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Financial institutions grapple with the escalating nature of fraudulent activities, necessitating innovative and timely detection methods. The review underscores the transformative potential of Big Data Analytics, emphasizing its pivotal role in the ongoing fight against fraud. Delving into the specifics, the paper explores diverse data sources, such as transaction and user behavior data, alongside external data from sources like social media, employing machine learning algorithms and predictive modeling for anomaly detection and risk assessment. Real-time processing emerges as a critical component for swift and effective fraud identification. Critically addressing implementation challenges, including data quality assurance and privacy concerns, the paper showcases case studies of successful Big Data Analytics implementations, highlighting their positive impacts on fraud prevention and financial security. Looking ahead, the review anticipates the role of emerging technologies like blockchain and artificial intelligence in enhancing fraud prevention strategies, emphasizing integration with cybersecurity for robust defense against sophisticated attacks. The paper concludes with recommendations for financial institutions, advocating collaborative efforts and information sharing within the industry. In summary, the review underscores the transformative contributions of Big Data Analytics to financial fraud detection, shaping the future of fraud prevention strategies and fortifying the resilience of the global financial ecosystem.. Keywords: Big, Data, Analytics, Finance, Fraud, Detection.
Shoetan et al. (Sun,) studied this question.