Financial Assistant AI Model Overview The Financial Assistant AI Model is a robust, AI-driven solution designed to provide expert financial advice, detect fraudulent transactions, and predict future expenses. Built using machine learning models and natural language processing (NLP), this application leverages advanced algorithms to address various financial queries and challenges.
Features Expert Financial Advice:
Provides tailored recommendations on financial planning, tax implications, regulatory compliance, risk management, and investment strategies.
Supports personalized advice based on user-defined parameters like risk tolerance and investment horizon.
Interactive Web Interface:
Users can input their financial queries via a simple web interface.
Receives structured advice formatted for easy readability.
Architecture The project comprises three main components:
- Backend API (app.py) Built with Flask, the API serves as the backbone of the application.
Key Endpoint:
/get_expert_advice: Accepts user queries via POST requests and returns detailed financial advice.
Includes Cross-Origin Resource Sharing (CORS) support for seamless integration with frontend clients.
- Core Logic (pop.py) Implements the core functionality of the financial assistant:
FinancialExpertAgent: Provides expert advice using Groq's NLP models.
Integrates with Groq's API for advanced NLP capabilities.
- Frontend (assistantAI.html) A simple HTML interface for users to submit financial queries and view responses.
Displays advice in a structured format for better user experience.
Installation Prerequisites Python 3.8 or higher
Required Python libraries: Flask, Flask-CORS, NumPy, scikit-learn, requests
Groq API Key (replace placeholder in pop.py)
Steps Clone this repository:
bash git clone https://github.com/your-repo/financial-assistant-ai.git cd financial-assistant-ai Install dependencies:
bash pip install -r requirements.txt Replace the placeholder GROQ_API_KEY in pop.py with your actual Groq API key.
Run the Flask server:
bash python app.py Open assistantAI.html in a web browser to interact with the application.
Usage Example Query Workflow Enter your financial query in the web interface (e.g., "How should I allocate my investments for medium risk?").
Submit the query to receive detailed advice covering:
Best practices
Regulatory considerations (e.g., RBI/SEBI guidelines)
Tax implications
Risk management strategies
Asset allocation recommendations
Optionally, use backend endpoints to:
Detect potential fraud in transactions.
Predict future expenses based on historical spending data.
Example API Request (cURL)
bash
curl -X POST http://127.0.0.1:5000/get_expert_advice
-H "Content-Type: application/json"
-d '{"user_query": "What are the best investment options for medium-term goals?"}'
Response Format
json
{
"query": "What are the best investment options for medium-term goals?",
"advice": "Detailed financial advice tailored to your query..."
}
Technologies Used
Programming Language: Python
Frameworks: Flask (Backend), HTML (Frontend)
Machine Learning Models: Isolation Forest, Random Forest Regressor
NLP Integration: Groq's Llama-based models
Future Enhancements Add support for multilingual queries.
Extend fraud detection capabilities with additional features like geolocation data.
Enable dynamic risk profiling based on user behavior.
Contributing Contributions are welcome! Please fork the repository and submit a pull request with your changes.
This README provides an overview of the Financial Assistant AI Model, highlighting its features, architecture, and usage instructions