TRANSNATIONAL CRIMINAL SYNDICATE ACTIVITY

Powerful Capabilities that Collectively Solve the Very Difficult Challenge of Identifying, Tracking and Countering Transnational Criminal Syndicate Activity


We define transnational criminals as persons and entities involved in Human Trafficking, Drug Trafficking and Weapons Trafficking, contraband, money laundering, extortion and terrorism offenses.


Our concept of development of a generative-alert rules engine to identify and report presence of transnational criminal activity across asymmetric digital environments is unique. Our focus is to identify meaningful signatures, through our hard-coded and AI/ML algorithmic approaches and business rules development, supplemented by human-in-the-loop targeted investigative activities.





Advanced Onion’s approach to solving this challenge centers on 3 fundamentals:

1. A core analytics platform that offers unprecedented flexibility with modular and extensible nodes for data management and analytics.

2. Unprecedented person-data led by a large data aggregation company, but also including additional US and international social and professional network sources, customer sources, Dark Web, and targeted human investigation results.

3. A truly multi-modal analysis capability with ‘Human-in-the-Loop’ Validation Cell activity.


Advanced Onion’s focus is to monitor and alert mission leaders and analysts of nefarious activity that is actionable and seeks to identify, track and counter transnational criminal syndicate activity by:

1. Performing advanced analytics on massive US and international data stores leveraging AI/ML associative capabilities, Predictive Modeling and acceptance of uncertainty using fuzzy logic approaches.

2. Incorporating analysis of Dark Web data from historical repository stores, in multiple languages incorporating human and machine translations, and applying human investigator targeted analysis to refine and enhance attributes of entities of interest.

3. Applying advanced entity device correlation techniques to disambiguate identities based on device associations to identified persons.



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