Anomolies in Financial Transactions

Abstract

This paper investigates the diverse spectrum of irregularities prevalent in financial transactions, ranging from fraudulent activities to unintentional errors. By examining the various types of irregularities, including money laundering, insider trading, and accounting manipulations, this study aims to elucidate their underlying causes and implications for financial institutions and markets. Drawing upon empirical research and theoretical frameworks, the paper explores the detection methods, regulatory measures, and technological advancements crucial for mitigating these irregularities. Furthermore, it discusses the role of machine learning algorithms and artificial intelligence in enhancing detection accuracy and reducing false positives. Ultimately, this research underscores the imperative for proactive measures and collaborative efforts among stakeholders to safeguard the integrity and transparency of financial transactions in today's dynamic global landscape. The detection and prevention of irregularities in financial transactions have become paramount in the contemporary landscape of finance. With the rapid advancement of technology and the increasing complexity of financial instruments, traditional methods of oversight are often inadequate in identifying fraudulent activities. This abstract presents a comprehensive analysis of various techniques and methodologies employed in the detection of irregularities in financial transactions.