Anomaly Aware Transaction Monitoring System

Abstract-- The rapid growth of digital transactions in banking, e-commerce, and online payment platforms has made transaction security a major issue. Traditional monitoring systems often struggle to detect new or changing fraudulent activities because they depend on predefined patterns. An Anomaly-Aware Transaction Monitor addresses this problem by focusing on identifying unusual or unexpected transaction behaviors that differ from normal user patterns. This system continuously monitors transaction data, such as amount, frequency, location, and time, learning what is considered "normal" over time. When a transaction varies significantly from established patterns, it is flagged as potentially suspicious for further review. By being aware of anomalies, the system can adjust to changing user behavior and detect previously unknown fraud attempts more effectively. This approach improves early fraud detection, reduces false positives, and builds overall trust in digital financial systems. Anomaly-aware monitoring is essential for enhancing security, minimizing financial losses, and ensuring safer digital transactions in today's financial ecosystems. Keywords: Anomaly Detection, Transaction Monitoring, Fraud Detection, Financial Security, Machine Learning, Behavioral Analysis.