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We’ve noticed that despite our target audience being the financial compliance industry, when we post with a heading to attract criminal attention, it gets read more!
So welcome to this post. In it we will discuss the steps that criminals take to use technology to hide their assets.
Firstly, a basic understanding of Placement, Layering and Integration are needed, you can find that here if you do not understand these three steps.
In the main, technology aids steps two and three. It helps criminals to layer their asset with repeated levels of trade of one sort or another. It then helps them to ‘cash out’ in the integration stage.
We do not make a distinction between a money launderer (professional) or a criminal (commits the predicate offence) other than simply. Similarly, with something like 150 definitions of an organised criminal or their enterprise, this post sets out to use the terms colloquially, inasmuch as any one of the three can act in any capacity so it’s fair to call them all criminals.
Europol define organised crime, not on a personal level as to state the activity of a single individual, but as to the commodities, groups and hubs that make up an OCG (Organised Criminal Gang). In the UK, the definition (loosely termed) relates to activity of a group rather than an individual or specific crime type. It more relates to criminals acting as groups at level two and above (above ‘street’ level crime and cross regional/international).
Money laundering is characterised as a secondary offence to the predicate offence that generated the illicit funds. It is a dynamic and flexible process with launderers actively adapting to fluctuating situational conditions. It is these dynamics that make it difficult to define, describe and prosecute. The levels of convictions are pitifully low globally and this is one area FATF are starting to push with the move to ‘Outcome’ based mutual evaluations at the national level.
As in our post describing the links between Nations that are high on the Corruption index and Bitcoin pricing, the links between laundering and regions that have lax controls and high levels of corruption are sought out by organised criminal groups to layer their transactions to conceal the real origin of the revenue. A research paper into Money Laundering has described it as the third largest industry globally, only oil and agriculture generate more revenue, this indicates just how professional the ‘sector’ is and the sometimes insurmountable odds the industry, regulators and prosecutors face.
Indeed it is these odds, and a nascent belief that the financial sector is inextricably and on occasion corruptly linked to OCGs that is forcing the political agenda to get tougher with sanctions and fines for firms that transgress and get caught. Certainly there are more huge fines than there are ‘bellies against the charge desk’ as I used to say in a former career.
I am going to deliberately steer away from the more obvious routes to launder money. This site covers the more obvious routes in other posts (search for money laundering in our search box and posts will come up). Instead I hope to show you more nuanced methods that a ‘professional’ launderer would use and hopefully point to ways for firms to identify those efforts.
It is obvious that the nearer the point to the actual criminal predicate offence, the more risk there is to laundering cash. The further the cash is layered, actually or digitally, the harder it is for an investigator to track the asset backwards. This is especially true of asset that moves across borders. It is also evident that the regulated sector, spends most of its time trying to detect this first placement into the system. Defeat the placement stage and the professional launderer has the odds stacked in his favour.
The key to laundering is therefore, the successful first step. To conceal it the launderer needs a Trojan horse. Digital transactions and cryptography along with ‘straw-men’ hide the identity of the original criminal. Again, in nations that have a large populous of poor communities, people are prepared to have their identity used as a placement vehicle; this influences the regional choices of OCGs. There are also cases showing more ‘middle class’ white collar workers prepared to lend their identities to support business transactions involving laundering transactions further into the layering process – this includes allowing legitimate business be used to clean money. It is these reasons that money laundering causes capital flight from an economy, investors fearful of the instability and risk attached to the market.
The Trojan Horse
A simple use of technology, a photocopier. Used in high value sales around the globe to prove the outlet checked the identity of the person purchasing- especially when paying in cash. It is a simple task to obscure the image of a passport, make it too dark or otherwise unidentifiable to help the launderer evade any future scrutiny. Purchasing gold, jewels and other high value goods.
Intelligent Deposit Machines
These machines were used to great effect in Australia where a gang used them to launder tens of millions of AUD. Enabling placement and movement of cash through the machines even when the banks were closed. The gang used a network of the machines at lower than threshold amounts to avoid suspicion.
e-commerce and mobile payments
OCGs setting up online storefronts that trade in transactions and not goods to help the launder layer his transactions in what appear genuine stores. No goods are actually moved. This problem is set to get worse as more fintechs operate in this space with a ‘less than conservative approach’ to regulations. One that shall remain unnamed even switched off its transaction monitoring for several months because it was alerting too much (!!)
An International internet payment provider was suspected of laundering on an industrial scale. Implicated were digital currency exchanges, precious metal dealers and more. The OCG had effectively infiltrated the entire system to move large sums in apparent legitimate trade.
An OCG used online betting and internet payment system to launder the proceeds of narcotic dealings. The gang used the two services to receive transactions and then move the funds offshore. In an investigation, it was found that two of the enterprises had the same registered physical address (our intelligence system would flag this as the transaction processed – even across institutional business lines). The gang transferred revenue acting as a remittance service and this disguised the origin of the transaction when the bank conducted the transactions. The gang also used the accounts to simply store funds, making passwords widely known so multiple members could draw on the accounts.
Sales Registers and CCTV
A nightclub had CCTV all over its premise except in the VIP lounge for ‘privacy reasons’. That lounge was then used to till up huge sums of cash transactions as ‘revellers’ purchased $800 champagne in huge quantities. Not so elaborate but an easy way to place cash into the legitimate system through bogus purchases of high value goods/services.
An old colleague of mine filmed a car wash over aperiod of several months to prove vehicles were indeed never washed there, helping to secure the conviction for the predicate offence and ML.
Elaborate ways to facilitate the first stage of money laundering are constantly evolving. As we have stated this is the most risky part of laundering. Not only because it’s the first and easy to trace back to the predicate offence, but also because policy focuses compliance agent attention on this first stage through transaction monitoring and identity checks.
It is also likely the algorithms being adopted through machine learning technology are focusing on this ‘thick edge’ of transactions. At the front of the process. The more involved and professional launderer will adopt moving and flexible processes to move the money many times over, using no set pattern and through many jurisdictions, transaction types and institutes. This way, unless the whole financial sector had one Ai system, the individual systems in each firm will fail to spot the patterns of transactions/behaviour.
Other technologies are also being used by OCGs. From encrypted communications to virtual services and products and the darknet facilitating on a huge scale the market-place for many OCGs and their gang members.
It is safe to say that criminal gangs have business processes very similar to legitimate businesses. They use highly skilled financiers and skilled business people to move money and assets around as we have discussed previously in our post about cyber crime and money laundering.
This post only details a few examples of ‘how’ criminals use technology. It explains ‘why’ they use it. There is a much bigger study currently ongoing to identify the methods regulators, policy-makers and the industry can use to identify how to combat the professional OCG by understanding their methods. Unfortunately, my experience in these matters tends to lean me towards the opinion that this will be mostly wasted effort. The reason I say this is because;
- OCGs will know the models adopted before they are launched – they will almost certainly have informed people on the inside.
- The policy-setters will take far too long through bureaucratic channels to put something live.
- OCGs will adapt and change and this requires not a look to the past but an accurate prediction of the future to enable swift machine learning from new sets of learning data.
Here at CYW we are developing a networked system that will, for the first time, link the institutes intelligence together. With over 300 metrics to monitor from outside the sector, we will merge data and intelligence to pass it at the speed of transactions to enhance red flags and reduce false positives.
This is essentially providing public level enforcement typologies to the private sector to provide more effective and efficient means to stop transactions and put the onus on the criminal to prove it is innocent. It is our view this is the only way the industry can proceed to force change, cause reductions in criminal use of the financial system and increase the amount of seizures being made. stand alone transaction modelling, even with machine learning functionality, will be hampered by the lack of full data as money moves outside their dataset and comes back in again through different entitites/routes. The firm would have no way of tracking it outside of their own institute.
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