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The top 3 crimes driving money laundering

We explore 3 predicate crimes for money laundering and explain how their perpetrators can exploit the financial system to launder their illicit funds.

Napier AI
January 11, 2022

In this blog, we will explore three different predicate crimes for money laundering and explain the ways in which they’re connected by their links with organised crime groups.

The crimes behind the crime

Predicate offences are defined as any offence which results in proceeds being generated that may become the subject of an offence as defined in this UNDOC convention.

Criminals that commit predicate offences are highly motivated to maximise profits, hence the premise behind anti-money laundering (AML) measures is to detect these criminals through analysing their financial behaviour and interactions with the legitimate economy – namely financial institutions.  

Detecting transactions which point to suspected money laundering activity can provide law enforcement with the intelligence to combat financial crime and unearth bad actors perpetrating predicate crimes.

According to the Financial Action Task Force (FATF), what constitutes a predicate offence is the responsibility of individual countries to specify.  

In some jurisdictions, such as the UK, predicate offences are defined on an ‘all crimes’ basis, which means that money laundering offences are not restricted to a list of defined predicate crimes. Elsewhere, in jurisdictions such as the European Union and the US, predicate crimes are prescribed in law.

For example, the EU’s Sixth Anti-Money Laundering Directive lists 22 predicate offences including human trafficking and migrant smuggling, environmental crime, tax crime, cybercrime, fraud, corruption, participating in organised crime groups, and racketeering.  

In this blog, we take a closer look at three of these predicate offences - human trafficking, illegal wildlife trading, and corruption. Each has a different but equally devastating societal impact:

1. Illegal wildlife trafficking

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 Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) defines wildlife crime as ‘the taking, trading (supplying, selling or trafficking) importing, exporting, processing, possessing, obtaining and consumption of wild flora and contravention of national or international law’. Over 37,000 species of wildlife are covered by CITES, including those threatened with extinction or overexploitation.  

Estimates of the proceeds of IWT are difficult to arrive at, but a recent report by the World Bank places it in the region of $7-23bn per year.

FATF’s 2020 report ‘Money Laundering and the Illegal Wildlife Trade,’ is its first global report on IWT, which it considers to be a ‘major transnational organised crime that fuels corruption, threatens biodiversity and can have significant public health impacts’.

In 2021, the UN adopted a resolution to combat IWT, which requires Member States to treat IWT as a predicate crime under domestic money laundering offences.

Additionally, the Egmont Group (a united body of 167 financial intelligence units) has also emphasised the importance of ‘following the money’ when it comes to IWT.

Understanding IWT 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 this crime.

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 now include 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.

2. Human trafficking

The most recent estimate cites 40.3 million victims of modern slavery - men, women and children trafficked for forced labour, including sexual exploitation and forced marriage.  

In 2018, UNODC estimated that 50% of victims were trafficked for sexual exploitation, 38% for forced labour and the remainder for other forms of exploitation.  

With the profits from this horrendous crime averaging $150bn 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, 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% of all victims of human trafficking.

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.

3. Corruption

The UN considers corruption to be one of the main obstacles to achieving its 2030 Sustainable Development Goals and it further estimates that corruption costs world governments over $3 trillion  annually, $1 trillion dollars of which are paid in bribes - the remainder is stolen.  

In recent years, there has been an increasing focus on the crime of ‘kleptocracy,’ a specific type of corruption which occurs when (often authoritarian) state leaders steal large sums from public coffers.  

Kleptocrats have become adept at hiding their ill-gotten funds through global webs of shell companies in offshore locations and through other established methods, such as large real estate transactions, with the associated money trails often ending up in the largest financial centres in the world such as London and New York.  

High-profile leaks such as the Panama Papers in 2016, the Paradise Papers in 2017, the FinCEN Files in 2020, and the Pandora Papers in 2021 have shed light on the scale and sophistication of corrupt methods used to hide dirty money, as well as some of the elaborate schemes used to avoid tax and work around international sanctions.

In June 2021, the UN held a special General Assembly Session against corruption and introduced a political declaration outlining its commitments to tackling corruption.

AML legislative frameworks have also been tightened up in recent years - partly in response to the leaks mentioned above - with stricter rules introduced around the identification of ultimate beneficial owners (UBOs), namely the 5th and 6th Anti-Money Laundering Directives in the EU and the Anti-Money Laundering Act of 2020 in the US.  

Preventative controls revolve around identifying customers who may be at risk of corruption - politicians and their associates - usually referred to as Politically Exposed Persons (PEPs). Screening against established lists of ‘names’ and then applying higher standards of due diligence where PEPs are identified is a standard part of the customer onboarding process.

This article is taken from our recent eBook. To discover more about predicate crimes, financial crime typologies and the role of technology, download it here.

How are predicate crimes of money laundering linked?

Discussing these three types of predicate crime separately also 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 any risk of exposure. For example, research has shown that IWT and corruption often go hand-in-hand.  

"Very rarely does an organised crime group focus on only one type of offence"

Research on the problems and solutions to IWT, even the types and methods, through the lens of anticorruption is in short supply. The gap in knowledge urgently needs addressing, as IWT is driven by corruption as for trafficking networks to be able to move their illicit goods and for IWT to thrive, it’s necessary for the perpetrators 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.


How can technology help?

AML measures exist to detect criminal activity, analyse suspicious financial behaviour, and help law enforcement to bring the perpetrators of these crimes to justice.  

But the job of compliance officers and financial institutions is never-ending as there’s no end to the ambition and creativity of criminals intent on maximising profit, and their methods of gaining and hiding illicit money are ever evolving in sophistication.  

Global advancements in technology make our day-to-day lives easier, but they also open up new avenues for money laundering for criminals – new avenues that AML processes need to keep up with to be effective.

AI is well-suited to applications such as AML, and can help to detect criminal typologies as they evolve, and even anticipate emerging patterns of criminal behaviour in a way that rules-based systems currently can’t. AI also increases the efficiency and accuracy of AML processes such as entity and payments screening, by intelligently discounting the overwhelming volumes of false positives generated.

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