Role of Artificial Intelligence and Big Data Technologies in Enhancing Anomaly Detection and Fraud Prevention in Digital Banking Systems

Abstract

The rapid digital transformation of the banking sector has led to a significant increase in online transactions, bringing both opportunities and challenges. Fraudulent activities and financial anomalies are on the rise, threatening the integrity and security of digital banking systems. Artificial Intelligence (AI) and Big Data technologies have emerged as transformative tools for detecting anomalies and preventing fraud in real-time, offering innovative solutions to counter these threats. AI algorithms, such as machine learning and deep learning, are enabling banks to identify suspicious behaviors, predict fraudulent patterns, and adapt dynamically to evolving threats. Concurrently, Big Data technologies allow the processing of massive amounts of transactional and behavioral data, creating a foundation for the deployment of advanced analytics. This paper explores how AI and Big Data technologies enhance anomaly detection and fraud prevention in digital banking. Key areas of focus include predictive modeling, anomaly detection techniques, behavioral analysis, and risk scoring. Additionally, challenges such as algorithmic biases, data privacy concerns, and the scalability of solutions are discussed. By leveraging AI and Big Data in tandem, financial institutions can improve security, ensure regulatory compliance, and build trust with their customers. This paper concludes with insights into future trends and recommendations for strengthening fraud prevention frameworks using these technologies.