The Evolution and Future Development of Criminological Forecasting in the Republic of Moldova

Abstract

This article examines the evolution and future prospects of criminological forecasting in the Republic of Moldova, presented as a key component of contemporary criminological science and crime prevention practice. The study aims to trace the historical stages of its development—from the absence of systematic methods during the Soviet period and the first attempts at their introduction in the post-Soviet era to current trends associated with digital technologies and international cooperation. Particular emphasis is placed on the impact of European integration processes and on the contributions of Moldovan scholars, who have played a pivotal role in shaping a national tradition of criminological analysis. The significance of the study lies in its dual perspective: criminological forecasting is examined both as a theoretical category and as a practical tool for enhancing the effectiveness of law enforcement in a context marked by limited resources, institutional barriers, and a complex criminogenic environment. The article underscores that successful progress in this field depends on three interconnected factors: the adoption of modern technologies, the strengthening of the local scientific base, and the intensification of international cooperation. Accordingly, the article has both theoretical and applied value. On the one hand, it reconstructs the trajectory of criminological thought in Moldova; on the other, it offers concrete recommendations for state institutions, law enforcement bodies, and the academic community. The study demonstrates that the integration of artificial intelligence, the establishment of a unified national crime database, and the expansion of international partnerships can significantly elevate Moldova’s criminological forecasting system to a qualitatively new level. KEYWORDS: artificial intelligence, crime prevention, criminological forecasting, European integration, international cooperation, Moldova, predictive analytics, transitional societies