AI-Driven Anomaly Detection and Performance Optimization in Background Screening Systems

Background screening systems play a vital role in verifying credentials, ensuring compliance, and maintaining security across industries. However, conventional monitoring methods rely on manual oversight and rule-based anomaly detection, leading to inefficiencies such as system failures, high false positives, and slow incident resolution. This research proposes an AI-driven framework leveraging the Isolation Forest algorithm with dynamic contamination control, predictive failure analysis, and automated log analysis to enhance anomaly detection accuracy and optimize system performance. The study demonstrates substantial improvements, including an 80% reduction in system failures, a 75% decrease in resolution times, and a tenfold increase in system scalability. The integration of AI-based infrastructure scaling and compliance automation further strengthens the security and regulatory adherence of background screening systems. This work contributes to the growing field of applied machine learning by demonstrating the effectiveness of AI in optimizing critical business processes.