https://github.com/hari7261/quantum-banking-system
Quantum Banking System combines cutting-edge AI technology with modern banking features to provide an unparalleled financial management experience.
https://github.com/hari7261/quantum-banking-system
ai bank-management-system banking enhanced loan matplotlib multiple-features nlp ntk os otp-verification pandas pillow python sklearn transaction transformer
Last synced: 7 months ago
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Quantum Banking System combines cutting-edge AI technology with modern banking features to provide an unparalleled financial management experience.
- Host: GitHub
- URL: https://github.com/hari7261/quantum-banking-system
- Owner: hari7261
- License: mit
- Created: 2025-01-20T12:58:24.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-20T12:59:55.000Z (9 months ago)
- Last Synced: 2025-03-20T15:14:04.151Z (7 months ago)
- Topics: ai, bank-management-system, banking, enhanced, loan, matplotlib, multiple-features, nlp, ntk, os, otp-verification, pandas, pillow, python, sklearn, transaction, transformer
- Language: Python
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🏦 Quantum Banking System - Next-Gen AI-Powered Banking Platform
> 🚀 Welcome to the future of banking! Quantum Banking System combines cutting-edge AI technology with modern banking features to provide an unparalleled financial management experience.
## 📋 Table of Contents
- [🌟 Features](#features)
- [🔧 Installation](#installation)
- [🛠️ Technical Requirements](#technical-requirements)
- [🚀 Getting Started](#getting-started)
- [🤖 AI Features](#ai-features)
- [💼 Core Banking Features](#core-banking-features)
- [🔒 Security Features](#security-features)
- [📊 Analytics & Reporting](#analytics--reporting)
- [📱 User Interface](#user-interface)
- [🔌 API Integration](#api-integration)
- [📚 Library Dependencies](#library-dependencies)
- [⚙️ Configuration](#configuration)
- [🔍 Troubleshooting](#troubleshooting)
- [🤝 Contributing](#contributing)
- [📄 License](#license)
### 🤖 AI-Powered Features
- **Sentiment Analysis** - Real-time transaction sentiment analysis
- **Fraud Detection** - ML-based anomaly detection
- **Smart Categorization** - NLP-powered transaction categorization
- **Automated Financial Advice** - AI-generated financial insights
- **Predictive Analytics** - ML-based spending predictions
### 💰 Banking Features
- **Account Management** - Full-featured account control
- **Investment Portfolio** - Real-time stock tracking
- **Budget Goals** - Smart goal setting and tracking
- **Bill Payments** - Automated bill management
- **Multi-Currency Support** - Real-time forex conversion
```bash
# Clone the repository
git clone https://github.com/yourusername/quantum-banking.git
# Navigate to project directory
cd quantum-banking
# Create virtual environment
python -m venv venv
# Activate virtual environment
# For Windows:
venv\Scripts\activate
# For Unix/MacOS:
source venv/bin/activate
# Install required packages
pip install -r requirements.txt
# Initialize database
python initialize_db.py
```
### System Requirements
- Python 3.8 or higher
- SQLite3
- 4GB RAM minimum
- 2GB free disk space
### Required Libraries and Dependencies
#### Core Libraries
```txt
customtkinter==5.2.0
numpy==1.24.3
pandas==2.0.0
scikit-learn==1.2.2
nltk==3.8.1
transformers==4.28.1
yfinance==0.2.18
Pillow==9.5.0
matplotlib==3.7.1
```
1. **Launch the Application**
```python
python main.py
```
2. **First-Time Setup**
- Create a new account using the registration interface
- Complete KYC verification
- Set up 2FA (recommended)
- Configure notification preferences
3. **Quick Start Guide**
```python
# Example code for basic operations
from banking_system import ModernBankingApp
# Initialize the application
app = ModernBankingApp()
# Create new account
app.register_account(
name="John Doe",
email="john@example.com",
initial_deposit=1000
)
# Perform transaction
app.transfer_funds(
from_account="123456",
to_account="789012",
amount=500
)
```
### Sentiment Analysis Engine
The system uses transformers for transaction sentiment analysis:
```python
from transformers import pipeline
sentiment_analyzer = pipeline("sentiment-analysis")
result = sentiment_analyzer("Transaction description")
```
### Fraud Detection System
Uses Isolation Forest algorithm for anomaly detection:
```python
from sklearn.ensemble import IsolatedForest
detector = IsolatedForest(contamination=0.1)
predictions = detector.fit_predict(transaction_data)
```
### NLP Transaction Categorization
```python
import nltk
from nltk.tokenize import word_tokenize
def categorize_transaction(description):
tokens = word_tokenize(description.lower())
# Category matching logic
return matched_category
```
### Database Schema
```sql
-- Accounts Table
CREATE TABLE accounts (
account_number INTEGER PRIMARY KEY,
name TEXT NOT NULL,
address TEXT,
phone TEXT,
email TEXT,
account_type TEXT,
balance REAL DEFAULT 0,
password TEXT
);
-- Transactions Table
CREATE TABLE transactions (
transaction_id INTEGER PRIMARY KEY AUTOINCREMENT,
account_number INTEGER,
transaction_type TEXT,
amount REAL,
timestamp TEXT,
remarks TEXT
);
```
### Investment Portfolio Management
```python
class InvestmentPortfolio:
def __init__(self):
self.stocks = {}
self.performance_metrics = {}
def add_stock(self, symbol, quantity, price):
# Stock addition logic
pass
def calculate_returns(self):
# Returns calculation logic
pass
```
### Two-Factor Authentication
- TOTP (Time-based One-Time Password) implementation
- QR code generation for authenticator apps
- Backup codes generation
### Encryption
- AES-256 encryption for sensitive data
- Secure password hashing using bcrypt
- End-to-end encryption for communications
### Transaction Analytics
```python
def generate_analytics(transactions):
# Generate spending patterns
patterns = analyze_spending(transactions)
# Create visualizations
create_visualizations(patterns)
# Generate insights
insights = generate_insights(patterns)
return patterns, insights
```
### Custom Matplotlib Charts
```python
def create_transaction_chart(data):
fig, ax = plt.subplots()
ax.plot(data['dates'], data['amounts'])
ax.set_title('Transaction Timeline')
return fig
```
The UI is built using customtkinter for a modern, responsive interface:
### Theme Configuration
```python
ctk.set_appearance_mode("dark")
ctk.set_default_color_theme("blue")
```
### Responsive Design
- Fluid layouts using grid system
- Dynamic widget sizing
- Touch-friendly interface
- Dark/Light mode support
### Stock Market Data
```python
import yfinance as yf
def get_stock_data(symbol):
stock = yf.Ticker(symbol)
return stock.info
```
### Forex Rates
```python
from forex_python.converter import CurrencyRates
def convert_currency(amount, from_currency, to_currency):
c = CurrencyRates()
return c.convert(from_currency, to_currency, amount)
```
### Environment Variables
Create a `.env` file:
```env
DB_PATH=bank.db
SECRET_KEY=your-secret-key
API_KEY=your-api-key
DEBUG_MODE=False
```
### Logging Configuration
```python
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
filename='bank.log'
)
```
Common issues and solutions:
1. **Database Connection Issues**
```python
# Check database connection
def test_db_connection():
try:
conn = sqlite3.connect('bank.db')
print("Database connection successful")
except sqlite3.Error as e:
print(f"Database error: {e}")
```
2. **API Connection Issues**
- Verify API keys
- Check network connection
- Validate request parameters
We welcome contributions! Please follow these steps:
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Submit a pull request
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
## 🌟 Star Us on GitHub
If you find this project helpful, please star it on GitHub! Your support helps us continue development.
---
Made with ❤️ by the Quantum Banking Team