UPI Fraud Detection System using Python & Machine Learning
UPI Fraud Detection System
In today’s digital-first financial world, detecting and preventing UPI-based fraud is more critical than ever. That’s why we’ve developed UPI Fraud Detection System , a robust, web-based fraud detection system tailored for UPI transactions. This professional-grade project uses Python, Flask, and Machine Learning to intelligently flag fraudulent activity in real time.
📌 Project Overview
| Project Name | Language/s Used | Developer |
|---|---|---|
| UPI Fraud Detection System | Python, HTML, CSS, JavaScript | UPDATEGADH |
- Type: Web Application
- Python Version (Recommended): 3.x
- Framework: Flask
- Machine Learning: Yes (Joblib Model Included)
- Developer: UPDATEGADH
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✅ Available Features
- 🔐 UPI Transaction Data Upload & Analysis
- 🧠 Machine Learning–Based Fraud Detection Engine
- 📊 Real-Time Fraud Prediction
- 🌐 Web Interface built with Flask & HTML/CSS
- 💼 Professional Code Structure for easy upgrades
- 🧾 Model File (
fraud_model.joblib) already trained and included - 🎯 Easy to customize and extend
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Github project for detecting UPI fraud
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Project for a Python-based UPI fraud detection system
The Python version of the UPI fraud detection system project is available on Github. UPI fraud detection is the process of identifying and stopping fraudulent transactions made through the Unified Payments Interface (UPI) by employing a variety of strategies, chiefly machine learning. These techniques examine user behaviour, transaction data, and other trends to identify questionable activity and shield users from monetary loss.
The goal of this research is to develop a thorough fraud detection system that can identify and anticipate fraudulent transactions in UPI systems. Our main goals are to spot questionable trends and forecast fraud using machine learning algorithms. Furthermore, an interactive dashboard is constructed to show the results and enable users to dynamically examine the data.
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