Relational hyperevent data capture time-stamped/-ordered interactions among any number of nodes in a network. Such data are central in criminal network research, both in studies on social organisation of crime and on co-offending. Until recently, researchers had to coerce these data for standard network methods, risking loss of information and biased results. The relational hyperevent model (RHEM) solves this by modelling hyperevent data directly, preserving their temporal and structural detail.This article introduces RHEM to criminal network researchers. We show how to collect and prepare data for RHEM, outline the model's logic in non-technical terms, compare it to other models and highlight the theoretical questions RHEM enables to answer. Two examples - a directed communication network and an undirected co-offending network - were used to illustrate the full workflow from data processing to model interpretation. Finally, we point the readers to RHEM applications in both criminal and non-criminal network studies for inspiration.
Caribbean Citizenship by Investment (CBI) programmes present considerable money laundering risks that Middle Eastern financial institutions must navigate under intense international scrutiny. Through semi-structured interviews with 47 senior financial executives across six Gulf and Levant states, we examine how professionals assess CBI-related transactions in their daily compliance work. Participants described attending to red flags including unusually rapid passport acquisition, mismatches between stated business activities and transaction patterns, and layered fund transfers designed to obscure origins. Executives described balancing legitimate client migration needs against regulatory pressures from Western correspondent banks that increasingly flag Caribbean passports as high-risk indicators. The findings suggest that front-line compliance professionals developed practical heuristics for risk assessment despite regulatory ambiguity, often relying on relationship banking knowledge and network intelligence rather than formal due diligence protocols alone. These insights illuminate the operational realities of anti-money laundering compliance in contexts where geopolitical tensions, economic instability and global mobility intersect.
