Our Challenge: Detecting Illegality in Global Supply Chains

Illegal trade in wildlife, timber and fisheries products is pervasive, but largely hidden. Much of that trade impacts some of the most endangered species, forests and marine ecosystems on our planet.  Big data analytics has been harnessed to uncover hidden patterns and illegality in other sectors, but the application of big data analytics for illegal wildlife, timber and fisheries trade monitoring has only recently begun.

Large amounts of publicly available data exist on the domestic and international trade in wildlife, seafood and timber products. There is an urgent need to accelerate the analysis of this data beyond conventional methods, to detect potentially illegal flows involving organized crime rings, and to spotlight discrepancies in global trade protocols that can obscure such trade. This work could help to support efforts of enforcement agencies, NGOs and companies that are committed to legal sourcing—to mitigate risks and to support responsible trade. There is immediate need for conservation practitioners to:

• Exchange case studies and lessons learned from applying big data analytics in other sectors;
• Identify and enhance access to relevant data sources that are common to wildlife, timber and fisheries trade;
• Identify prospective partnerships for future collaborations to detect illegality in global wildlife, timber and fisheries supply chains; and
• Identify big data quality and analysis challenges, and potential solutions.

The United States: A Major Market

The US is one of the world’s largest end-markets for many illegal products from endangered species and regions, many of which are purchased by unsuspecting consumers.  Illegal activity can occur throughout a supply chain, ranging from poaching in protected areas, to situations where illegally sourced products become “legalized” due to falsified paperwork or a lack of enforcement, to misdeclarations of species or country of origin. 

Big data analytics holds enormous promise.  The Obama Administration has highlighted the importance of encouraging the potential of these technologies, while also emphasizing the importance of minimizing risks to privacy, fair treatment, and other core American values” (The White House 2015).  Large amounts of publicly available trade and related data exists for wildlife, seafood and timber products such as customs data sources (e.g., COMTRADE, PIERS, Panjiva, Zepol); the CITES trade database; various publicly available information such as commodity quotas, tariffs and export bans; government data sources such as USFWS LEMIS, and other industry sources.  But there is currently limited capacity to analyze this disparate data in a comprehensive, cost-effective way, to detect potentially illegal trade where organized crime may be involved. Further, there could be problems distinguishing between reasonable data “mistakes” or inconsistencies in ways that global trade data is reported, versus deliberate illegal trade. 

Wildlife

The illegal wildlife trade has an estimated value of $7.8 – $10 billion per year and “like other forms of illicit trade, wildlife trafficking undermines security across nations”(The White House 2014). An estimated 30,000 African elephants were killed in the last year to supply the illegal ivory market, with an average of 18 tons of ivory seized per year over the past 20 years. Large ivory shipments are generally concealed in sea containers that also transport other cargo and have shifting trade routes. Lesser known wild species—ranging from pangolins to tortoises and terrapins, tokay geckos, and song birds—are also being depleted for consumption and for the live animal trade. Between 2000 and 2012, 218,155 pangolins were reported in seizures—a significant underrepresentation of the total estimated volume of trade (TRAFFIC 2013). Nineteen thousand tortoises and freshwater turtles were seized in Thailand alone between 2008 and 2013 (TRAFFIC 2014). Too little is known about the role of the United States market as a driver of illegal wildlife trade. Harnessing the power of big data analytics to draw connections between incidents and to flag risk areas where companies may be unknowingly enabling the transport of illegal wildlife cargo—can help to ensure the legality of trade and to deliver on the US National Strategy for Combating Wildlife Trafficking.

Customs officials in Suvarnabhumi discover a shipment of African elephant tusks from Mozambique.

© WWF / James Morgan

Timber

The US is a significant end-user of tropical wood species from the Amazon, Southeast Asia, and the Congo Basin, where rates of illegal logging can exceed 80%.  A 2012 UNEP/Interpol report highlighted that as much as 30% of all timber traded globally may be illegal, with an estimated value of $30-100B annually (Nellemann 2012). Estimates are that as much as $3 billion in annual US timber imports may be of illegal origin (Lawson 2015). A 2014 TRAFFIC report highlighted significant discrepancies in import and export data for several CITES-listed timber species, and noted that some of these discrepancies are likely to be accounted for by unreported (intentional or unintentional) and therefore illegal trade (Chatham House/TRAFFIC 2014). Other reports in recent years have also highlighted lack of harmonization in import and export protocols around the globe that can obscure actual trade patterns (Chen 2010). Many CITES-listed timber species can be easily purchased in stores and via e-commerce sites in the US, typically with no assurance from companies selling these products that they are in compliance with CITES or other timber legality requirements.  The US Lacey Act was amended in 2008 to create civil and criminal penalties for importing illegally traded wood.  However, identifying and pursuing potential cases is expensive and time-consuming, particularly if only conventional data analysis technologies are available.  Harnessing the power of big data analytics could allow evaluation of data with a speed and accuracy as never before.   

Marine Fisheries

Illegal, unreported, and unregulated (IUU) fishing is a serious global problem that not only has significant environmental and economic consequences but has also been linked to other criminal activities including organized crime, drug smuggling, and human trafficking. IUU is estimated to account for 13-31% of global catch and contributes up to $23 billion in economic losses annually. It is estimated that between 20-32% of the wild caught seafood imported into the United States each year is illegally caught (Ganapathiraju et al. 2014), meaning that most consumers of seafood in the U.S. are unwittingly eating stolen fish. The Presidential Task Force on Combatting Illegal, Unreported, and Unregulated Fishing and Seafood Fraud was established in June 2014 to combat this problem and to stop illegally caught fish from entering the US market. Big data analytics could be applied to improve the effectiveness and efficiencies of enforcement efforts and identify suspicious areas within supply chains areas at high-risk for IUU, and to identify discrepancies between import and export data in need of deeper investigation.

Current Approaches

There are many current examples of how trade data and/or big data analytics are being used for conservation ends. To name a few:

• TRAFFIC’s international analysts regularly track information on illegal resource flows in wildlife trade monitoring databases. 
• USG enforcement agencies such as the U.S. Immigration and Customs Enforcement (ICE) have effectively used advanced trade data analysis for trade fraud cases including “Honeygate”.
• USDA’s Animal Plant Health Inspection Service is considering ways to invest more in technology advances.
• Global Fishing Watch, a technology partnership between Oceana, Google, and SkyTruth, aggregates and analyzes data from Automated Identification Systems (AIS) -- automated tracking used on ships and vessel traffic services used for identifying and locating vessels — to identify patterns of suspicious fishing activity.
• Washington, DC-based non-profit C4ADS recently used Palantir software to conduct big data analysis of disparate data sets to identify elephant poaching and ivory trafficking along with key transport routes and links to transnational organized crime.
• Big data analysis is incorporated into WRI’s Global Forest Watch, an online forest monitoring and alert system that harnesses satellite technology, open data, cloud computing, and crowdsourcing to guarantee access to near-real-time and reliable information about forests, in partnership with many partner institutions.

A New Vision

Big data analytics needs to be increasingly applied to large amounts of publicly available wildlife, timber and fisheries data, in a way that allows the identification of potential illegalities at a speed and accuracy as never before.  Additionally, algorithms are needed that can scan data in an ongoing fashion to allow for modeling and predictive analysis, and provide information that can also be used to advocate for strengthened governance and global trade protocols.  The application of big data analytics can help advance conservation by yielding more cases that level the playing field for responsible companies, and reduces illegal trafficking of wildlife, seafood and timber. It can also spotlight overlaps between these illegal trades and other illegal trafficking, and incentivize more US companies to practice due care and responsible sourcing. Through increased networking and information exchange across sectors, including via the Data Science group, we hope to pick up the pace.

1. Chatham House/TRAFFIC. An Analysis of Trade in Five CITES-listed Taxa. May 2014. Available at: http://www.chathamhouse.org/publications/papers/view/199351.

2. Chen, H.K. 2010. Lost in Transit: Export and Import protocols as contributors to discrepancies in international timber trade data. ASEAN REFOP policy paper.

3. Ganapathiraju P, Nakamura K, Pitcher T, Delagran L. 2014. Estimates of illegal and unreported fish in seafood imports to the USA. Marine Policy 48. 102-113. Available at: http://dx.doi.org/10.1016/j.marpol.2014.03.019

4. Lawson, Sam. 2015. The Lacey Act’s Effectiveness in Reducing Illegal Wood Imports.  Union of Concerned Scientists.  15 p.  Available at: http://www.ucsusa.org/sites/default/files/attach/2015/10/ucs-lacey-report-2015.pdf

5. Nellemann, C. INTERPOL Environmental Crime Programme (eds). 2012. Green Carbon, Black Trade: Illegal Logging, Tax Fraud and Laundering in the Worlds Tropical Forests. A Rapid Response Assessment. United Nations Environment Programme, GRID-Arendal.  Available at: http://www.unep.org/publications/contents/pub_details_search.asp?ID=6276 

6. The White House. 2015. Big Data: Siezing Opportunities, Preserving Values. Available at: https://www.whitehouse.gov/sites/default/files/docs/20150204_Big_Data_Seizing_Opportunities_Preserving_Values_Memo.pdf

7. The White House. 2014. National Strategy for Combating Wildlife Trafficking. Available at: https://www.whitehouse.gov/sites/default/files/docs/nationalstrategywildlifetrafficking.pdf

8. TRAFFIC. 2013. Background report on illegal trade in elephant, rhino, big cats and pangolins. Available at: http://www.traffic.org/mammals/  

9. TRAFFIC. 2014. Seizures of Tortoises and Freshwater Turtles in Thailand, 2008-2013. Available at: www.traffic.org/species-reports/traffic_species_reptiles38.pdf

10. US Fish & Wildlife Service Law Enforcement Management Information System. Available at: http://www.fws.gov/le/