AI-Driven Algorithmic Intelligence for Navigating Complexity: Entropy-Based Models for Cybercrime and Management Economic and Financial Systems

Navigating the complexities of modern organizational landscapes, particularly in the context of cybercrime and economic – financial challenges, remains a critical issue for industries. Despite advancements in hybrid intelligence, cloud-based platforms, and algorithmic solutions, gaps persist in integrating data-driven, entropy-based approaches into next-generation management systems tailored for cybercrime prevention and economic optimization. This study addresses these gaps by proposing a novel framework that integrates a hybrid algorithmic model with entropy-based optimization techniques. Utilizing four datasets—three publicly available and one originally collected through online sources—this research explores how real-time data and adaptive decision-making can enhance cybercrime detection and economic – financial forecasting. The theoretical novelty lies in combining entropy-based modeling with a rule-based neural network to achieve superior accuracy, explainability, and scalability in complex settings. The proposed system delivers practical benefits, including improved cyberthreat identification, economic anomaly detection, and resource optimization, fostering resilient and adaptive management frameworks. Experimental results demonstrate statistically significant improvements in accuracy (p < 0.05) compared to baseline models, particularly in dynamic, resource-intensive environments. This study contributes to the literature by offering a comprehensive empirical evaluation, discussing integration with existing enterprise systems, and addressing scalability and cost-effectiveness in the context of cybercrime and economic management. By bridging these research gaps, we present an approach with both theoretical significance and practical utility for combating cybercrime and optimizing economic and financial performance.