AI-Augmented Data Engineering Strategies for Real-Time Fraud Detection in Digital Ecosystems
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Cyber fraud is experienced in digital ecosystems, and it is very dangerous to organizational and individuals’ experiments. The general approaches to fraud detection work well in stable environments but are incapable of the real-time results required by modern digital spaces. This article aims to demonstrate how artificial intelligence (AI) can complement approaches for data engineering to meet these challenges. When AI models are supported with strong data feeds, organising can, therefore, identify fraudulent activities in real-time, thereby reducing losses and encouraging a safe digital economy. To offer some background to the reader about the content of the article, the abstract divides its content into the three major ideas supported by the author: AI scalability, speed, and accuracy in fraud detection. It also gives the reader an understanding of the alternative and supplemental approaches, examples of implementations, and trends within the paper. Finally, the article seeks to establish that AI enhanced data engineering has the capability of become instrumental in protecting digital economy against emerging forms of frauds.
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