AI-Driven Intelligent Data Anomaly Detection Using Machine Learning Techniques

Abstract

Data anomalies—such as outliers, inconsistencies, fraudulent patterns, missing values, and unexpected behaviors—pose significant challenges across domains including finance, cybersecurity, healthcare, retail, cloud operations, and sensor-based IoT systems. Traditional rule-based anomaly detection methods often fail to capture complex, high-dimensional, and evolving patterns. With the rise of artificial intelligence (AI) and machine learning (ML), organizations can now detect anomalies more accurately, adaptively, and autonomously. This research explores the design of an AI-driven intelligent anomaly detection framework leveraging supervised learning, unsupervised learning, clustering algorithms, and deep learning models. The framework enhances anomaly detection by learning from multidimensional data, discovering hidden correlations, generating contextual thresholds, and continuously adapting to changes in the underlying distribution. A real-world case study demonstrates how ML techniques outperform traditional methods in detecting unusual customer behavior in a telecom dataset. The study shows that AI-driven anomaly detection significantly improves accuracy, reduces false positives, and automates behavior interpretation