According to estimates brought into light by the United Nations Office on Drugs and Crime (UNODC), money laundering is around $800 billion to $2 trillion which makes 2-5% of the global GDP. Since this criminal threat is forecasted to set new scores, authorities are making efforts by obliging businesses to develop efficient AML compliance programs. With more sophisticated fraudulent schemers skyrocketing, existing countermeasures are no more sufficient.
Legal and financial institutions such as the International Monetary Fund (IMF) and Financial Action Task Force (FATF) are placing requirements for businesses to develop anti money laundering software, especially after the incident of 9/11. This blog covers how integrating AI and ML into AML verification systems can help companies to leap forward in fighting financial crimes.
Sophisticated Money Laundering Schemes
Criminals are deploying more fraudulent schemes to reap financial benefits or launder their money for escaping AML compliance. Their use of advanced technologies to conceal identities and true funds sources are questioning the accuracy of traditional crime prevention mechanisms. Criminals easily mislead legal bodies and perform malicious activities without the businesses even noticing.
Increasing financial crime threats are making AML screening more tiresome for regulators. Furthermore, traditional technologies result in exhaustive processes while taking months to examine datasets. Criminals rely on the following stages to further cunningly escape legal scrutiny and launder high sums of money.
Criminals flow their money through various legitimate financial systems by either using fake invoices, checks, or investments. Moreover, fraudsters also physically move their proceeds of crimes to off-shore bank accounts that help them escape anti-money laundering scrutiny.
Criminals transfer their funds by making multiple small transactions or a single high-value withdrawal to hide anti-money laundering systems and suspicious detection. Hence, criminals disguise their transfer trail by using advanced bookkeeping tricks to escape AML security checks.
Making the money look legitimate by portraying it as salary, loans from a friend, or corporate investment adds to integration. This technique makes AML compliance challenging for financial institutions as the money is later used to fund terrorism, evade taxes, perform corruption, and purchase illegal weapons.
Spanish Police Detained One of the Notorious Money Launderers
Spanish police detained a suspect that is said to be one of the biggest money launderers. This arrested man was found to be an associate of the Irish gang who was involved in flowing around €350,000 in dirty funds on a daily basis. The suspect was involved in handling these transactions without any legal record and later send the money to other international criminal groups.
In order to escape anti-money laundering regulatory checks, this man was using Dubai as a safe haven. By using legitimate financial systems with AML compliance programs, he later laundered the money into Europe. To disguise the funds, suspects created a luxury vodka brand. Upon AML monitoring and investigation, records from Spain’s tax agency highlight that the lush lifestyle of this man and others involved do not support the profit this liquor company generates for them. This suspiciousness further led to the arrest of this criminal group.
Challenges in Establishing Mechanisms to Fight Money Laundering
With advanced technologies at their disposal, creating synthetic identities to bypass AML compliance and launder money has become easier. Regulatory bodies such as FATF are putting forth strict requirements like 6AMLD. Therefore, businesses also need to play their part by integrating efficient digital onboarding mechanisms to counter financial crimes. However, industries face the following challenges in establishing AML solutions for ensuring secure transactions.
- Data exists in diverse formats and records which makes analyzing information in multiple forms challenging
- High risk of false positives in alerts due to traditional AML compliance programs
- Inefficient suspicious detection that leaves enough room for criminals
- AML systems with insufficient background checks make the risk scoring questionable as businesses will only rely on employment, source of funds, and PEP status
Benefits of Integrating Artificial Intelligence (AI) and Machine Learning (ML) into AML systems
Businesses need to come up with more efficient AML compliance mechanisms. Hence, they should integrate AI/ML technologies within their crime prevention systems. These advanced solutions play a crucial part in managing financial data, detecting suspiciousness, real-time customer verification, analytics activities, and much more. Benefits of making AI and ML a vital part of anti-money laundering systems include:
- Automated examination of change in financial behavior
- Ensures real-time presence of customers
- Advanced document scanning, forgery detection, and analysis of huge datasets
- Efficient risk scoring
- Error-free transaction monitoring
Criminals are using advanced identity fraud techniques such as spoofing attempts, deepfake attacks, document forgery, and 2D/3D masks to trick AML screening checks. Integrating AI and ML within businesses’ anti money laundering red flags systems streamline identifying trails of financial crimes. These AML solutions enhance examining of suspicious financial behavior while helping industries stay a step ahead of criminals.