Maritime Security Cooperation

A Maritime Security example of how to risk assess Cargo and Crew

Countries, organizations, and agencies continue to struggle to define, bound, and identify the Maritime Domain.  It has become evident that no single organization/agency has the resources, experts, or wherewithal to accomplish the task alone.  The Maritime Domain is too vast, with too many disparate participants to wrap up into a traditional mission area. Information sharing has become an acknowledged key tenant of any understanding of the Maritime Domain and it is not just country to country, but organizations and agencies within the country as well.

Monitoring the Maritime Domain requires more than just assessing the risk of vessels and owners/operators.  When Maritime Security customers use IHS/GreenLine’s MDA WatchKeeper vessel risk assessment application they tend to naturally gravitate to the next questions of “what if…”.

  • What if I could tailor the data going into the application?
  • What if I could put all the ‘Crew’ data into the system?
  • What if I could put all the ‘Cargo’ data into the system?

The questions go on and on…. and the mantra that ”vessels do not do bad things, people do” is a key tenant for all Maritime Domain/Situational Awareness (MDA/MSA) problem sets and the more unique data brought into the equation helps the risk assessment picture become cleaner.

Crew and Cargo information is regarded differently in countries and agencies; many times it is treated as sensitive law enforcement data, or exclusive customs data.  Naturally, a system with such sensitive data would require it to be hosted in a secure environment behind a firewall. GreenLine System’s enterprise product for this natural progression is called iBench™.

With our iBench™ product you get all the same features and functions as you do with MDA Watchkeeper (vessel risk assessment tool) but you also have the ability to extend the product with your own datasets providing unique insight into the problem by addressing many of the following data points.

The two most popular additional data sets we see customers using are Cargo and Crew. This information allows analysts to really understand the people, cargo and commodities, vessels, and companies that operate within the Maritime Environment.

Crew

The worldwide supply of seafarers in 2010 was estimated to be 624,000 officers and 747,000 ratings (Total 1,371,000). This is based on the numbers holding STCW certificates. (source BIMCO/ISF 2010 Manpower Study)

Key findings:

  • There is little relationship between vessel flag and nationality of crew employed on the vessel.
  • Overall, Asian countries supplied 59.4% of total crewmembers on foreign-flag vessels.
  • Eastern European nations were the second greatest source of crewmembers at 22.1% of the total.
  • W. European nations were an important source of command officers (master & chief engineer).
  • Vessel Size, Age, and Type are important variables affecting crew size.
  • Newer and smaller vessels had lower crew complements.

It is these lists of Crew members and Passengers, including data elements such as date of birth, nationality, identification number and identification type (passport, merchant mariner’s document), position, and place and date of embarkation, usually provided by national agencies that provide insight into this key aspect of MDA/MSA.  iBench™ ingests this data and then fuses it to the Vessel and Voyage. iBench™ then cross-references the crew and passenger names and other information against the designated terrorist and criminal databases and any other watchlists as part of its Risk Analysis
processing, i.e., Office of Foreign Asset Control (OFAC) Specially Designated Nationals (SDN) database.

Here is how this might look:


GreenLine’s iBench™ risk application crew detail page is displayed below:

Cargo

The World Customs Organization (WCO) promotes the use of risk management among its members in order to better utilize customs resources in a manner that enhances and balances trade facilitation with the secure movement of goods across borders and throughout the supply chain.  GreenLine’s iBench™ automatically performs a risk assessment of all manifest and Customs data and is designed to trigger and
identify on the risks associated with:

  • Revenue Evasion
  • Security
  • Prohibited Items
  • Narcotics
  • Safety
  • Other threats

iBench™ tiers the manifest and declaration data in order of high, medium, and low risk scores.  This will facilitate the triage of the data.  Presenting the data in this manner allows analysts to ensure they are focused on the highest risk trade while pre-approved and/or low risk trade can be facilitated.

This approach has been proven to be highly successful in identifying and interdicting threats upon arrival at a port of entry.  iBench™ can be used to refer shipments of interest for closer scrutiny. Results of inspections are collected and used to validate the reasons for selectivity.  This ensures that the system is always updated with the latest smuggling trends.

The next few slides provide a quick case study of how this would work within iBench. In this first slide you can see the voluminous amount of traffic around the Netherlands and when constraints are added so that we only see the ships having cargo onboard that is destined to a port of discharge within The Netherlands, we pare down the problem to a much more manageable number.

 

Cargo information, including description and type of cargo, quantity, harmonized tariff schedule code, HAZMAT code, shipper, owner/consignee, origin and destination, can be pulled from Export Declarations and Manifests. Additionally, Notice of Arrivals (NOA), Stow Plans, and Container Status Messages (CSM) provide varying degrees of cargo information. A NOA would typically provide general cargo information but not have Owner or Origin. Stow Plans and CSMs, while available in a wide variety of formats, will typically include the place of receipt and the place of delivery for the cargo and names and addresses of the Shipper and owner/Consignee.

You can also create views that allow you to drill down

on one ship and see the detailed movement track.

You might also want detailed information on what is driving the risk score as we see here:


Drilling down into the risk scorecard shows the anomalies that create the cumulative score and resultant level of risk, in this case theses anomalies are:

  • Recipient is a bank, not normal
  • Shipper is a Freight Forwarder, not normal
  • Container Check digit sum is incorrect
  • Vague Commodity Description


 Stow Plan Viewer

Another key aspect of GreenLine’s iBench™ risk application is the Stow Plan Viewer feature which allows users to quickly see where any suspicious containers are located on a ship and if any nearby containers are carrying HAZMAT, both key pieces of operational information in the event an interdiction is needed. Users can drill down into individual containers to see the full contents and additional logistical details.

Conclusion

Using a broad set of criteria, iBench™ generates risk profiles for vessels, crew, and cargo based on an integrated universe of transactional information, references, and watch lists.  iBench™ supports the simultaneous execution of rule sets for independently assessing risk on measures such as anti-terrorism, counter proliferation, narcotics, etc. It uses proprietary and third-party reference data and watch lists, and supports easy configuration of scores, thresholds, and weights associated with rules and rule categories while providing complete transparency into the criteria and rationale used to assess risk.

The system supports administrative controls that allow enabling/disabling specific rules; adjusting risk scores associated with each rule; grouping rules into risk categories such as Trusted Shipper, Origin Determination, Country of Risk, En route Compromise etc.; as well as adjusting risk thresholds and weights associated with risk categories and risk assessment modules.

Bringing multiple data sources to the MDA/MSA problem sets allow analysts to really drill down using complex and associated rule sets to really understand how vessels, cargo, and crew are inter-related and provides the analyst a multi-dimensional view of the Maritime Environment.

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