Optimising corporate governance with internet of things and artificial intelligence: a data-driven framework for legal systems

Abstract

This research introduces an IoT-driven data analysis and AI framework for optimising corporate governance. The framework leverages deep learning algorithms, including transformer-based neural networks, convolutional neural networks (CNNs), and reinforcement learning models, to enhance decision-making, regulatory compliance, and transparency. Real-time data streams from IoT devices were processed, and a dataset of over 500 corporate entities was analysed. Transformer models achieved a predictive accuracy of 99.2% in identifying governance risks, CNNs detected anomalies in IoT data with 98.6% accuracy, and reinforcement learning models reduced compliance-related delays by 47%. The framework also led to a 38% increase in regulatory adherence and a 55% improvement in operational efficiency. These results demonstrate the potential of IoT and AI in addressing modern governance challenges and provide a scalable solution for sustainable governance.