Methodology
Sayari analysts developed a systematic methodology for identifying entities at higher risk of chemical diversion by analyzing trade patterns across 70+ precursor chemicals used in illicit synthetic drug production. The approach focuses on entities where essential chemical imports constitute more than 50% of total import volume – a strong indicator of potential diversion activity. By cross-referencing import manifests, corporate registration data, and known trafficking patterns, the analysis identifies companies whose import profiles are inconsistent with legitimate commercial activity.
Supply chain mapping
The investigation traces precursor chemical supply chains from Chinese manufacturers through intermediary trading companies – including U.S.-based front companies – to final recipients in Mexico. Trade data reveals that many of these supply chains converge on specific geographic areas in Sinaloa and Jalisco, regions with known cartel manufacturing operations. The analysis identifies 31 Mexico-based entities whose import patterns, corporate structures, and geographic concentrations indicate elevated risk of involvement in precursor chemical diversion.
Network analysis
Corporate record analysis reveals shared ownership structures, common registered agents, and overlapping supply relationships among the identified entities – suggesting coordinated procurement networks rather than isolated actors. Several entities share beneficial owners or directors who appear across multiple import companies, indicating deliberate organizational design to distribute risk and evade detection. The findings demonstrate how trade data and corporate records together reveal network structures that neither data source could expose alone.
Why this matters
The fentanyl crisis is a supply chain problem. Understanding precursor chemical procurement networks is essential for disrupting production before drugs reach the street. This methodology – combining trade pattern analysis with corporate network mapping – provides a scalable, data-driven approach to identifying high-risk entities that traditional intelligence methods miss. For government agencies, this means earlier detection of diversion networks. For enterprise compliance teams, it means better screening of trading partners connected to illicit supply chains.
Sayari’s Commercial World Model covers 11.7B+ primary-source records across 250+ jurisdictions. The platform resolves entity identities, traces ownership chains, and delivers evidence-grade intelligence that enables analysts to conduct investigations like this one at scale – from corporate registries and trade manifests to beneficial ownership records and sanctions lists.