The increasing sophistication of cyber threats necessitates advanced methods for securing financial systems. This paper introduces an innovative approach to fraud detection and prevention using Adversarially Resilient Deep Learning (ARDL) models. Leveraging adversarial training techniques, these models withstand attempts by malicious actors to deceive detection mechanisms. The ARDL framework incorporates robust data preprocessing, feature extraction, and anomaly detection, collectively enhancing the system's ability to identify fraudulent activities. Extensive experimentation on real-world financial datasets demonstrates the models' superior performance in detecting and mitigating fraud compared to traditional methods. The research emphasizes continuous learning and adaptation to evolving cyber threats, equipping ARDL models with mechanisms for ongoing refinement and validation to ensure resilience against new fraud tactics. AdditionallyThe study emphasizes the importance of explainability and transparency in AI-driven fraud detection systems, proposing strategies for providing clear, interpretable insights into model decisions. These advancements pave the way for more secure financial systems capable of proactively countering adversarial threats and safeguarding sensitive transactions.