom GPT/Gem), statement and report summary templates, and a data-verification and PDPA checklist.
Day 1: AI for Reading Statements, News and Analysis
Section 1: AI in Investor and Analyst Work
- An overview of generative AI in financial analysis work and what changes in the process
- AI use cases: reading statements, summarizing reports, summarizing news, comparison and portfolio monitoring
- Key frame: AI is an analysis aid, not investment advice, and data must be verified every time
Section 2: Prompts and Analysis Frameworks
- A good prompt structure for analysis work: role, problem, data and output format
- Having AI work within the learner's analysis framework systematically and verifiably
- Building a prompt library for recurring work
Section 3: Reading and Summarizing Statements and Reports
- Using AI to help read and summarize financial statements and annual reports and pull key points
- Always verifying numbers and sources against the original documents before use
- Workshop: build a statement or report summary with AI and verify it
Section 4: News and Sentiment Summaries
- Using AI to summarize news and analyses and assess basic sentiment carefully
- Being careful of misinformation and bias and citing verifiable sources
- Workshop: build a news summary and watch points with AI
Section 5: Comparison and Summary Reporting
- Using AI to help compare securities or industries against defined criteria
- Preparing easy-to-read, cited summary reports while verifying numbers before use
- Workshop: build a comparison table and summary report with AI
Day 2: Portfolio Monitoring, Assistants, Administration and Automation
Section 6: Portfolio Monitoring and Data Organization
- Using AI to help organize and summarize the learner's own portfolio data (on masked data)
- Preparing an overview and basic risk points while verifying numbers before use
- Workshop: build a portfolio summary or watchlist with AI
Section 7: Chatbots and Analysis Assistants
- Building a Custom GPT or Gem as an assistant working to the learner's analysis framework and standards
- Embedding knowledge, examples and guidelines for consistent, verifiable results
- Workshop: build one analysis assistant for frequent tasks
Section 8: Documents and Administration
- Using AI to help with documents, analysis notes and knowledge management
- Summarizing meetings and preparing basic reports
- Managing data and documents systematically, being careful with sensitive data
Section 9: Analysis Automation with n8n
- Setting up automation with n8n, such as collecting news, summarizing data and alerting on watch points
- Connecting data across tools and channels with access control
- Workshop: design one simple automation flow for analysis work
Section 10: AI Limits, Verification and PDPA
- AI limits and risks: hallucinated numbers and out-of-date information
- Verifying numbers and sources every time and reinforcing that AI is not investment advice
- Personal and sensitive data under PDPA and setting an AI usage guideline
Section 11: Capstone Project and Real-world Adoption
- Task: set up an end-to-end AI toolkit for the learner's analysis work, from reading statements and news and comparison to an assistant and portfolio monitoring
- Present the work and get feedback from the instructor and peers
- A first-30-days adoption plan and setting up AI use in analysis work