Anomaly Detection in Financial Transactions Using Advanced Data Mining Algorithms

Abstract

In the realm of financial transactions, detecting anomalies is critical for identifying fraudulent activities, ensuring security, and enhancing decision-making processes. With the rise of digital payments and online banking, the volume and complexity of financial transactions have grown significantly, making manual detection of anomalies insufficient. This paper explores the use of advanced data mining algorithms to automate the anomaly detection process. Various algorithms, including clustering, classification, and deep learning techniques, are examined for their effectiveness in identifying suspicious behavior in financial data. The study evaluates the strengths and weaknesses of these algorithms based on several metrics, such as detection accuracy, computational efficiency, and scalability. A comparison of results from different algorithms is provided to guide future implementations in the financial sector.