https://github.com/projects-developer/upi-fraud-detection-using-machine-learning

The Unified Payments Interface (UPI) has revolutionized the way people make payments in India. However, with the increasing adoption of UPI, there has been a rise in fraudulent transactions. To combat this, a UPI fraud detection system is essential.
https://github.com/projects-developer/upi-fraud-detection-using-machine-learning

code computer-science-project documents finalyearproject payment-security real-time-detection upi-fraud-detection

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The Unified Payments Interface (UPI) has revolutionized the way people make payments in India. However, with the increasing adoption of UPI, there has been a rise in fraudulent transactions. To combat this, a UPI fraud detection system is essential.

• Host: GitHub
• URL: https://github.com/projects-developer/upi-fraud-detection-using-machine-learning
• Owner: Projects-Developer
• Created: 2024-12-18T10:57:21.000Z (21 days ago)
• Default Branch: main
• Last Pushed: 2024-12-18T11:35:51.000Z (21 days ago)
• Last Synced: 2024-12-18T12:25:27.580Z (21 days ago)
• Topics: code, computer-science-project, documents, finalyearproject, payment-security, real-time-detection, upi-fraud-detection
• Homepage: https://www.finalproject.in/
• Size: 9.77 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# UPI-FRAUD-DETECTION-USING-MACHINE-LEARNING

UPI Fraud Detection Using Machine Learning | Source Code & Documents

[[![image](https://github.com/user-attachments/assets/47fce75b-12ab-4032-85bf-4aade3417dc1)](https://github.com/Projects-Developer/UPI-FRAUD-DETECTION-USING-MACHINE-LEARNING/issues/1#issue-2747559323)

## Youtube video: https://youtu.be/uzVdxlG1gIY?si=zbc-n9LEjifhrsvn

### Abstract :

The increasing adoption of Unified Payments Interface (UPI) has led to a rise in fraudulent transactions. To combat this, we propose a real-time UPI fraud detection system using machine learning and data analytics. Our system collects transaction data from various sources, including banks and payment gateways. We then apply machine learning algorithms, such as random forest and support vector machine, to identify patterns and anomalies in the data. The system also uses data analytics techniques, including statistical analysis and data visualization, to provide insights into fraudulent transactions. Our system is designed to detect fraudulent transactions in real-time, allowing for prompt action to be taken to prevent financial losses. We evaluated our system using a dataset of real-world UPI transactions and achieved an accuracy of 95% in detecting fraudulent transactions.

keywords: UPI Fraud Detection, Machine Learning, Payement Security, social media

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