Comparative Study of Fraudulent Activities and Various Fraud Detection Techniques

Abstract

Abstract: This study highlights the critical role that effective fraud detection systems play by examining the rising occurrence of fraud across industries, which is being driven by technological advancements. By examining a number of fraud categories, such as money laundering, cryptocurrency-related schemes, credit card fraud, and mortgage fraud, this study highlights the need for rigorous preventive measures. By carefully examining a wide range of detection techniques, including Support Vector Machines, Fuzzy Logic, Artificial Neural Networks, Hidden Markov Models, K-Nearest Neighbour, and Bayesian Networks, the research reveals the expanding toolset against fraudulent activity. Through a rigorous examination of applicable literature, creative cutting-edge methods, and astute investigations, the research highlights the crucial necessity of fraud detection systems across a variety of industries. The need for more innovation and interdisciplinary collaboration is highlighted by the discovery of knowledge gaps in real-time data analysis, handling unbalanced datasets, and resilient tactics against adversarial attacks. Our research also contributes to the development of strategies for preventing fraud, ensuring the dependability of financial institutions, and fostering confidence in a time of complex technological connections.