Version 1
: Received: 4 November 2024 / Approved: 5 November 2024 / Online: 5 November 2024 (16:58:03 CET)
How to cite:
Babatope, A. Machine Learning for Advanced Fraud Detection and Content Moderation. Preprints2024, 2024110352. https://doi.org/10.20944/preprints202411.0352.v1
Babatope, A. Machine Learning for Advanced Fraud Detection and Content Moderation. Preprints 2024, 2024110352. https://doi.org/10.20944/preprints202411.0352.v1
Babatope, A. Machine Learning for Advanced Fraud Detection and Content Moderation. Preprints2024, 2024110352. https://doi.org/10.20944/preprints202411.0352.v1
APA Style
Babatope, A. (2024). Machine Learning for Advanced Fraud Detection and Content Moderation. Preprints. https://doi.org/10.20944/preprints202411.0352.v1
Chicago/Turabian Style
Babatope, A. 2024 "Machine Learning for Advanced Fraud Detection and Content Moderation" Preprints. https://doi.org/10.20944/preprints202411.0352.v1
Abstract
The rapid advancement of digital technologies has led to a corresponding increase in fraudulent activities and harmful content across online platforms, posing significant challenges to both cybersecurity and content integrity. Traditional methods of fraud detection and content moderation, often based on rule-based systems, have proven inadequate in addressing the dynamic and sophisticated nature of these threats. This paper explores the application of machine learning (ML) techniques to enhance the effectiveness of fraud detection and content moderation systems. By leveraging supervised, unsupervised, and deep learning models, ML provides a more adaptive and scalable approach to identifying fraudulent transactions, detecting anomalies, and moderating content on digital platforms. Key methodologies discussed include the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for content analysis and the application of clustering algorithms for anomaly detection in financial transactions. The paper also addresses the challenges of implementing ML-based systems, such as data quality, bias, and the need for real-time processing, and proposes solutions to mitigate these issues. Furthermore, the ethical implications of using ML for these purposes are considered, with a focus on fairness and transparency. The findings demonstrate that ML significantly improves the accuracy and efficiency of fraud detection and content moderation, making it a critical tool in the fight against digital threats. Continued research and innovation in this field are essential to keep pace with evolving threats.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
