It is believed that 2% to 5% of global GDP, or US$800 billion to US$2 trillion, is laundered annually, and this is a conservative estimate.
Money laundering is often associated with illicit weapons sales, smuggling, embezzlement, insider trading, bribery, and computer fraud schemes.
It is also prevalent in organised crime, such as human, arms, or drug trafficking.
According to a recent global anti-money laundering (AML) research conducted by SAS, the leader in analytics and AI, in partnership with ACAMS and KPMG, 57% of institutions have adopted AI and machine learning (ML) in their AML compliance department or plan to do so imminently.
For South African companies, the reality of the country potentially being grey listed by the Financial Action Task Force (FATF) in February next year is significant cause for concern.
The country received 20 negative ratings (out of a potential 40). While the FATF placed South Africa on enhanced follow-up which saw the task force performing a follow-up visit in October this year, there have not been many discernible changes made to policy and regulation to realistically see it avoid a grey listing.
The impact of this will see local businesses be subject to enhanced due diligence, which will mean more frequent and more invasive assessments for anti-money laundering and combatting of terrorism financing measures risks, amongst others.
The common denominator is that financial institutions need to act fast to stay compliant with the AML / CFT regulatory requirements. Compliance, however, poses three major challenges: extremely high false positives, a growing volume of cross-border transactions, and constantly changing AML / CFT regulations and business requirements.
As regulators around the world, including across Africa increasingly judge financial institutions’ compliance efforts based on the effectiveness of the intelligence they provide to law enforcement, it’s no surprise that 66% of SAS survey respondents believe regulators want their institutions to leverage AI and machine learning.
These technologies help reduce false positives, ease caseloads, streamline reporting and lower operational costs.
SAS Anti-Money Laundering takes a risk-based approach to help financial institutions uncover illicit activities and comply with AML and CTF rules.
With embedded AI, machine learning, and other advanced analytics techniques, such as deep learning, neural networks, natural language generation and processing, unsupervised learning and clustering, robotic process automation and more, SAS Anti-Money Laundering drastically bolsters AML and CFT efforts.
SAS has been named a Leader in anti-money laundering solutions in The Forrester Wave: Anti-Money-Laundering Solutions, Q3 2022 report, achieving an almost perfect score of 4.85/5 on its current offering.
“As money laundering methods get more sophisticated, financial organisations rely on ever-more advanced AML solutions to detect and combat financial crimes,” said Stephan Wessels, SAS Head of Customer Advisory for South Africa. “However, despite the promising results and billions spent yearly on basic regulatory compliance tasks, some have been slow to change. Our sophisticated anti-money laundering solution leverages AI, machine learning, intelligent automation, and advanced network visualisation, to deliver unprecedented prediction and detection capabilities, the lowest false positives, and reduced investigation times.”
