Anomaly Detection in Financial Transactions Using Advanced Data Mining Algorithms

Abstract

Anomaly detection is an essential task in the field of financial transactions, enabling the identification of fraudulent activities and ensuring the security of financial systems. Traditional methods have become insufficient due to the increasing complexity and volume of financial data. This paper investigates the application of advanced data mining algorithms in anomaly detection for financial transactions. We review various techniques such as clustering, classification, and deep learning models that have been employed to identify anomalous patterns in financial data. The paper also discusses the effectiveness of these algorithms, with an emphasis on their scalability, accuracy, and real-time detection capabilities. Experimental results demonstrate the efficiency of advanced data mining algorithms, highlighting their potential to revolutionize financial transaction monitoring and fraud detection.