The uphill battle against money laundering and financial crime in 2022 was marked by progress in some areas but offset by a mountain of new challenges.
In fact, progress in AML programs appears to be “stuck,” according to the Basel AML Index which ranks money laundering and terrorist financing risks around the world.
The 2022 report concluded that any real progress is being impeded by increased risks. No doubt these trends will continue to impact anti-money laundering efforts in 2023, as most countries continue to be too many steps behind criminals seeking to launder illicit funds.
The following are some of the negative headwinds that are weighing on compliance programs.
Sanctions screening pressure
Sanctions lists saw a 95% jump in updates in the first half of 2022 due to the Russia-Ukraine war. This has caused a massive impact on compliance programs now faced with a higher stack of alerts, pressure on list updating processes, and burdensome alert remediation.
Real-time sanctions screening is a regulatory requirement and a major point of friction in the world of instant payments. Sanctions screening quickly turns into a bottleneck for financial institutions as surgical precision is required while matching a variety of imperfect and unstructured payment data against sanctions lists.
Crypto is still a heyday for financial crime
Financial criminals continued to flow to cryptocurrency and other virtual assets in 2022 to innovative methods of money laundering. The Basel report called the AML risks from virtual assets “worrisome” with compliance oversight dropping dramatically.
In one case that was uncovered last month, two Estonian citizens were arrested in a $575 million cryptocurrency fraud and money laundering scheme.
Data breaches
Digital authentication hazards continue to challenge the financial system, as face-to-face identification is no longer a requirement to open a digital or mobile bank account. Criminal sophistication continues to cause data breaches and identity theft, where information can end up in money-laundering schemes.
The ease of opening bank accounts and the surge in the number of financial ecosystem players complicate the ability of the financial ecosystem to detect the true identities of the people and the activity behind them.
Major data-compromising incidents and new patterns were recorded globally in 2022. In South Africa, credit bureau TransUnion SA suffered a cyberattack that saw data stolen from around three million customers by a criminal third party. In South Korea, researchers identified a new “fake calls” banking trojan. The bot has the ability to “talk” to victims and pretend to be an employee of the bank in order to gather personal information.
In the United States, 2.5 million social security numbers were stolen in a student loan data breach at Nelnet Servicing.
In positive developments, measures are being taken to improve AML/CFT capabilities. Banks and fintech are continuing to adopt new AML solutions powered by advanced AI technology to get out of the rut. Indeed, according to the Basel report, the highest level of progress was achieved with the involvement of the private sector.
Here are areas where AML is heading in the right direction:
New AML legislation is on the horizon
With the intensity of financial threats growing, many governments are moving to enact legislation to close gaps in risky areas.
In the United States this month, the Senate introduced a bipartisan anti-money laundering bill that aims to prevent the use of cryptocurrencies as a way to launder money and finance terrorism. The Digital Asset Anti-Money Laundering (DAAML) Act would extend existing banking regulations to cover digital currencies and designate cryptocash sellers as money service businesses.
Likewise in the EU, the European Council concluded its AML policy discussions with a decision on a strengthened rulebook against money laundering and combating the financing of terrorism (AML/CFT).
The new EU AML rules will be extended to the entire crypto sector, obliging all crypto-asset service providers (CASPs) to conduct due diligence on their customers. The council is also proposing an EU-wide maximum limit of €10,000 for cash payments to make it harder for criminals to launder dirty money.
In Hong Kong, the Legislative Council passed an amendment to the Anti-Money Laundering and Counter-Terrorist Financing Ordinance (AMLO) that introduces a licensing regime for virtual asset service providers (VASPs).
A risk-based approach to AML
The adoption of the risk-based approach prescribed by the FATF is improving. According to the Basel Report, this is a “positive development that enables governments and financial institutions to efficiently allocate resources towards the biggest and most serious risks or cases.”
Specifically, the FATF is promoting the use of technology to implement a risk-based approach that can improve AML/CFT efforts. According to the FATF, technology has the potential to make efforts to combat money laundering and terrorist financing (AML/CFT) faster, cheaper, and more efficiently. Moreover, according to the FATF, artificial intelligence-based tools can analyze data accurately and help better identify emerging risks.
How to get out of the rut
There is no better way to get out of a rut than to make a strategic change or look at a problem from a different perspective.
Artificial intelligence is an innovative approach that can analyze transaction data from a different angle, delivering deep insights from a risk-based perspective.
In fact, deploying machine learning and AI technology is the best way today to implement the most effective risk-based AML transaction monitoring solution. AI can effectively calculate the risks to pinpoint suspicious activities outside of expected behavior.
This explains why AI-based solutions are experiencing accelerated adoption by financial institutions in search of more sophisticated solutions to manage compliance operations. With criminals becoming increasingly sophisticated, new more powerful tools are necessary to connect the dots.
AI can piece together and analyze a multitude of data sources and risk factors to detect suspicious activities across complex, cross-border transaction paths – including the use of shell companies by bad actors attempting to launder money or circumvent sanctions.
Allowing the data to lead to abnormalities, instead of telling the system what to look for in a rigidly deterministic rules-only based methodology, also affords AI the ability to detect new and unpredictable typologies, or “unknown-unknowns” And thereby get a step ahead of financial crime.
Go get the bad guys in 2023!
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