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
├── requirements.txt                         # Pinned Python dependencies
├── .env.example                             # Environment variable template
├── 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)

1. Clone the repository:

   git clone https://github.com/anujdevsingh/fingenius-ai-financial-advisor.git
   cd fingenius-ai-financial-advisor

2. Install dependencies:

   pip install -r requirements.txt

   Or use the installer script:

   python install_dependencies.py

3. Set up your Google API Key:

   • Get your API key from Google AI Studio
   • Copy .env.example to .env and add your key:

     cp .env.example .env
     # Edit .env and add your GOOGLE_API_KEY

4. Launch Jupyter Notebook:

   jupyter notebook

5. Start with the main notebook:

   • Open fingenius-notebook-gemini-agent.ipynb for the complete implementation
   • This notebook contains all 5 Gen AI capabilities and is the most comprehensive

The fingenius-notebook-gemini-agent.ipynb notebook (334KB) includes:

This is your complete Gen AI showcase - all 5 capabilities in one comprehensive notebook!

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 0.6.11
• Vector Database: ChromaDB 1.5.7
• 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!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. 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

"Bridging the gap between advanced AI technology and practical financial solutions for everyone."