AI-Powered Data Anomaly Detection: Enhancing Data Integrity, Addressing Complex Data Patterns and Anomalies in Relational Databases
DOI:
https://doi.org/10.36676/jrps.v14.i1.1602Keywords:
Machine learning, AI, AI-Powered Data Anomaly DetectionAbstract
Compliance with data integrity is central to determining reliable and accurate data handling in relational databases. Machine learning, specifically for identifying anomalies, is a groundbreaking concept that improves data and irregularity discovery. This paper analyses the roles played by AI in identifying anomalies, enhancing data accuracy, and managing extensive data. This study outlines through model, practice, and appraisal of such problems that AI can parse all data and secure organizational databases against mistakes and misconceptions. This paper demonstrates the effectiveness of AI-based anomaly detection through field case studies of the financial and healthcare industries. The paper ends with solutions for the challenges of applying AI in anomaly detection, indicating the prospects for future development in this area.
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