AI-DRIVEN DATA QUALITY MANAGEMENT AND ANOMALY DETECTION IN LARGE-SCALE E-COMMERCE DATABASES: A COMPREHENSIVE ANALYSIS WITH FOCUS ON UZBEKISTAN'S DIGITAL MARKETPLACE ECOSYSTEM

Contemporary e-commerce environments face unprecedented challenges in maintaining data integrity across vast transnational databases. This investigation examines artificial intelligence applications for automated data quality control and anomaly identification within high-volume digital commerce platforms. Our empirical study, conducted across multiple e-commerce ecosystems including Uzbekistan's rapidly expanding digital market, processed 62.7 million transactions over eight months. Machine learning implementations achieved 91.8% precision in detecting data inconsistencies while reducing manual oversight requirements by 72%. Ensemble-based detection systems demonstrated 38% superior performance compared to conventional rule-based approaches, particularly in identifying fraudulent patterns and database anomalies. The Uzbekistan market analysis revealed unique data challenges including multi-currency processing, diverse payment integration, and multilingual content management, providing valuable insights for emerging digital economies.