The rapid proliferation of transnational electronic transactions has led to an unprecedented increase in the sophistication and frequency of financial fraud activities. Conventional machine learning-based fraud detection systems often struggle to adapt to evolving behavioral patterns and jurisdictional heterogeneity across global payment infrastructures. To address these limitations, this paper introduces an Adaptive Hybrid Intelligence Framework (AHIF) designed to enable proactive and dynamic fraud detection in cross-border transaction ecosystems. The proposed framework integrates cognitive reasoning, deep learning, and adaptive reinforcement mechanisms within a federated intelligence layer to enhance detection accuracy, resilience, and interpretability. The system employs multi-source data fusion, contextual anomaly scoring, and continuous learning to identify latent risk signatures in real time. Experimental validation, conducted using benchmark and synthetic international payment datasets, demonstrates significant improvements in detection precision and adaptability compared to existing models. The results confirm that AHIF effectively minimizes false alerts, anticipates cross-border fraudulent trends, and ensures rapid response to emerging transactional anomalies. The implications of this study extend toward the development of globally interoperable, privacy-preserving fraud prevention architectures that strengthen the integrity and trust of international digital financial networks.

Keywords: Hybrid Intelligence, Adaptive Systems, Cross-Border Fraud Detection, Electronic Transactions, Federated AI, Cognitive Analytics, Financial Cybersecurity