With the growing sophistication of cyber threats, financial institutions are facing unprecedented risks of fraud, data breaches, and financial crimes. Traditional security measures, while effective in detecting known threats, often struggle to identify emerging attack vectors in real time. Advanced AI-driven threat intelligence systems provide a proactive approach to cybersecurity by leveraging machine learning (ML), deep learning, and natural language processing (NLP) to detect, analyze, and mitigate cyber fraud. These intelligent systems continuously learn from vast datasets, enabling real-time identification of anomalies, suspicious transactions, and fraudulent activities. This study explores the implementation of AI-driven threat intelligence frameworks tailored for financial institutions, highlighting key technologies such as predictive analytics, behavioral analysis, and anomaly detection. The integration of AI with cybersecurity enhances fraud detection through adaptive learning models, which improve over time by identifying new attack patterns. Additionally, AI-powered automated response mechanisms, such as intelligent risk scoring and autonomous threat containment, significantly reduce the time to respond to cyber threats. Challenges associated with AI-driven threat intelligence, including data privacy concerns, adversarial AI attacks, and scalability, are also discussed. Strategies for overcoming these challenges, such as federated learning for secure data sharing, explainable AI for transparency, and hybrid AI models combining rule-based and learning-based approaches, are examined. The findings suggest that financial institutions adopting AI-driven threat intelligence systems can achieve superior fraud prevention, enhanced regulatory compliance, and a more resilient cybersecurity posture.