Happiest Minds Anomaly Detection is a machine learning-based solution designed to identify anomalies across various domains including security threats, fraud detection, device failure prediction, and discount glitches. The solution operates as a domain-agnostic platform that runs multiple algorithms to detect data anomalies. The system uses a feedback-based approach that learns over time by evaluating algorithm efficiency. This iterative process allows the solution to adapt and improve its detection capabilities based on performance metrics. The platform is positioned as an alternative to traditional rule-based engines, which may not provide adequate insights given the increasing volume and variety of data. The solution addresses the challenge of analyzing large-scale data environments where traditional processes and rule-based systems are insufficient. It automates the iterative process of anomaly detection by continuously evaluating and selecting the most effective algorithms for the specific data patterns encountered. The platform is designed to handle the complexity of modern digital environments where data production continues to grow exponentially. It provides organizations with the ability to detect unusual patterns and behaviors that may indicate security incidents, fraudulent activities, or operational issues without relying solely on predefined rules.