AI Techniques for Web-Based Fraud Detection: A Review

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@article{1712448,
      author = {R. Prasanth Reddy, Nagavelli Yogender Nath, Gattu Ramya, Syed Abdul Haq},
      title = {AI Techniques for Web-Based Fraud Detection: A Review},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {8},
      pages = {556-564},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712448.pdf},
      abstract = {Web-based frauds pose significant challenges due to their evolving nature, sophisticated methods, and the vast amount of data involved. However, advancements in AI and machine learning offer promising possibilities for effective detection and prevention. By continuously updating detection models and employing robust measures, it is possible to mitigate the risks associated with online frauds and protect individuals and organizations from its adverse effects. This paper review establishes a foundation for future advancements in AI-driven fraud detection, contributing to the ongoing efforts to safeguard individuals and organizations against fraudulent activities. This paper presents machine learning approaches for web-based fraud detection, addressing the limitations of traditional methods and offering robust solutions for identifying and preventing web-based frauds. The proposed frameworks demonstrate high accuracy and precision in detecting various types of web-based fraud, highlighting their potential for practical applications.},
      keywords = {Deep Learning, Web-based Fraud, Fraud Detection, Intelligent Approaches Web-based},
      month = {February},
      doi = {https://doi.org/10.64388/IREV7I8-1712448}
  }