Big Data Analytics And Ai-Driven Solutions For Financial Fraud Detection: Techniques, Applications, And Challenges

Abstract

Financial fraud is an ever-evolving threat that poses significant challenges to the stability and integrity of financial systems. This study systematically reviews the application of big data analytics and advanced technologies, including machine learning and natural language processing (NLP), in detecting and mitigating financial fraud. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 189 peer-reviewed articles were meticulously analyzed to explore state-of-the-art methodologies and identify critical gaps in the literature. The findings underscore the transformative role of big data analytics in uncovering patterns and anomalies that traditional systems often miss, enabling real-time fraud detection with enhanced accuracy and efficiency. Machine learning techniques, particularly ensemble models and deep learning algorithms, were found to adapt effectively to dynamic fraud patterns, while NLP extended the scope of detection to unstructured data, such as emails, contracts, and social media. Furthermore, real-time fraud detection systems emerged as vital tools for immediate mitigation, yet challenges persist in integrating multimodal data sources like audio and video and shifting from reactive to proactive fraud prevention strategies. This comprehensive review provides a holistic understanding of current advancements, highlighting both achievements and areas requiring further exploration, and sets the stage for the development of robust, scalable, and ethical fraud detection frameworks.