However, as these unsung heroes work tirelessly to stay ahead of the game, so do determined criminals. Financial criminals are constantly switching up their techniques for laundering illicit funds and continue to become an increasingly sophisticated threat – and now this threat has spread to consumers favorite online retail giants. Global law enforcement authorities have uncovered scandalizing links between the crime of counterfeit goods and other serious offenses, particularly money laundering.
According to the International Trademark Association, counterfeit goods are a $460 billion industry, with most of these goods sold and purchased online through shopping favorites such as Amazon, eBay and Overstock.com. As of 2016, it was estimated Amazon completes as many as 636 transactions per second. The sheer volume of online retail transactions highlights the great potential for money laundering.
The trafficking of counterfeit goods provides criminals a corresponding source of income and an outlet for laundering their money. Often, criminals will feed fake goods into the legitimate supply chain, providing them with ‘clean’ money.
This sophisticated technique is just one example of trade-based money laundering (TBML) which allows criminals to funnel the illicit revenue earned from the sale of counterfeit goods and use them to fund further production of fake goods and other heinous crimes. If only 10% of online transactions were considered illicit, this would still reflect billions of dollars’ worth of product that could have been stolen and sold online, phantom transactions for nonexistent goods, or the goods themselves that could be counterfeit.
The expanding trend of fraudulent transactions taking place through online retailers is known as ghost laundering. Ghost laundering is the multi-faceted process of leveraging true or passive merchants to appear to sell items which are counterfeit, valueless or entirely nonexistent. Ghost laundering combines the inability to detect phantom sales (transactions where no actual or legitimate goods are sold) with the diversity of merchant typologies and vendors.
For instance, in exchange for the sales of prostitution (human trafficking) or street drugs, a portion of the debt owed could be repaid through a one-time purchase or a series of purchases through an online vendor. A classic vinyl record, first edition book or some other rare commodity could be listed online at a falsified price of anywhere from $1-$1,000 and repeatedly sold, allowing money launderers and fraudsters to pay their debt.
Ghost laundering isn’t limited to online retailers. Service providers such as Uber, Lyft and Airbnb run the risk of having their platforms extorted for this kind of financial criminal behavior as well.
So where does this leave investigative teams?
Financial criminals have perfected their ability to go undetected within our global banking system and have familiarized themselves with the processes and legacy technology, such as transaction monitoring systems (TMS), utilized by compliance teams and AML investigators to thwart criminal efforts.
Smart money launderers know the rules commonly programmed into TMS and therefore know how to avoid them. In fact, an estimated 50 percent of financial crimes trafficked through the banking system pass through TMS undetected. Furthermore, statistics suggest that a majority of TMS alerts, industry-wide, are false positives which only further hold up crucial investigations.
Advancements in data science such as artificial intelligence (AI) and machine learning were born to solve this problem. AI systems have progressed to the point where large volumes of transactional and other sources of financial data can be culled, consolidated, analyzed, and scored for risk so that investigators can make more accurate determinations of suspicious activity.
With AI-powered solutions AML investigators can analyze more in-depth critical typologies and points of comparison such as:
- Examine the volume of online sales or services within high-risk/high-crime jurisdictions while zeroing in on specific merchant types listed in geographic targeting orders
- Review personal accounts for a great number of merchant/vendor transactions crediting the account for supposed sales with no apparent business purpose
- Multiple concurrent transactions across or within the same jurisdiction within a narrow time frame
- Multiple businesses with similar, iterative variations of identifiers such as sequential tax identification numbers, or iterations of the business owners name
Money laundering is not a victimless crime, and it continues to evolve every day. However, AI-based solutions can easily analyze massive amounts of data, and quickly identify where exceptions or anomalies exist, unveiling sophisticated intelligence networks which are utilized as a flow of communication between fellow fraudsters and money launderers.
When you stop the money, you stop the crime. And with the adoption of advanced AI and machine learning, decreasing global crime becomes a reality.
Report: How Financial Firms Are Using Artificial Intelligence and Machine Learning to Meet AML Demands of Today and Tomorrow
It’s a well-known fact that the global pandemic caused a radical shift in consumer banking and payments behavior. What isn’t as obvious is how financial institutions responded behind the scenes. Fortunately, a new study helps shed light on the pandemic’s impact on the adoption of new technologies for anti-money laundering (AML) efforts.
Regulators and those handling compliance at covered institutions have long accepted the pitiful state of AML program efficacy, including: An estimated $2 trillion laundered through the global banking system annually 90+% of false positives coming from transaction...
While not mandating that firms invest in technology to automate financial crime investigations, regulators are certainly encouraging it. They are noticing that advanced BSA/AML teams are using robotic process automation (RPA) bots to gather data for investigations. They are aware that those same firms are using machine learning to analyze huge data sets, identify patterns, and pinpoint where exceptions or anomalies exist.