🎓 Gen AI Intensive Project
An intelligent financial advisory system showcasing 5 key Gen AI capabilities: Structured Output/JSON Mode, RAG (Retrieval Augmented Generation), Embeddings, Function Calling, and LangGraph Agents.
FinGenius is an AI-powered personal finance advisor that revolutionizes how users interact with their financial data. , this system demonstrates cutting-edge generative AI capabilities applied to real-world financial challenges.
- 🔍 Smart Transaction Analysis: Automatically categorizes and analyzes financial transactions using embeddings
- 💬 Conversational AI Interface: Natural language interaction for financial queries using LangGraph agents
- 📈 Intelligent Spending Insights: Identifies patterns and trends in financial behavior
- 🎯 Personalized Recommendations: Tailored advice based on individual financial profiles using RAG
- 📊 Structured Financial Reports: JSON-mode output for consistent data formatting
- 🧮 Advanced Financial Calculations: Function calling for loans, investments, and budget planning
- 📚 Knowledge-Based Advice: Evidence-based recommendations from financial literature
Many individuals struggle with effective financial management, needing help with:
- Understanding spending patterns and financial behavior
- Getting personalized, accessible financial advice
- Learning financial concepts and best practices
- Creating and maintaining realistic budgets
- Planning for short and long-term financial goals
FinGenius addresses these challenges by providing an intelligent, conversational assistant that analyzes financial data and offers personalized guidance.
This project showcases 5 comprehensive Gen AI capabilities:
- Consistent transaction categorization and analysis
- Standardized budget breakdown visualization
- Structured financial health summary reports
- Automated expense pattern detection
- Financial knowledge base integration for evidence-based advice
- Retrieval of relevant financial best practices and strategies
- Access to financial regulations and terminology explanations
- Context-aware recommendations based on financial literature
- Semantic understanding of transaction descriptions and patterns
- Intelligent clustering of similar expenses and spending categories
- Advanced spending pattern recognition over time periods
- Similarity search for financial concepts and advice
- Automated financial calculations (savings projections, interest calculations)
- Real-time data processing and financial metric computation
- Integration capabilities for external financial data sources
- Advanced budget allocation and optimization algorithms
- Sophisticated conversational financial advisor workflow
- Multi-step financial planning and goal-setting processes
- Intelligent decision trees for personalized recommendations
- Stateful conversation management across financial topics
fingenius-ai-financial-advisor/
├── 📓 fingenius-notebook-gemini-agent.ipynb # 🌟 Main implementation (RECOMMENDED)
├── 🐍 install_dependencies.py # Dependency installation script
├── 📄 project_structure.md # Detailed technical documentation
├── 📄 LICENSE # MIT License
├── 📄 .gitignore # Python/Jupyter gitignore
├── 📄 .gitattributes # Git attributes configuration
└── 📄 README.md # This file
- Python 3.8+ 🐍
- Jupyter Notebook or JupyterLab 📓
- Google API Key (for Gemini AI) 🔑
-
Clone the repository:
git clone https://github.com/yourusername/fingenius-ai-financial-advisor.git cd fingenius-ai-financial-advisor -
Install dependencies:
python install_dependencies.py
Or manually install:
pip install google-genai==1.7.0 chromadb==0.6.3 langgraph==0.3.21 langchain-google-genai==2.1.2
-
Set up your Google API Key:
- Get your API key from Google AI Studio
- Set up environment variable or use secure methods as shown in notebooks
-
Launch Jupyter Notebook:
jupyter notebook
-
🌟 Start with the main notebook:
- Open
fingenius-notebook-gemini-agent.ipynbfor the complete implementation - This notebook contains all 5 Gen AI capabilities and is the most comprehensive
- Open
The fingenius-notebook-gemini-agent.ipynb notebook (334KB) includes:
| Feature | Implementation | Status |
|---|---|---|
| 📊 Structured Output/JSON Mode | Transaction categorization, budget reports | ✅ Complete |
| 🔍 RAG (Retrieval Augmented Generation) | Financial knowledge base integration | ✅ Complete |
| 🧠 Embeddings | Semantic transaction analysis | ✅ Complete |
| 🛠️ Function Calling | Financial calculations & data processing | ✅ Complete |
| 🤖 Agents with LangGraph | Conversational financial advisor workflow | ✅ Complete |
The main notebook demonstrates comprehensive financial scenarios:
# Automatic categorization and pattern recognition
transactions = load_financial_data()
insights = analyze_spending_patterns(transactions)# Natural language financial queries
response = financial_advisor.query("How should I budget my $5000 monthly income?")# Automated loan and investment calculations
mortgage_payment = calculate_loan_payment(300000, 0.045, 30)
investment_growth = calculate_investment_return(10000, 0.08, 20)# Pattern detection and recommendations
patterns = find_spending_patterns(transaction_embeddings)
recommendations = generate_budget_advice(patterns, financial_goals)- ✅ Secure API Key Handling: Uses environment variables and secure client methods
- ✅ No Hardcoded Secrets: All sensitive information properly externalized
- ✅ Synthetic Data: All examples use generated/sample data for demonstrations
- ✅ Privacy Best Practices: Follows financial data handling guidelines
- Personal banking applications with AI-powered insights
- Credit union member financial wellness programs
- Robo-advisor platforms with conversational interfaces
- Mobile budgeting apps with intelligent categorization
- Investment platforms with personalized advice
- Expense tracking with automated pattern recognition
- Educational platforms teaching financial literacy
- Personal finance coaching tools
- Financial planning software for advisors
- Accessible financial advice for underserved populations
- Multilingual financial guidance systems
- Microfinance and community banking solutions
- AI Framework: Google Gemini 2.0 Flash-Lite
- Agent Framework: LangGraph 0.3.21
- Vector Database: ChromaDB 0.6.3
- Data Processing: Pandas, NumPy
- Visualization: Matplotlib, Seaborn, Plotly
- Environment: Jupyter Notebooks
- Automated retry mechanisms for API calls
- Exponential backoff for rate limit handling
- Efficient embedding-based similarity search
- Optimized function calling workflows
- Real-time financial data integration (APIs for banks/cards)
- Advanced ML models for fraud detection
- Multi-language support for global accessibility
- Mobile app development with React Native/Flutter
- Goal-based financial planning modules
- Investment portfolio optimization
- Tax optimization strategies
- Retirement planning calculators
- End-to-end encryption for sensitive data
- Compliance with financial regulations (PCI DSS, GDPR)
- Multi-tenant architecture for scalability
- Advanced analytics dashboard for financial advisors
This project was developed as an educational capstone, but contributions are welcome!
- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- Additional financial calculation functions
- Enhanced visualization capabilities
- New Gen AI capability demonstrations
- Performance optimizations
- Documentation improvements
This project is licensed under the MIT License - see the LICENSE file for details.
- 🤖 Google: For providing the powerful Gemini AI platform
- 🔗 LangChain Community: For the incredible agent framework and tools
- 🌟 Open Source Community: For the various libraries and tools used
- 🎯 IITM BS Data Science Program: For the educational foundation
- 🐙 GitHub: Open an issue for questions or suggestions
⭐ If FinGenius helps with your financial AI projects, please star this repository! ⭐
Built with ❤️ for the Gen AI community
"Bridging the gap between advanced AI technology and practical financial solutions for everyone."