MarketGuard Lite - Fraud Detection System
MarketGuard Lite is a comprehensive fraud detection system designed to identify and prevent securities market fraud. It analyzes text content, URLs, and financial advisor credentials to detect potential fraudulent activities in investment communications.
✨ Features
Text Analysis → Scans social media posts, emails, and announcements for fraud indicators
URL Analysis → Examines websites and links for known fraudulent patterns
Advisor Verification → Checks financial advisor credentials against regulatory databases
Real-time Detection → Provides instant risk assessment with detailed indicators
Interactive Dashboard → Visualizes detection statistics and historical data
Regulatory Reporting → Facilitates reporting of suspicious activities to authorities
🛠 Technology Stack Frontend
HTML5, CSS3, JavaScript (ES6+)
Bootstrap 5 (responsive UI)
Chart.js (data visualization)
Font Awesome (icons)
Backend
Python 3.8+
Flask web framework
Rule-based detection engine
RESTful API architecture
🚀 Installation Prerequisites
Python 3.8+
pip (Python package manager)
A modern web browser (Chrome, Firefox, Safari, Edge)
Backend Setup
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python app.py
Backend runs at 👉 http://localhost:8000
Frontend Setup cd frontend
python -m http.server 8080
Frontend runs at 👉 http://localhost:8080
📖 Usage 🔹 Text Analysis
Select Text Analysis tab
Paste suspicious content
Select platform + content type
Click Analyze for Fraud Indicators
🔹 URL Analysis
Select URL Analysis tab
Enter URL
Click Analyze URL
🔹 Advisor Verification
Select Advisor Check tab
Enter advisor name / registration number
Click Verify Advisor
📡 API Endpoints Text Analysis POST /api/analyze/text
Params:
text (string)
source_platform (string)
content_type (string)
Response: Risk score, indicators, recommendations
URL Analysis POST /api/analyze/url
Params: url (string) Response: Domain risk assessment
Advisor Verification POST /api/check-advisor
Params: name (string, optional), registration_number (string, optional) Response: Registration status, legitimacy assessment
Dashboard GET /api/dashboard
Response: System statistics
🔎 Detection Methodology
Fraud Indicators: urgency language, unrealistic returns, fake endorsements, grammar errors, unregulated entities
Legitimacy Indicators: proper disclosures, registration mentions, legitimate contact info
Risk Scoring: weighted indicators + platform/content modifiers + historical patterns
📂 Project Structure marketguard_lite/ ├── README.md ├── backend/ │ ├── app.py │ ├── requirements.txt │ ├── rule_based_detector.py │ └── config.py ├── data/ │ ├── regulatory_db.csv │ ├── social_posts.jsonl │ ├── ticks.csv │ └── whois_mock.csv ├── frontend/ │ └── index.html ├── scripts/ │ └── run_uvicorn.sh └── train/ ├── train_dataset.csv └── train_model.py