The increasing volume and complexity of financial transactions have made traditional fraud detection mechanisms inadequate in identifying sophisticated fraudulent activities. Big Data-driven financial fraud detection and anomaly detection systems have emerged as transformative solutions that enhance the accuracy, efficiency, and adaptability of fraud prevention frameworks. By leveraging advanced machine learning algorithms, artificial intelligence (AI), and real-time data analytics, these systems can detect suspicious patterns, anomalies, and illicit financial behaviors with greater precision. The integration of Big Data analytics into fraud detection enables financial institutions to process vast amounts of structured and unstructured data from diverse sources, including transactional records, customer behavior patterns, and external regulatory data. This facilitates proactive fraud prevention by identifying subtle correlations and emerging threats that conventional rule-based systems might overlook. Moreover, anomaly detection systems play a critical role in ensuring regulatory compliance, as they assist financial institutions in adhering to stringent anti-money laundering (AML) and counter-terrorism financing (CTF) regulations. By continuously monitoring transactions, detecting outliers, and reducing false positives, these systems contribute to market stability and investor confidence. Despite their benefits, the implementation of Big Data-driven fraud detection systems presents challenges, such as data privacy concerns, computational costs, and adversarial attacks on AI models. Future research must focus on improving explainability, enhancing robustness, and integrating blockchain technology for secure and tamper-proof transaction verification. As financial crime tactics evolve, the synergy between regulatory authorities, financial institutions, and AI-driven fraud detection systems will be crucial in maintaining the integrity of global financial markets.