AI-Driven Fraud Detection and RiskAnalysis of Financial Misinformation

Abstract

Financial fraud is a menace that evolves every now and then and it is difficult to remain afloat and honest by financial institutions. The paper systematically reviews how big data analytics, as well as advanced technologies, including machine learning and natural language processing (NLP), can be used to detect and prevent financial crime. Using the PRISMA guidelines on a systematic review and meta-analyses, 179 peer-reviewed articles were thoroughly reviewed in order to identify the most effective methods and most critical gaps in the literature. The findings demonstrate the potential transformation that big data analytics can bring to the game in terms of identifying trends and unusual behaviors that are typically overlooked by conventional systems so that real-time detection of fraud will become much more accurate and efficient. It was demonstrated that machine learning techniques, particularly, the ensemble models and deep learning algorithms, could be well adapted to shifting trends of fraud. NLP, however, might be used to detect fraud in an unstructured format, such as emails, contracts, and social media. In addition, real-time fraud detection systems were also required to quickly mitigate the situation; however, challenges in integrating multimodal sources of data (audio and video) and shifting the current fraud protection responses to proactive mitigation measures persist. This critical review creates a complete overview of the newest trends, highlighting both achievements and the fields that require additional investigation. It also preconditions the development of powerful, scalable, and ethical systems of fraud detection.