Abstract

This paper presents a conceptual model for AI-powered anomaly detection in airline booking and transaction systems. As the airline industry becomes increasingly reliant on digital platforms for booking, payment processing, and customer management, the need for robust anomaly detection mechanisms to safeguard transactions and improve operational efficiency has never been more critical. The proposed model integrates advanced AI techniques, such as supervised learning, clustering, and deep learning, to identify fraudulent bookings, erroneous ticketing, and payment discrepancies. It emphasizes key components such as data preprocessing, feature extraction, anomaly detection algorithms, and real-time post-detection actions like alerts and automated response protocols. The paper also discusses practical applications, including the detection of fraud in airline transactions and the operational benefits of AI-powered systems, such as improved fraud detection, enhanced user trust, and cost savings. Additionally, it examines case studies that highlight the successful implementation of AI-driven anomaly detection and suggests future research directions to improve algorithm accuracy and adaptability. Ultimately, this paper demonstrates the significant role AI can play in ensuring secure, efficient, and reliable airline booking and transaction systems.