🛡️ FRAUD_DETECTION_IN_EAST_AFRICA — Real-time fraud detection and risk scoring engine for East Africa's short-rental market. Built for Pumzika Hackathon 2026 (Track #04).
# 🛡️ Fraud Detection & Trust Scoring System
## East African Rental Market Intelligence Platform
## ⚡ Executive Summary
A real-time fraud detection and trust scoring system for East African rental listings that evaluates risk using multi-factor analysis to prevent rental scams before payment occurs.
## đź§ľ Submission Info
- Participant: Frank Karani
- Country: Tanzania
- Challenge: #04 Fraud Detection & Trust Scoring
- Institution: Institute of Accountancy Arusha (IAA)
- Year: First Year (2025/2026)
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## 👨‍💻 Author
**Frank Karani**
Cyber Security Student | Institute of Accountancy Arusha (IAA)
đź”— LinkedIn: Frank Karani
**GitHub:**
github.com
🚀 Live Demo: Fraud Detection & Trust Scoring System
## 📌 Overview
This system detects fraudulent rental listings in the East African market (Tanzania, Kenya, Uganda) using a multi-factor risk analysis engine.
It is designed to help users identify suspicious listings before making financial commitments, improving trust and safety in digital property marketplaces.
🎯 Live Demo & System Validation
The system has been tested with real-world scenarios from the East African rental market. Below is visual proof of the Fraud Detection Engine in action, demonstrating both approved listings and blocked fraudulent attempts.
### 1. User Input Interface
The system provides a clean, intuitive form for landlords to submit property details for real-time risk analysis. All critical data points including price, location, images, and user history are captured for the ML model.
### 2. Legitimate Listing Approved - Trust Score: 100%
This demonstrates the system's ability to accurately identify and approve legitimate properties. The ML model found no suspicious patterns, resulting in a Fraud Score: 0% and Risk Level: LOW. The listing is automatically approved and published.
### 3. Fraudulent Listing Blocked - Price Anomaly Detected
This shows the fraud detection engine successfully identi …