Leveraging AI for Fraud Detection in Identity and Access Management: A Focus on Large-Scale Customer Data

Abstract

Due to the increasing complexity of theft, organizations handling substantial consumer data must implement sophisticated identity and access management (IAM) systems. This study examines the application of artificial intelligence (AI) in detecting fraud within Identity and Access Management (IAM) systems, particularly those managing extensive customer datasets. The study examines the challenges arising from the vast volume, rapidity, and diversity of data, together with the evolving nature of fraud schemes. Artificial intelligence (AI) encompasses machine learning algorithms, anomaly detection models, and pattern recognition methodologies. These can swiftly identify potential scam scenarios that traditional approaches may overlook. The research examines the advantages and disadvantages of employing AI for fraud detection. It addresses concerns regarding data quality, false positives, and system scalability. Enhancing scam protection necessitates continuous training of models, as evidenced by real-world examples, and the integration of AI into current identity and access management systems. This report provides valuable recommendations for firms seeking to enhance their IAM systems through the utilization of AI technologies.