GPT in Finance

🤖 GPT in Finance – Full Course Track

Welcome to the GPT track of QuantML — where you’ll learn how to use large language models (LLMs) like ChatGPT to automate everything from dashboards to decks, and finally give your brain a break from writing “variance is due to…” for the 37th time.

This is not just prompting. This is full-scale LLM + Python + Finance automation.

You’ll build tools that:

• Analyze and explain financial results

• Generate reports and commentary

• Build “chat with your P&L” apps

• Translate numbers into business insight, automatically

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🧠 What You’ll Learn

You’ll go from:

“Can GPT write Excel formulas?”
to
“I built a GPT-powered reporting engine that writes my monthly business review deck.”

This track is hands-on and business-ready. You’ll learn how to:

• Use OpenAI’s API with Python

• Automate commentary and insight generation

• Plug GPT into dashboards, reports, and Streamlit apps

• Connect LLMs to Excel, PowerPoint, and financial models

• Build GPT-powered assistants for variance analysis, forecasts, and more

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🧱 How This Track Works

This is a modular, code-based course with real business tools.

Each module includes:

• ✅ Live Python notebooks

• 💡 Prompt design templates

• 💬 Commentary examples and GPT outputs

• 📊 Real finance data (P&L, budget, dashboards)

• 🎥 Optional walkthroughs

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🔥 Complete GPT for Finance Modules

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✅ Module 0 – Getting Started with GPT + Python

Goal: Connect GPT to your code and make it do something useful
Outcome: You’ll get your first GPT-powered response, in Python, using your own data

Lessons:

• OpenAI API access: keys, setup, and security

• Using openai Python package

• Creating basic prompts and getting responses

• Intro to prompt engineering: finance edition

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✅ Module 1 – GPT for Variance Analysis Commentary

Goal: Stop writing boring variance notes manually
Outcome: GPT writes your commentary while you drink coffee and pretend you’re busy

Lessons:

• Structuring variance data (actual vs budget, YoY, MoM)

• Feeding numbers into GPT for explanation

• Writing effective prompts to match your company’s tone

• Batch commentary generation for large reports

• Real-world: GPT-generated summary for P&L / Cost Center reports

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✅ Module 2 – GPT for Forecast Insight & Scenario Narratives

Goal: Add smart “why” to your numeric “what”
Outcome: GPT adds insight, trends, and caveats to your forecasts

Lessons:

• Forecast vs actual explanation

• GPT prompts for sensitivity/scenario explanations

• Prompt chaining for detailed business insight generation

• Use case: “What changed from last quarter?” + GPT writes the answer

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✅ Module 3 – GPT + Excel / PowerPoint Automation

Goal: Build the holy grail: automated decks and GPT-driven slides
Outcome: Never write another finance deck again (almost)

Lessons:

• Using python-pptx to build slides

• Feeding GPT output into bullet points and notes

• Automatically generating commentary per slide

• Exporting GPT-powered decks with charts + insights

• Bonus: Email-ready commentary and summaries

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✅ Module 4 – GPT-Powered Financial Assistant

Goal: Build your own AI analyst
Outcome: You’ll create a chat app that understands and explains your finance data

Lessons:

• Building a chat interface with Streamlit

• Feeding GPT structured financial data (CSV, Excel, SQL)

• Asking questions like: “What happened to gross margin last quarter?”

• Chat history and follow-up prompt design

• Bonus: RAG (retrieval-augmented generation) with internal docs

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✅ Module 5 – Multi-Persona GPT for Business Reporting

Goal: Tailor GPT output for different business stakeholders
Outcome: One report, many voices — from analyst detail to CEO summary

Lessons:

• Prompting GPT to write for different personas (CFO vs Analyst)

• Tone control: formal, executive, technical

• Use case: One dataset → multiple auto-generated email updates

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✅ Module 6 – GPT Tools for EPM / ERP / BI Reporting

Goal: Use GPT with outputs from Oracle, SAP, Tableau, and others
Outcome: GPT reads the report and tells you what it means

Lessons:

• Parsing BI/ERP output files (Excel, CSV, exports)

• GPT prompts for multi-line structured commentary

• GPT to explain dashboards, trends, and anomalies

• Building a plug-and-play GPT report generator

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✅ Module 7 – Final Capstone Projects

Goal: Build a GPT-powered tool people actually want to use
Outcome: You’ll graduate this track with a working, real-world finance AI app

Projects:

• GPT-Powered Business Review Deck Generator

• Chat With Your P&L – Streamlit app for live data Q&A

• Budget Variance Email Generator – auto sends monthly summaries

• Forecast Scenario Narrator – explains best/worst/likely cases

• CFO Report Assistant – executive-ready summaries on demand

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🎯 Who This Is For

This track is designed for:

• FP&A teams tired of repetitive commentary writing

• Business analysts who want smarter automation

• BI/reporting pros looking to layer in natural language

• Finance consultants building next-gen client tools

• Anyone who’s ever typed “due to increased costs” more than 10 times

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🧠 Requirements

You’ll need:

• A basic understanding of Python and pandas

• OpenAI API access (or equivalent LLM access)

• Finance data: P&L, forecast, dashboard exports

• A desire to never write manual commentary again

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👇 Get Started

This track is where spreadsheets meet storytelling — and you stop sounding like a robot because an actual robot is doing the writing.

👉 Subscribe to QuantML to get all modules + code + walkthroughs

Or don’t. Stay manually typing “Revenue increased due to…” until retirement. Your choice.