In Anti-Money Laundering, Global RADAR, Political Corruption, Trending

Artificial Intelligence Making Waves in AML

It seems like just yesterday that many an organization’s compliance responsibilities involved delegating a significant amount of time and manpower to manually monitoring transactions for suspicious activity. If something suspicious was discovered, more time would then be wasted on notifying other compliance team members, creating a case to manage the investigation into the activity, and ultimately creating a suspicious activity report and subsequently filing it. This process was riddled with issues; inconsistencies in findings from person to person, a limited capacity for reviewing transactions depending upon the manpower involved, and greater expenses that a financial institution was forced to pay to maintain this process in their attempts to remain complaint – making this system basically unsustainable for most businesses.

While this practice had the potential to be somewhat effective for small-scale institutions, it seemed that the greater the number of transactions that needed to be sorted through, the greater the room for error. In most cases, these manual errors were the reason behind numerous large compliance-related fines issued between 2008-2015. In fact, “global banks had paid approximately $162.2bn in fines and legal settlements with US regulators” within that time period (Stabe & Stanley, 2015). Of late however, fines for AML non-compliance have drastically decreased as the movement towards automating previously manual processes has increased. Coupled with automation is the influence of new, developing technologies that have made the once-daunting tasks involved with manual compliance easier to handle than ever before. Coincidence? I think not.

The compliance landscape has changed dramatically overall over the last several years, specifically as financial institutions around the world have rushed to capitalize on and incorporate artificial intelligence (AI) into their every day tasks. It is clear that artificial intelligence and machine learning has had a sizable impact on increasing the efficiency of transaction monitoring processes, but AI has also had a large impact on data management, client onboarding and customer due diligence (CDD) as well. The article “Artificial intelligence can cut money laundering’s ‘acceptable losses’”, cited in BSA News Now on Monday, April 24th, 2017, examined several benefits of artificial intelligence usage in anti-money laundering. The article writes that what makes AI so valuable to financial institutions of all sizes is in its ability to “efficiently identify all money laundering transactions, regardless of the dollar amount”, cutting down on what many in the financial services sector has described as the “acceptable costs” of money laundering (McLaughlin, 2017). This concept involves a risk based approach which defines the amount of money laundering that is “deemed acceptable” by an organization. This amount is generally the byproduct of dollar thresholds set for transaction monitoring, where an organization screens all transactions above said threshold. Below the threshold line, organizations have been found to accept some level of money laundering, terrorist financing, and other financial crimes. “The percentage that is allowed to pass unscreened is determined by institutions and federal regulators that work with them to ensure that they comply with AML regulatory guidelines” (McLaughlin, 2017).

While the above the line, below the line (ATLBTL) practice was common in recent years, it has begun to draw to a close with the rise of artificial intelligence, which has increased screening efficiency to the point that compromises like ATLBTL no longer need to be made. Financial institutions have also found that “AI can also allow prior knowledge and rules as well as updated rules to be incorporated into the investigations process” (Kentouris, 2016). By learning from predefined rules and use of past investigation alerts, AI has the ability to develop solutions quickly for resolving new cases. This leads to far more efficient data analysis, which ultimately aids in the identification of patterns and behaviors that can be useful to those analyzing individual transactions or client accounts. The benefits of incorporating technology have been profound, and have had a direct correlation to increased financial security and the downslope of regulatory fines seen between 2015 and 2017. Even so, many believe that we are just scratching the surface on how transformative (in a positive manner) artificial intelligence can be for those in the financial realm.

When utilized alongside AML software systems, artificial intelligence has the power to increase efficiency not only within compliance departments, but also within organizations altogether. Relentless monitoring that cuts no corners has and will continue to lead to a reduction of false positives, as well as a reduction of risk for FI’s around the world. It is safe to say that as technology continues to advance, artificial intelligence within the financial services sector will grow from a helpful tool into a necessity in the coming years.
Weekly Roundup

AIB Hit With Million-Dollar Fine

Earlier this week, Allied Irish Banks Plc. (AIB) was issued a fine in excess of 2 million euros (approximately 2.275 million USD) by the Irish Central Bank due to multiple compliance failures discovered between 2010 and 2014. These failures included issues in the reporting of suspicious transactions to authorities and other breaches of anti-money laundering and terrorist financing laws. The Irish Central Bank is said to have also discovered failures in proper customer due diligence procedures by the bank, as well as “breaches regarding AIB’s policies and procedures concerning anti-money laundering and anti-terror financing in “a number of areas,” including its trade finance business” (Flanagan, 2017).

The fine is the largest ever brought against AIB. A statement made by the director of enforcement for the Irish Central Bank, Derville Rowland, was critical of the lack of the communication between AIB’s senior management and its compliance officials, stating that “It was particularly concerning that sufficient resources were not applied promptly to investigate a substantial backlog of alerts of potentially suspicious activity” (Hancock, 2017). AIB has since added a  “robust” AML program to their compliance repertoire; one which representatives believe will resolve the majority of the issues the company faced previously.

Money Laundering Charges Against Haitian Parliament Member

On Monday, a man elected with the purpose of potentially helping to lead Haiti out of rough economic times pleaded guilty to conspiring to launder drug money. After recently gaining election to Haitian parliament, Guy Philippe was arrested on United States drug charges just days before he was set to be sworn in as Senator in early January of 2017. The timing of the arrest was perfect; had Philippe been officially sworn in, he would have gained immunity from federal prosecution. The charges for which Philippe was apprehended date back to 2005.

Philippe pleaded guilty to “abusing his position as a high-ranking police officer to protect narcotics shipments headed to the US between 1999 and 2003” (AFP, 2017). He also admitted to taking bribes from drug traffickers that netted him several million dollars – some of which was used to pay members of Haitian police and security to “ensure their cooperation.” Additionally, Philippe admitted to playing an active role in an operation that brought drugs, including cocaine, to Miami, Florida. He faces up to 20 years in prison, and his sentencing is set for July 5th, 2017.

Bitcoin Gaining Legal Status in Florida?

After quickly passing through a primary committee vote, H.B. 1379 – a bill seeking to amend the state of Florida’s money laundering statute on bitcoin and virtual currencies – will be presented to the House floor in the coming weeks. If the bill passes, bitcoin will be “defined as a “medium of exchange in electronic or digital format that is not a coin or currency of the United States or any other country” (Spahr LLP, 2017).

The move to enact the bill comes just months after a Florida judge dismissed criminal charges against a Miami resident that involved the illegal transmission and laundering of bitcoin funds. The judge’s ruling was due in large part to her belief that bitcoin was not the equivalent of money, meaning the case would not fall under Florida’s money laundering and money services statutes. In a statement, the judge from the aforementioned trial stated “the currency’s limited acceptance and use of exchange, high volatility and lack of a central authority indicate that it does not represent “tangible wealth,” or money” (Spahr LLP, 2017).

Due to the lack of a consensus agreement on whether or not bitcoin is equal to money, a national trend has emerged to define the legal status of virtual currencies state-by-state. Most would agree that this latest bill would undoubtedly have an impact on the future of how bitcoin and other forms of virtual currency are managed and defined by AML laws and regulations.

 

Citations

AFP News. “Former Haiti Coup Leader Admits Drug-money Laundering Charge.” Yahoo! News. Yahoo!, 24 Apr. 2017. Web. 

Ballard Spahr LLP. “Florida Lawmakers Seek to Bring Virtual Currency into the Fold.” JD Supra. 26 Apr. 2017. Web. 

Flanagan, Peter. “AIB Slapped With Fine for Compliance Failures as IPO Looms.” Bloomberg.com. Bloomberg, 25 Apr. 2017. Web. 

Hancock, Ciarán. “AIB Fined €2.3m for Breaching Money-laundering Rules.” The Irish Times. The Irish Times, 26 Apr. 2017. Web.

Kentouris, Chris. “Artificial Intelligence: The Next Frontier in AML Compliance.” FinOps.  08 Sept. 2016. Web.

McLaughlin, David. “Artificial Intelligence Can Cut Money Laundering’s ‘acceptable Losses’.” PaymentsSource. 21 Apr. 2017. Web.

Stabe, Martin, and Aaron Stanley. “Bank Fines: Get the Data.” Financial Times. 22 July 2015. Web.

 

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