Abstract
Digital transformation of public administration is an important direction for modernizing regulatory systems in strategic sectors, including subsoil use. The aim of this study is to develop an analytical model of digital risk-based regulation using data analytics and statistical methods. The methodology includes panel data analysis, regulatory risk forecasting, and comparative analysis of monitoring models. The proposed digital monitoring model enables the identification of high-risk facilities and the optimization of inspection resource allocation. The findings demonstrate that a risk-based approach supported by digital data improves the efficiency of regulatory oversight while reducing administrative burdens. The scientific novelty of the study lies in integrating data analytics tools with the concept of risk-based regulation in the subsoil use sector.