Exploring Data-Driven Approach for Financial Fraud Detection: A Comprehensive Literature Review

Abstract

Financial fraud detection has emerged as a critical area of research with the growing complexity and scale of fraudulent activities in the financial sector. Traditional methods of fraud detection, which are based on rule-based systems and manual oversight, fail to capture the dynamic and sophisticated nature of modern fraud schemes. This comprehensive literature review examines data-driven approaches that take into account the advancement of machine learning, artificial intelligence, and big data analytics to improve fraud detection. Some of the key methodologies covered are supervised, unsupervised, and hybrid models. The survey reflects growing usage in neural networks, ensemble methods, and anomaly detection techniques, emphasizing their performance in identifying complex fraud patterns in different financial datasets. Discussions include the difficulties with unbalanced datasets, evolving tactics for frauds, and requirements for explainability that remain future areas of interest. Drawing upon recent relevant research work, this review synthesis aims at informing readers concerning the landscape evolution in fraud detection against finances and presenting possible innovations in order for these to remain robust yet adaptive, clear, and transparent in nature.