Mini-Project 3: Build an Advanced AI Finance Tool (n8n or your platform)#
From building tools to building SMART tools
The Assignment#
Build a more advanced AI finance tool using n8n or another platform of your choice. It should analyze, explain, and/or advise on financial decisions—using AI as the brain.
Timeline: One week from Session 10
Submission: No public link required. Present your tool via a video demo showing your workflow (nodes, prompts, logic) and the tool in action. Keep API keys private.
Reference tutorial: Build an Financial AI tool with n8n (YouTube)
Note: You will need to sign up for paid AI API keys (e.g., OpenAI, Claude, Gemini)
Don’t know how? Research how to get an AI API key together with your AI copilot!
The Progression#
Mini-Project 1: You can build things ✓ Mini-Project 2: You can build useful finance tools ✓ Mini-Project 3: You can build INTELLIGENT finance tools Session 12: “I can build the future of finance”
What Makes This Different#
Your previous tools did calculations. This tool should:
Understand context
Explain complex concepts
Provide personalized insights
Answer “why” not just “what”
DRIVER Loops for AI Integration#
Loop 1: Basic AI Integration#
D - Discover: What financial decision needs explanation, not just calculation?
Investment analysis beyond numbers
Risk assessment with context
Strategic recommendations
Jargon translation
R - Reason: How can AI make this smarter?
Natural language input (“I have $10k to invest…”)
Contextual analysis (“Based on your age and goals…”)
Plain English output (“Here’s what this means for you…”)
Dynamic recommendations
I - Implement: Connect AI using n8n or your chosen platform
Use OpenAI, Claude, Gemini, or a local model (e.g., Ollama)
Go beyond a single prompt. Include at least two advanced capabilities, for example:
External data via API calls (rates, prices, news) and incorporate into analysis
Multi-step reasoning or tool use (decompose tasks; call functions/HTTP nodes)
Structured outputs (JSON) with validation and rendering (tables/charts)
Memory/profile state across steps (risk tolerance, goals)
Scenario generation and comparison with rationale
Guardrails/sanity checks to prevent bad advice
Cost control (token budgeting, caching, truncation)
V - Verify: Is the AI actually helpful?
Test with real scenarios
Check for hallucinations
Ensure finance accuracy
E - Evolve: What would make the AI more useful?
R - Reflect: What’s different about building with AI as a component?
Loop 2: Make It Trustworthy#
Critical improvements:
Add sources/explanations for AI advice
Build in sanity checks
Handle edge cases gracefully
Make limitations clear
Optional Loop 3+: Make It Powerful#
Advanced features:
Multi-step analysis
Scenario comparison
Learning from user feedback
Integration with real data + AI insights
Inspiration Examples#
AI + Simple Analysis:
Earnings Call Translator - “What did the CEO really say?”
Stock News Sentiment Analyzer - “Should I worry about this headline?”
Financial Statement Explainer - “What’s wrong with this company?”
Investment Thesis Generator - “Why buy/sell in plain English”
AI + Personal Finance:
Financial Health Advisor - Analyzes your situation conversationally
Goal-Based Investment Guide - “I want to buy a house in 5 years…”
Spending Pattern Therapist - “Why do I keep doing this?”
Negotiation Assistant - “How to ask for a raise based on…”
AI + Learning:
Finance Concept Tutor - Explains topics at your level
Case Study Analyzer - “What went wrong with SVB?”
Strategy Simulator - “What if I had done X instead?”
Jargon-Free Market Updates - News for normal humans
Creative Combinations:
Warren Buffett Decision Bot - “What would Buffett do?”
Risk Tolerance Interviewer - Discovers your real risk profile
Financial Scenario Generator - “What could go wrong?”
Deal Structure Optimizer - “How to make this work for everyone”
Technical Leveling Up#
New Skills to Master:
API integration (OpenAI, Claude, etc.)
Prompt engineering for finance
Error handling for AI responses
Token/cost management
Response streaming
Function/tool calling and structured outputs
Retrieval-augmented generation (RAG) and web/data integrations
Key Challenges:
Making AI financially accurate
Handling ambiguous user input
Managing API costs
Preventing harmful advice
Building user trust
Ask AI for Help With:
“How to integrate OpenAI API with my web app”
“Best practices for financial prompt engineering”
“How to validate AI financial advice”
“Streaming AI responses for better UX”
Requirements#
Platform & Core AI Functionality
Built with n8n or another platform of your choice (e.g., custom web app, Python app, Make/Zapier, LangChain/Flowise)
Uses AI to analyze/explain/advise; handles natural language input
Provides contextual, user-relevant insights (the “why,” not just the number)
Advanced Capabilities (choose 2 or more)
External data integration via APIs (e.g., rates, prices, news) with incorporation into analysis
Multi-step logic or tool use (function calling, HTTP requests, spreadsheet/code nodes)
Structured outputs with validation and clear rendering (tables/charts)
Memory/state (e.g., goals, risk tolerance) that influences recommendations
Scenario comparison with trade-offs and a recommendation
Guardrails/sanity checks + transparent disclaimers
Cost controls (prompt optimization, caching, token limits)
Safety & Accuracy
Clear disclaimers about AI limitations and appropriate use
At least one accuracy/robustness mechanism (sanity checks, bounds, unit tests, or cross-check prompts)
Appropriate for the financial domain and user audience
Submission: Video Demo
2–3 minutes screen recording showing workflow (nodes/prompts/logic) and end-to-end demo
No public link required; keep API keys private
Narrate who it’s for and why it’s useful
Iteration
Show that you improved the tool from v1 to v2 (prompt refinement, new node, validation, or UX polish)
Reflection in your video
How does AI change the user experience?
What was hard about financial prompt engineering?
What ethical considerations did you face?
Would you trust your own tool with real money?
Assessment Criteria#
Pass Requirements:
Successfully integrates AI for financial analysis ✓
Provides value beyond simple calculation ✓
Shows iterative improvement ✓
Addresses accuracy/trust concerns ✓
Excellence Indicators:
Genuinely insightful AI responses
Elegant handling of edge cases
Creative application of AI capabilities
Other students want to use it
You’d use it for real decisions
Common Pitfalls#
Over-Trusting AI: “The AI said buy, so buy!” → Build in verification and disclaimers
Under-Using AI: “It just formats the output nicely” → Make AI do actual analysis
Prompt Problems: “Explain finance” (too vague) → Craft specific, contextual prompts
No Value Add: “It’s ChatGPT in a wrapper” → Add domain-specific intelligence
Cost Explosion: “Each query costs $5” → Optimize prompts and cache responses
Ethical Considerations#
Your tool will give financial guidance. Consider:
Clear disclaimers (not financial advice)
Transparency about AI involvement
Protection against misuse
Accessibility for different backgrounds
Bias in financial recommendations
The Mindset Evolution#
Mini-Project 1: “I can build” Mini-Project 2: “I can build useful finance tools” Mini-Project 3: “I can build INTELLIGENT finance tools” Session 12: “I can build the future of finance”
The New Reality: Financial analysis is transforming. Traditional tools and AI are converging to create new opportunities for those who can leverage both effectively.
Your Edge: You can build tools that USE AI to solve problems AI alone cannot. That’s not just job security - that’s leadership potential.
Starting Points#
What financial concept confuses people? → Build an AI explainer
What decision do people struggle with? → Build an AI advisor
What analysis takes forever? → Build an AI analyzer
What pattern is hard to spot? → Build an AI detector
Pro Tips#
Paid API tiers work better
Test prompts in ChatGPT/Claude first
Build the UI, then add AI (not vice versa)
Log everything for debugging
Have non-finance friends test it
Remember: The best AI finance tool is one that makes someone say “Oh, NOW I get it!”
Make finance smarter, not just faster.
Ship something that thinks.