A Comprehensive Survey on Fraud Detection Methods in Financial Transactions

Abstract

Financial institutions have enormous hurdles as a result of fraudulent transactions, which makes the creation and application of strong detection techniques necessary. This research paper offers a thorough examination of the approaches currently in use for identifying fraudulent activity on different financial platforms. It examines both cutting-edge and conventional strategies, such as data mining, artificial intelligence, and machine learning algorithms, as well as statistical and rule-based systems. It also looks at how big data and real-time analytics may work together to improve detection accuracy. Future approaches in fraud detection research are also covered in this study, along with the advantages and disadvantages of these methods and the significance of adaptive learning models. Through an amalgamation of current developments and persistent obstacles, this assessment seeks to provide significant perspectives for scholars committed to battling financial deception. Key Words: Fraudulent transactions, financial platforms, rule-based systems, machine learning, data mining, real-time analytics, big data.