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FinTech: Reinforcement for Banks’ AML (Anti-Money Laundering) Efforts

In today’s financial landscape, banks must adhere to strict regulations associated with Know Your Customer (KYC) and Anti-Money Laundering (AML) laws. Since the financial crisis in 2008, banks and financial institutions have been bombarded relentlessly with financial regulations intended to safeguard financial systems and customers, as well as increase transparency. If banks fail to comply with regulations, they risk paying enormous fees or legal penalties. Recently, the U.S. government determined, between 2009 and 2014, US Bank did not have adequate safeguards against money laundering. US Bank’s parent company U.S. Bancorp was ordered to pay the federal government $613 million to settle the criminal charges.

As banks look for ways to meet the challenges of complying with KYC and AML laws, it’s important to consider the rise of financial technology (FinTech) and how it has the potential to aid their anti-money laundering efforts.

Banks are required by regulators to monitor, assess and report suspected money laundering. But the current systems they have in place are not sophisticated enough to accurately determine illegal activity from legitimate transactions. Their systems are limited to processing a small sample of data and are unable to see the entire picture or make all the connections.

Criminals get around banks’ systems and filter their illegal profits into the legitimate banking systems by creating a complex chain of accounts and transfers. Large cash sums are broken up into small cash deposits made by different customers – also known as ‘smurfing’ – in order to stay below the reporting threshold of $10,000. By doing this, they’re able to turn illegal assets into legal ones. Making it nearly impossible to distinguish dirty money from clean.

One of the challenges banks face is inadequate AML systems which produce a large number of false alerts. Because of the limited amount of data available to process, current AML systems have trouble accurately distinguishing real criminal activity from legitimate transactions. Regulators require banks to investigate every alert generated by a bank’s AML system. Since many of these alerts turn out to be legitimate activity, it results in wasted time and resources investigating false alerts. It also means the investigators are less likely to recognize and reduce real criminal activity.

As banks look toward the future, digital currency also brings its own set of challenges. Digital currency has a high risk of money laundering and terrorist funding due to its high degree of anonymity and ease for cross-border transactions. The Australian government has decided to regulate their Digital Currency Exchange in order to help fight money laundering in the digital space. The DCE must report to the Australian Transactions Reports and Analysis Center (AUSTRAC) social media identifiers, unique device identifiers, and the digital wallet address of its customers. This removes the anonymity of digital currency, allowing the AUSTRAC to monitor suspicious transactions and trace it back to an individual’s digital wallet. 

 

As technology advances and evolves so do criminals. But FinTech has the potential to help banks stay a step ahead. Whether tracking digital currency, connecting data, or machine learning, more powerful computer systems combined with advances in technology, is opening up new possibilities in the fight against money laundering.

With the circulation of digital currency on the rise, so is the concern for how to regulate it. Blockchain technology may hold the key to tracking digital currency. Blockchain is a public distribution ledger that records transactions which are then independently verified. Once a payment is confirmed, it can’t be altered. This makes every payment to a digital wallet traceable and reliable. It’s possible that regulators could require verified wallets to be KYC and AML compliant. Customers would enter all their personal information as a ‘block’. That information would be encrypted and stored on the blockchain. A customer would then be given a password to access the information. If that customer wants to enter into a business relationship with a financial institution, they would provide their password, allowing the financial institution access to their personal information. The financial institution now has access to the transaction history of the customer’s digital currency and can verify that customer’s identity.

Another FinTech solution that has great potential to help fight money laundering is machine learning. Machine learning uses algorithms, rather than a set of explicitly programmed instructions, in order to analyze information, make decisions, and learn from those decisions. Over time, machine learning then modifies its own code, without human oversight, so it can make better, faster, and more accurate decisions. Machine learning technology can review verified money laundering and identify often obscure predictive variables that go unnoticed by data scientists. Thanks to huge amounts of data and stronger computing power, algorithms can detect subtle patterns of criminal activity. By implementing machine learning technology, banks can reduce the number of false alerts, allowing investigators to spend more time reviewing high-risk cases.

When banks invest in a strategic approach versus a reactive one, they can reduce their risk and benefit the most from anti-money laundering FinTech solutions. The first thing for banks to consider is evaluating their entire KYC and AML process, from start to finish. When starting a business relationship with a new customer, how banks onboard and verify their identity sets the foundation for their entire AML process. Ensuring individuals are screened through the right database to identify any links to criminals, terrorist groups, public figures, or politicians is crucial in the defense against money laundering. Next, banks need to consider how to connect all of their customer information to their KYC and AML data to screen for anomalies or suspicious activity. One FinTech solution to consider is machine learning, which can connect the dots between individuals to customer investments, associated entities and individuals, and sanctioned data. Machine learning can detect patterns often missed by data scientist when looking at a bigger picture, resulting in fewer false alerts. As banks rely more on technology in their daily operations, it’s easy to see how FinTech fits into their anti-money laundering processes. The benefits of FinTech solutions are beginning to outweigh the challenges of implementing the technology into AML practices. FinTech has great potential to save time and money, while making more accurate decisions, which ultimately helps to better serve banks’ customers.

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About the author

Susan Hackett is the CEO of Legal Executive Leadership, LLC, a law practice management consulting firm she founded in 2011 after serving as the Senior Vice President and General Counsel of the Association of Corporate Counsel (ACC) for more than two decades. As an insider working with thousands of top corporate practice leaders, Susan has an amazing breadth of experience with the inner workings of in-house practice and the implementation of value-based legal models, as well as an international reputation for innovation, excellence and success.