Pole Star Global reports that it has launched the Maritime Transparency Index (MTI), a machine learning-based risk scoring system designed to provide assessments of vessel, voyage and ownership transparency.
The MTI converts a variety of maritime compliance data into a single score ranging from 0, described as ‘Hard Dark’, to 5, classified as ‘Transparent’. The index uses Pole Star’s maritime intelligence datasets and applies machine learning models to assess more than 40,000 vessels on a quarterly basis, representing around 80% of the active commercial fleet.
The vessel score evaluates historical and structural indicators such as vessel age, ownership and flag changes, sanctions exposure, port state control detentions and recorded deficiencies. Vessel position analysis focuses on AIS reporting gaps, spoofing activity and other behavioural indicators collected from multiple position data sources.
Operational behaviours including port call patterns, ship-to-ship transfers, slow steaming, unexplained delays and exposure to higher-risk geographies are also assessed by the system.
Pole Star Global said the technology is designed to automatically identify issues such as AIS manipulation, dark activity, flag hopping and frequent ownership changes, updating scores as vessel activity and risk profiles evolve.
“By combining machine learning and advanced analytics with 25 years of maritime intelligence expertise, we’ve created a tool that’s as powerful as it is simple to use,” said Saleem Khan, Chief Data and Analytics Officer of Pole Star Global.
“MTI applies credit-style scoring to maritime operations, transforming complex data patterns into clear, actionable scores that give compliance teams, flag states, and ship owners a common language for transparency.”
“What once required hours of analysis now takes seconds, enabling our customers to make faster, more informed decisions while reducing exposure to sanctioned vessels and deceptive shipping practices.”
