Every year, 2%-5% of the global GDP, or US$800 billion-US$2 trillion is being laundered across the globe. That’s almost equivalent to the GDP of Canada (1,643.40 billion USD in 2020) or Italy ($1,886.45 USD in 2020). Neither the record-breaking heat, nor the intense floods experienced around the world this summer seem to have stopped financial criminals from inventing new ways to hide illegal sources of their income.
In the past, we’ve heard stories of elaborate money laundering plans where, for example, drug cartels from one country would move money to another country and use money exchangers to cover the origin of the funds. Nowadays, they can simply set up an e-commerce site through which they can route illegal transactions. This method of laundering, called “transaction laundering” is not entirely new, but in the era of COVID-19, when the world moved to digital channels, fraudsters had a golden opportunity for their new money moves.
During the last 18 months of the global pandemic, financial institutions (FIs) and merchants have felt the pressure to provide their clients with a positive digital experience, while simultaneously keeping fraud to a minimum and meeting their revenue targets. Stronger anti-money laundering (AML) regulations, such as Canada introduced in July 2021, put pressure on FIs compliance and risk teams to become more effective, including the ability to identify emerging threats faster.
At the end of 2020, fines and penalties imposed on FIs that breached AML regulations reached $14.21 billion. The failure to comply with AML regulations does not equate to negligence; rather, it highlights the ongoing challenges of FIs all around the world to manage the vast amounts of data required to comply with regulations.
What Makes AML Compliance So Challenging?
Money laundering is the act of criminals trying to legitimize the proceeds of crime by misusing businesses and financial institutions. It involves three main stages: placement, layering, and integration. Placement is the act of moving illegally obtained funds to a legitimate system. Layering stage consists of covering the origin of the funds. Integration is making the money available to criminals for their use. In many cases, it’s a rinse and repeat process.
Over the past few years, cryptocurrency has attracted a lot of attention from criminals. In 2019, Chainalysis traced a total of $2.8 billion in Bitcoin that had been moved by fraudsters. However, 400 times more money is still laundered in fiat currencies than in crypto.
AML regulations require financial institutions to monitor their clients to prevent illegal money transfers. Know Your Customer (KYC) is a significant element of AML programs in banking. This process of verifying the client’s identity when opening the account and periodically repeating the verification is mandatory. These procedures involve monitoring the activity of a client and understanding the types of transactions that can raise a red flag.
As FIs try to ensure a positive online customer experience, rapidly identify emerging threats, and adhere to regulations, they face a number of challenges:
- False positives. The conventional approach to detecting money laundering involves the use of rules-based systems with the focus on investigating alerts which come from multiple sources. Often a good portion of this approach is done manually. Human error increases the risk of false positives. Luckily, in 2021, more and more financial institutions are adopting a risk-based approach with payment intelligence and fraud detection tools that use AI to spot suspicious patterns that have not previously been seen.
- Reduced speed of investigation that translates into revenue losses. Increased digitization and appearance of more complex payment streams have made AML compliance even more challenging. Despite digital transformation, a lot of decision making is still made manually, as changing legacy software or fixing gaps in the payment process takes time. The amount of available data to FIs is also increasing making it harder to harness its value. As a result, investigation and information sharing between teams are lengthy and can’t move in real-time as fast as fraud.
- Lack of communication between entities or lack of international communication. Technology development and an increase in cross-border funds movement brings new challenges in obtaining and exchanging information on financial crimes between FIs in different countries as most enforcement strategies are regionally focused, not globally.
In August 2021, the European Commission presented new legislative proposals that will help solve some current AML compliance problems in the EU. At the heart of the legislative package is the creation of a new EU Authority which will transform AML supervision and enhance cooperation among Financial Intelligence Units (FIUs).
Transaction Laundering: A Growing Global Threat
In transaction laundering, criminals can take over the account of a legal merchant or make transactions by using a stolen credit card information. They can also deposit their money into a merchant’s account and withdraw them from a different location. The main problem is that very often merchants don’t have in-house fraud detection tools to identify transaction laundering and it might take significant time to uncover the criminal activity.
The volume of transactions happening online is increasing every single day. It’s cheap and easy for financial criminals to set up a professional-looking website and sell illegal products or launder money while pretending to be a legitimate e-commerce site or business.
The rise of financial payment facilitators has also contributed to the problem. A single transaction can pass through a customer, merchant, issuer, gateway, processors, and a third-party vendor. It’s much harder to monitor end-to-end transaction journeys that are becoming more complex, particularly if the monitoring system isn’t seeing where the transaction starts and ends and, everything in between.
How AML Compliance with Risk-Based Transaction Monitoring Meet
Fortunately, while financial crimes are becoming more and more sophisticated, risk management strategies and fraud detection tools are evolving at a high rate.
Anti-money laundering transaction monitoring is a process of monitoring customer transactions for suspicious activities and evaluating their client’s risk profiles. Being an essential part of any AML compliance program, transaction monitoring helps spot unusual transaction patterns (including by FI employees, mule accounts, transactions above regulatory thresholds, and other anomalies).
With many software options available to banks and merchants on the market, it all comes to the quality and availability of transaction data that will be used for making decisions. Artificial Intelligence and machine learning (ML) are playing a vital role in payment data management and precise fraud prevention.
- Adaptive machine learning and risk-scoring will help lower false positives. Transaction monitoring software tools with machine learning continuously assess individual customer activity and assign risk advice for every transaction in milliseconds. The result is a more precise fraud risk score for all types of transactions, because it’s not based on what a human thinks is a fraud: it’s based on the up-to-date information, analyzed patterns, and context.
- Machine learning models can adapt and spot threats that were not previously seen. Fraud prevention and AML tools with unsupervised machine learning can build individual customer models on the fly and flag anomalies at a customer, card or device-level, based on past behavior. They help detect new fraudulent patterns that have not been seen previously.
- Transaction monitoring with machine learning and real-time data acquisition increases the speed and precision of crime activity detection. More complete data means more advanced AI and machine learning models. In a world where customers demand near instant processing of their transaction and where fraud is evolving at the speed of light, Fis and merchants can only get ahead with a true real-time data collection, straight off their networks.
Being a step ahead of fraudsters and meeting AML regulations requires that financial institutions and merchants transform their control frameworks and use artificial intelligence tools in their transaction monitoring. Fraudsters will move more towards relatively new channels and environments, like crypto and digital wallets. They will also target companies that are viewed as weaker in their fraud prevention strategies. Real-time payment data analysis, combined with adaptive machine learning and risk-scoring, will help keep up with emerging threats while enabling FIs and merchants to stay compliant and profitable.