AI and money laundering: nullifying systemic issues
From diagnosing skin cancer to developing Amazon's Alexa, Artificial Intelligence (AI) has grown to play a part in everyday life. AI's potential for good is significant, and its growing use in the complex world of Anti Money Laundering (AML) can help nullify a truly systemic issue.
Money laundering is now primarily conducted online. As advanced technology becomes more readily available, the volume and complexity of the crime it enables equally increases. Criminals continue evolving to stay one step ahead, using new, sophisticated technologies to get the upper hand.
Billions of pounds of illicit funds flood into the UK financial system each year. The pain and suffering associated with this money are unquantifiable. However, we know these funds directly contribute to the UK cost of living crisis and also directly finance Putin's war in Ukraine.
If the UK can bolster its defences to stem the influx of dirty money, we can improve the lives of everyday Brits. AI is a powerful weapon that can strengthen our arsenal.
AI in AML Today
The FCA defines AI as the theory and development of computer systems that can perform tasks which previously required human intelligence.
AI is used across various AML tasks and improves efficiency, quality, and accuracy. For example, many Electronic Identity Verification (EIV) systems use AI for facial recognition and document tampering detection. It has also enabled organisations to filter out false positives for sanctioned and politically exposed people (PEP) while reducing operational costs.
AI-powered AML systems enable compliance teams to cut through the noise associated with vast volumes of data and focus on high-risk red flags.
Compliance teams are using AI to detect suspicious transaction patterns
AI can automate looking for anomalous behaviours, with machines continuously monitoring and effortlessly detecting patterns as they occur. In the face of vast volumes of data, identifying complex behavioural patterns is a task that is now virtually impossible for humans to do alone.
AI can help firms simplify their reporting process. For example, it can flag and auto-generate tasks based on monitoring, verifications and sanctions results, giving compliance teams quicker answers whilst upholding regulatory standards.
AI dramatically reduces labour costs and improves operational efficiency in AML efforts by streamlining the process. However, there are still several challenges that AI must overcome before it can tackle money laundering.
The limitations of AI
Due to AI being in its nascent stage, it does not give us the perfect solution to AML and still comes with certain errors and inaccuracies. Because of this, human input and considerations still matter. Whatsmore, human interactions ensure individuals are supported and guided through the AML process.
While AI will change the type of work compliance teams do, it does not eliminate the need for humans. Paul R. Daugherty and H. James Wilson explain specifically why humans and machines need to work in unison. AI systems collect and process data, while humans focus on analysing that data and making informed risk-based decisions.
The future of AML with AI
The highly interconnected nature of businesses, cash and economies means that global money laundering will always be an issue.
Not all hope is lost. Cross-border AML collaboration with shared global datasets could be one way to help tackle this problem, as well as exceptional cyber security. That said, perhaps the most effective AML approach is to blend traditional human-driven methods and AI technology for maximum speed, experience and accuracy.
