When faced with the day-to-day complexities of navigating compliance regulations and processes, it can be easy to feel disconnected from the human suffering and harm associated with money laundering. Human trafficking and illegal wildlife trading are just two examples of the many crimes behind financial crime, also known as predicate crimes.
Here Julian Dixon, founder and CEO of the financial crime compliance technology specialist Napier, digs deeper into those offences and look at how technology and AI can be leveraged to improve the effectiveness of financial crime fighting efforts.
Dixon founded Napier in 2015 when he recognised that there was a need to replace existing slow and inefficient compliance systems with advanced technology that combines big data with AI to increase efficiency and combat financial crime.
Determined to create a world-leading technology solution to help compliance officers fight financial crime in an efficient way, Dixon has grown Napier from a start-up to a global company, with clients including Tier 1 banks, payment services companies, and blue-chip corporates.
With over 20 years of financial services experience gained at major investment banks including Deutsche Bank, JP Morgan and Commerzbank, Dixon has extensive knowledge of financial services processes and technology.
Illegal wildlife trading
Environmental crime, which includes the offence of illegal wildlife trading (IWT) has come to the fore as a significant predicate crime for money laundering in recent years. The trade in protected species of wildlife is the remit of organised criminal gangs, however it is seen by criminals as offering larger profits and lower penalties than other, more established types of criminal activity.
Estimates of the proceeds of IWT are difficult to arrive at, but a recent report by the World Bank places it somewhere in the region of $7-23billion per year. Similar to legitimate businesses, illegal wildlife traffickers make use of well-defined supply chains that can span several jurisdictions and comprise source, transit, and destination countries. Source countries are those where the wildlife originates, and it may be poached, killed, or procured from these countries. For example, Kenya and South Africa are both significant source countries for elephant ivory which is primarily destined for markets in Asia, with UNODC estimating that Vietnam, China, and Cambodia together make up 88 per cent of the destination market for ivory tusks.
Understanding these supply chains, which can vary species by species, and the types of actors involved in the process, is key in identifying the financial activities and behaviours that are indicative of IWT. According to the Financial Action Task Force (FATF), laundering of proceeds occurs at multiple points in the chain and, like other predicate crimes, multiple methods are used to clean the money. These include:
- Placing and layering of funds in the legitimate financial sector
- Concealing payments behind shell companies and co-mingling lawful and illicit funds
- Purchasing high value goods, including real estate and other luxury items
- Using money value transfer systems, such as Hawala
For financial institutions, the illegal wildlife traffickers’ use of the formal financial system is where they can most easily take action and where their regulatory obligations reside. Many financial institutions are now including IWT as a specific financial crime risk, but there are challenges associated with being able to distinguish behaviour that is related to IWT from that which may be indicative of other types of crime.
The most recent estimate cites 40.3 million victims of modern slavery. In 2018, UNODC estimated that 50 per cent of victims were trafficked for sexual exploitation, 38 per cent for forced labour and the remainder for other forms of exploitation. With FATF reporting that profits from this horrendous crime, averaging $150billion per year, organised gangs are expanding their portfolio of activities to include trafficking in human beings (THB) by taking advantage of global dislocation, making it one of the fastest growing global crime categories. As well as being a violation of human rights, the FATF describes human trafficking as ‘also one of the most significant generators of criminal proceeds in the world’.
Victims of THB are usually already victims of difficult circumstances, often coming from conflict-ridden and poverty-stricken regions. Undocumented migrants and children that have been abandoned or come from poor families are particularly vulnerable. Together, children and women comprise nearly 80 per cent of all victims of human trafficking.
Given the profitability of human trafficking for criminals, tackling the illicit flow of funds is a key tool in the overall strategy to reduce human trafficking and bring its perpetrators to justice. Unfortunately, global prosecution rates for human trafficking are low – and falling – with just 9,876 successful cases being brought in 2020 compared to 11,841 in 2019. This frustratingly low level of convictions can be attributed to an over-reliance on victim testimony, placing pressure on those who are already traumatised, vulnerable and potentially in fear of being deported if they are undocumented migrants. Human trafficking is consequently a low-risk, high profit crime.
Financial institutions play an important role in the disruption of THB by identifying human trafficking as a predicate crime with associated typologies and including these indicators in their customer due diligence and transaction monitoring activities. In this way, they can better identify the proceeds of crime related to THB. Financial institutions are additionally able to participate in the ‘identification, disruption and prosecution of THB cases as a result of their ability to analyse the associated money flows’, especially as part of complex financial investigations.
A tangled criminal web
Discussing examples of predicate crime separately risks glossing over some of the added complexities associated with the convergence of various types of predicate crime. Very rarely does an organised crime group focus on only one type of offence – they are more likely to have multiple revenue streams, which they can switch between should one stream face risk of exposure. For example, research has shown that IWT and corruption often go hand-in-hand.
Research on the problems and solutions to IWT through the lens of anticorruption is in short supply. The gap in knowledge urgently needs addressing, as IWT is driven by corruption as trafficking networks to be able to move their illicit goods and for IWT to thrive, the perpetrators need to build relationships with public officials through corrupt means such as bribery. Elsewhere, drug trafficking groups may also be implicated in human trafficking, utilising similar networks and channels to traffic both narcotics and people.
For both financial institutions and law enforcement, crime convergence makes it harder to make the links between money laundering red flags and predicate crimes, exacerbating the already challenging process of following the money. Traditional approaches to financial crime typologies, including the publication of red flags on a periodic basis, leave gaps that criminals take advantage of. However, technology and innovation can generate insights that help close these gaps, with the latest advances in artificial intelligence (AI) bringing new dynamism and flexibility to the fight against increasingly agile criminal elements.
Human trafficking, corruption, wildlife smuggling and cybercrime are the specialty of organised criminal gangs, who will stop at nothing in the pursuit of profit and have no qualms in exploiting the financial system to legitimise the ill-gotten proceeds of their crimes.
However, it is precisely by having a greater understanding of how these crimes manifest in patterns of money laundering behaviour (also known as typologies) that will improve risk identification and investigative activities. Insights provided through the smart utilisation of AI can make a significant contribution to identifying customer activity that is indicative of known financial crime typologies and can also help to surface emerging and previously unknown patterns.
To find out more about how AI can improve the detection of evolving typologies driving financial crime, read Napier’s eBook here.