CHAT GPT FOR NETWORK ANALYSIS CRIMINAL CO-OFFENDERS

Abstract

In the complex structure of global society, crime remains a persistent problem. It significantly threatens community foundations and hinders social and economic progress. Today, artificial intelligence (AI) technologies are actively being developed to predict possible offenses and detect and analyze criminal network structures through criminal data analysis. The article presents a new approach to studying social connections in criminal networks using GPT-4 tools. A methodology for visualizing criminal data in graphs has been developed to identify criminal group structures. Visual models of criminal co-offender networks were created using data from 2,113 criminal proceedings involving vehicle theft, robberies, and armed robberies committed in the Ternopil region between 2013 and 2024. Using the GPT-4 multimodal model, data processing was performed and graphs were constructed that reflect the structure of social connections between criminals. The analysis revealed significant differences in the structure of criminal interactions for different types of crimes: vehicle theft shows complex interconnected networks with a high degree of centralization and the presence of key coordinator figures; robberies are dominated by small stable groups of 2-3 people, which is explained by the specifics of executing these crimes; armed robberies are characterized by the formation of larger (4-6 people) and structured criminal groups with defined role distribution, due to the need for violence and ensuring control over victims. The proposed methodology effectively allows law enforcement agencies to counter organized crime in modern conditions. The obtained results have practical value for law enforcement agencies in making operational and strategic decisions, as they allow for the identification of key participants in criminal networks and the prediction of their potential criminal activities.