Using big data techniquesto monitor corruption risks in publicly available information: A technical guide

Harnessing this technology will help law-enforcement teams globally prevent and disrupt illegal wildlife trafficking – one of the most profitable criminal activities worldwide. 

In its new guide, Using Big Data Techniques to Monitor Corruption Risks in Publicly Available Information, TRAFFIC has applied its expertise as a world leader in monitoring illegal trade in wild species, bringing together multiple data sources. 

Using big data techniques makes it possible to zoom in on connections within a supply chain where bribery or corruption are likely to be taking place. This gives global law-enforcement agencies a head-start on tackling criminal networks profiteering at the expense of local communities and wild species.”

Gabriel Šípoš, TRAFFIC Anti-Corruption Coordinator and Guideline co-author.  

Big data, a term referring to research involving larger, more complex data sets, allows researchers to filter vast amounts of information in manageable portions. These data sets are so voluminous that traditional data processing software just can’t manage them. Typically, big data techniques have been used by financial institutions to uncover different types of corruption, but TRAFFIC is using these to unearth corruption in wildlife supply chains.  

By combining information from existing databases (such as forestry registers), using web scrapers (that automatically copy data from the internet), and harnessing the processing power of new software to analyse information, it is possible to spot professional and personal connections between people who are likely to be the cause of criminal activity. 

“These techniques can take our research to the next level. Being able to access and make sense of the sheer volume of knowledge that these large datasets can provide will revolutionise our work and the work of NGOs, law enforcement, and other governing bodies” said Antony Bagott.

By using these techniques, we’re able to gather and process data that can unearth the complicated web of people potentially linked to illegal wildlife trade networks, and help authorities build evidence-based cases against the people profiting from these supply chains.” 

Antony Bagott, TRAFFIC Data Manager and Guideline co-author.  

Where data on conservation decisions and the movement of money are publicly available, the media and NGOs have more opportunity to scrutinise politicians’ potential conflicts of interest. 

For example, more accessible data can illuminate ‘Politically Exposed Persons’ (PEPs) within supply chains – people appointed to high-profile positions with influence (such as government officials) who can be more susceptible to bribery and corruption. Focussing on PEPs can help identify areas where bribery and corruption risks are high, where proceeds may be laundered, and where networks are involved in the illegal wildlife trade. 

Once all the data is harvested, it is put through a corruption risk model to identify the likelihood of corruption. Typically, this has been used by financial institutions to uncover corruption, but TRAFFIC is using these to unearth corruption in wildlife supply chains.  

Applying to wild species trade Nature Crimes 

Nature crimes, which include illegal forms of logging, mining, wild species trade, land conversion, and activities associated with fishing, are driving environmental degradation and biodiversity loss, devastating local communities, fuelling financial crime and corruption, and challenging the rule of law.  

“We have laws for conservation already in place, yet corruption leads to them being ignored, or if caught, for perpetrators to avoid prosecution. Most experts consider corruption to be the key enabler of nature crimes, more so that the most biodiverse countries tend to suffer from high levels of corruption as well,” concluded Gabriel Šípoš.