Enhancing OTP Fraud Detection in Telangana: A Machine Learning Approach
Author : V Sumalatha
Abstract :
This paper presents a machine learning-based approach for OTP fraud detection in online transactions. By leveraging transactional data and user behavior patterns, we develop an effective model to identify fraudulent activities. Various machine learning algorithms are evaluated, with Random Forest demonstrating superior performance. The proposed model achieves over 99% accuracy in detecting OTP fraud. Key features contributing to fraud detection include unusual transaction amounts, transaction frequency, and suspicious user behavior. Our findings highlight the effectiveness of machine learning in enhancing security and combating fraudulent activities in online transactions. For financial institutions, implement machine learning-based fraud detection systems, offering training on integration. Online service providers should integrate the model into transaction processing systems and provide guidelines for monitoring suspicious activities. Inform government agencies about the findings to advocate for policies promoting advanced security measures. Share results in academic journals, conferences, and collaborate with researchers to enhance fraud detection.
Keywords :
OTP Fraud Detection, Machine Learning, Random Forest, Online Transaction Security, Telangana.
