The rise of financial crimes and cyber fraud poses significant threats to global economic stability, necessitating a holistic approach that integrates predictive analytics with regulatory compliance frameworks. Traditional fraud detection mechanisms, while effective to a degree, often rely on rule-based systems that lack adaptability to evolving fraudulent tactics. As financial institutions and regulatory bodies strive to stay ahead of sophisticated cyber threats, the role of machine learning (ML), artificial intelligence (AI), and big data analytics has become increasingly crucial. Predictive analytics leverages real-time data processing, anomaly detection, and behavioral pattern recognition to proactively identify suspicious activities before they escalate into systemic financial crimes. Regulatory compliance, on the other hand, serves as the backbone of financial integrity, ensuring adherence to anti-money laundering (AML) laws, Know Your Customer (KYC) protocols, and transaction monitoring requirements. However, fragmented compliance structures and regulatory inefficiencies often create loopholes that fraudsters exploit. By integrating predictive analytics within compliance frameworks, financial institutions can automate risk assessments, enhance transaction screening, and optimize suspicious activity reporting (SARs). This paper explores the synergy between AI-driven fraud analytics and dynamic compliance models, emphasizing the benefits of a risk-based approach in financial crime mitigation. Case studies of major financial institutions illustrate how real-time analytics, blockchain transparency, and AI-enhanced regulatory reporting can significantly reduce fraud occurrences. The study concludes with recommendations for regulatory harmonization, AI-driven compliance automation, and cross-border collaboration to fortify global financial ecosystems against emerging cyber threats.