Intelligent Surveillance Engine (ISE): An AI-Driven Digital Sovereignty Framework for Financial Crime Detection

The growing threat of cyber-enabled financial crimes, along with data sovereignty regulations, poses serious challenges for today’s fraud detection systems used for digital sovereignty. Traditional centralized methods struggle to detect complex fraud patterns and often fail to meet national data privacy requirements, leading to many undetected fraud cases and reduced accuracy. This chapter introduces the Intelligent Surveillance Engine (ISE), a sovereign-compliant artificial intelligence (AI) approach developed to enhance financial fraud detection. Unlike existing frameworks, ISE is purposefully designed to enable national digital sovereignty through auditable, privacy-preserving AI, adaptable to diverse legal and geopolitical contexts such as GDPR in Europe and India’s MeghRaj. ISE uses a mix of collaborative filtering, layered anomaly detection, and ensemble learning to improve fraud detection. It creates user behavior profiles, applies unsupervised techniques like Isolation Forest, Autoencoders, and DBSCAN to find unusual patterns, and then uses supervised classifiers like Random Forest, SVM, and Decision Trees. The results are combined through methods like stacking and majority voting to increase accuracy. Tests on real and synthetic financial datasets showed that ISE achieved a False Negative Rate (FNR) of 0.0%, Recall of 99.55%, and an F1-Score of 99.7%. These results significantly outperform conventional fraud detection systems, which had an FNR of 36.11%, Recall of 65.2%, and an F1-Score of 88.21%. The study illustrates that ISE significantly enhances anomaly detection in financial systems by reducing false negatives, aligning with digital sovereignty requirements, and offering a scalable, adaptive, and regulation-compliant fraud mitigation architecture that outperforms conventional models. This study also highlights how ISE enforces digital sovereignty through privacy-preserving AI models, national data control, and ethical AI governance architectures. Financial crime detection systems often face challenges balancing efficiency, privacy, and compliance with digital sovereignty principles. This study aims to propose the Intelligent Surveillance Engine (ISE), an AI-driven framework for sovereign-compliant financial fraud detection. A hybrid approach integrating systematic anomaly detection, privacy-preserving machine learning models, and sovereign data governance mechanisms was adopted. Results demonstrate that ISE achieves high detection accuracy while ensuring compliance with digital sovereignty and ethical AI governance requirements. These findings suggest that sovereignty-aware AI systems like ISE are vital for national data control, ethical surveillance, and technological independence.