The banking sector faces escalating cyber threats, necessitating robust cybersecurity solutions. This paper investigates AI-driven frameworks for unsupervised fraud detection, emphasizing their role in enhancing banking cybersecurity. By integrating artificial intelligence (AI) with unsupervised learning, these frameworks excel in identifying anomalous patterns indicative of fraud without relying on labeled datasets, making them adaptable to emerging threats. The study examines their IoT security and predictive analytics application, offering a proactive approach to real-time cyber attack prevention. A thorough literature review evaluates recent advancements, uncovering challenges such as model interpretability and adversarial robustness. The proposed methodology employs AI algorithms, including clustering and autoencoders, to detect subtle anomalies in transactional data, augmented by a hybrid approach combining natural language processing and graph theory for deeper insights. Results affirm the frameworks' effectiveness in bolstering fraud detection, highlighting their transformative potential for banking security. However, limitations like data dependency and the need for continuous updates are noted. The paper addresses these challenges and proposes future research directions, such as quantum machine learning and explainable AI, to counter evolving threats. This work underscores the critical need for innovative, adaptive cybersecurity strategies to safeguard the banking sector's sensitive data and financial assets.