Money laundering remains big business. The 2018 Basel AML Index report suggests that little real progress has been made in the seven years that the index has been published, despite ongoing local and international efforts – including FATF initiatives and the EU’s AMLD4 and AMLD5.
Estimates for the total amount of money laundered worldwide range from $800 billion to $2 trillion, and the countless financial institutions that play a role in the transaction chain are on the front line. Institutional awareness of anti-money laundering (AML) avoidance measures and ways to counter these measures is therefore essential.
Typical techniques used to bypass AML measures
Over the ages criminals have always been determined to find ways to counter crime prevention efforts, often relying on weakly applied measures or indeed ignorance so that their crimes can continue unimpeded. Money laundering is no different. The growing line-up of AML avoidance strategies include:
- Hiding money laundering transactions amongst the terabytes
The sheer numbers of electronic transactions executed every year is phenomenal, running into the hundreds of billions. SWIFT alone processes over 33 million international payments in just one month. Accurately and consistently applying AML measures to every transaction is difficult, to say the least.
A working money laundering strategy is to simply try and slip transactions past the AML net by hiding transactions amongst massive transaction volumes. Some transactions may get stopped, but unless a sophisticated automated AML system is in place criminals can expect to clear a good deal of their funds.
- Transacting where AML measures appear weak
Criminals may target nations with weak anti-money laundering measures for transaction processing. Institutions in developed economies tend to apply stricter AML measures which make laundering more difficult, while institutions in developing nations may be less adept at catching illicit funds.
The same applies to individual institutions: depending on their exact role in the transaction chain an institution may apply extremely strict AML measures, or may only touch on the most essentials points demanded by regulators. The latter group is a target for criminals.
- Rely on delayed transaction vetting
Manual analysis of transactions takes time. Tracing the source of funds, including the true beneficial owners of the parties to a transaction, can rarely be completed instantly. Instead, link tracing remains a painstaking process.
With electronic funds transfers completing at an ever-faster pace criminals can send funds and attempt to move the funds to the next destination before AML measures can take effect.
- Using layering and integration schemes
A classic money laundering technique, layering involves separating illicit gains from the source of these gains by creating layers of financial transactions which trips up efforts to audit laundered funds. Layering can involve bogus invoices, taking out loans or indeed paying back debts using illicit funds.
Further down the path, integration schemes try to “normalize” the funds, bringing the illicit gains back into the normal economy. This may involve buying into an ordinary company selling goods or performing services in the economy – normal, of course, aside from the fact that the company is capitalized by illicit funds.
- Trade-based money laundering (TBML)
Not dissimilar to layering schemes, criminals attempt to use international trade to hide illicit gains. This can include a mix of relatively subtle measures that in aggregate allows criminals to clear large sums.
For example, TBML can involve under and over-invoicing, where an exporter trades goods at a price that does not resemble the market rates. Criminals can also practice “phantom shipping” techniques, sending more goods or fewer goods than invoiced or falsely describe the goods being traded.
- Stripping payment transaction data
Money launderers can attempt to make the tracing of illicit funds more difficult by removing material information from the electronic payment instruction. This shields the true originators from scrutiny, making it more difficult to flag known bad sources of funds.
Sometimes staff at an institution must be complicit to enable stripping, at other times stripping may involve rogue actors trying to resend a slightly modified version of a payment message that was previously stopped by AML filters.
- Cryptocurrency and money laundering
As much as crypto-currencies espouse anonymity, the reality is that blockchain transactions are fully traceable because transaction data is usually immutably committed to the blockchain. That said, money laundering can take advantage of crypto-currencies by making use of exchanges that do not apply KYC on-boarding procedures.
Further crypto strategies used by money launderers include so-called bitcoin mixers which, though painstaking and costly to make use of, can be very effective. However, cryptocurrency is a high-risk strategy for money launderers who must make use of unpredictable partners, often on the dark web.
Key strategies for institutions serious about AML
Applying AML directives demands an encompassing, and increasingly automated approach. The fines for non-compliance are heavy, irrespective of actual complicity or indeed whether an institution simply dropped the ball for a moment.
- Advanced automation is critical
Automated screening tools have provided a solid supportive base for AML efforts for some time, sorting through billions of transactions and matching against watch lists in real time. However, without intelligent capabilities, AML tools are relatively blunt instruments that need to allow through an unacceptably large volume of illicit transactions in order to avoid triggering excessive false positives.
In contrast, automated tools that use artificial intelligence can work in a far more granular way, ranking alerts by degrees of severity so that transactions can be halted and manually investigated when needed – and only if truly warranted. It makes it tougher for criminals to rely on hiding amongst masses of transactions or hoping that institutions will be unresponsive.
Furthermore, intelligent automated tools can learn over time. Thanks to machine learning algorithms automated AML measures can ensure that real-time analysis catch even the latest money laundering methodologies. Intelligent automation detects new strategies on the fly and adds these methods to internal models for future reference.
- Trade-based screening
Advanced AML software can screen not just the exchange of money, but also the trade documents that match up to financial transactions. By doing so financial institutions can track suspicious trading behavior – catching out parties that are selling plastic pens for the price of a Swiss watch, for example.
The best solutions continues to screen transaction data throughout the process of completing a transaction. Doing so reduces the chances that a financial institution will facilitate a step in the transaction life-cycle where one of the parties are on a blacklist.
- Link analysis and deep investigation
Link analysis systems can build models that flesh out the relationship between the parties in a transaction, and indeed the networks that surround these parties. Money laundering networks involving layering and integrations schemes are notoriously complex, making manual analysis particularly time consuming.
Software that visualizes link analysis makes it more practical to determine ultimate beneficial ownership, enabling institutions to more easily comply with rules including the EU’s Statutory Instrument No. 560 of 2016. It may not be possible to investigate every transaction using link analysis, but an AI-driven alerting system will flag the transactions that warrant deeper investigation.
- Practice a broad AML strategy
Technology is a powerful tool in the AML arsenal, but successful anti money laundering measures require an institutional approach. A mix of a compliance-aware culture, ongoing training and highly diligent auditing is essential to catch money laundering in the act. That said, intelligent AML software significantly reduces resource-intensive tasks, allowing compliance officers to focus on higher-order AML efforts.
AML software inject actionable insights into compliance operations, by highlighting suspect cases using AI-driven analysis of transaction variables that may seem of no obvious relevance to humans. Nonetheless, there must be an institution-wide approach to minimizing risk, and staff must stay alert - advanced software does not absolve staff from AML duties.
AML is a complex realm, but your technology partner can help
Some of the AML measures we’ve suggested rely heavily on technology, but we’ve also pointed out how AML teams alongside an institution-wide approach play a core role in effectively combating money laundering.
In conclusion, AML measures require a mix of intelligent software tools operating in concert with a motivated team. EastNets have worked in the AML sphere for decades and offer several effective solutions, including en.SafeWatch Filtering for watch-list matching, en.SafeWatch Profiling to monitor client behavior and en.SafeWatch360 Transaction Risk Radar to flag real time transactions that require deeper investigation.
We understand both the technology and the institutional aspects of AML, and we know that financial institutions often feel that regulators have the upper hand, given the complexity of the task. Contact us to see how AML technology can take the pressure off your teams, helping your institution stays compliant.