The increasing sophistication of fraudulent financial activities necessitates the development of intelligent systems capable of dynamically adapting to evolving attack patterns. Traditional fraud detection models often rely on static feature sets and fixed decision boundaries, which limit their ability to identify emerging threats in real time. This study presents a comprehensive exploration of adaptive learning architectures integrated within deep neural network (DNN) frameworks for enhancing financial fraud detection accuracy and resilience. From a broader perspective, the research examines the convergence of machine learning, behavioral analytics, and adaptive systems in managing large-scale transactional datasets across digital banking and fintech platforms. The proposed architecture employs self-adjusting learning layers and dynamic feature weighting mechanisms that enable the model to recalibrate its parameters as fraudulent behavior evolves. By incorporating temporal drift detection and contextual embedding techniques, the framework continuously improves performance through feedback-driven retraining cycles. Empirical validation using real-world financial datasets demonstrates that the adaptive DNN outperforms conventional static models in identifying anomalies and minimizing false positives. Furthermore, the model’s scalability and interpretability are enhanced through explainable AI components, ensuring compliance with regulatory standards and facilitating integration into enterprise risk management systems. The findings emphasize that adaptive deep learning frameworks not only improve detection precision but also provide a sustainable solution for mitigating financial risks in an increasingly digitalized economy. This research contributes to the advancement of fintech security analytics, offering a robust foundation for future intelligent fraud prevention systems capable of learning and evolving autonomously.