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This study seeks to enhance the accuracy of credit card fraud detection by utilizing advanced machine learning techniques, with a specific focus on the XG Boost algorithm. Various ML approaches, including Decision Trees, Logistic Regression, Naive Bayes, Random Forest, and XG Boost, are evaluated for their efficiency in detecting fraudulent transactions using patterns derived from historical data. Recent advancements highlight the integration of diverse authentication methods and randomized training datasets to mitigate vulnerabilities in fraud detection systems.

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Real-Time Fraud Detection in Credit Card Transactions: Leveraging Face Detection and Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Supriya,
  • Ananya G. Bhat,
  • B. A. Chandana,
  • P. Niharika

摘要

This study seeks to enhance the accuracy of credit card fraud detection by utilizing advanced machine learning techniques, with a specific focus on the XG Boost algorithm. Various ML approaches, including Decision Trees, Logistic Regression, Naive Bayes, Random Forest, and XG Boost, are evaluated for their efficiency in detecting fraudulent transactions using patterns derived from historical data. Recent advancements highlight the integration of diverse authentication methods and randomized training datasets to mitigate vulnerabilities in fraud detection systems.