Banks and financial institutions are increasingly under scrutiny for financial crime from governing authorities and regulatory bodies. Money laundering is one of the most common financial crimes committed globally. The Basel Institute on governance, a leading organization on global governance, assesses countries and the world's average risk level of money laundering and gives them scores from 0 to 10. Their latest report estimated the average global money laundering risk score at 5.25 from 5.30 in 2021. This drop, however, could be attributed to a slight improvement in Anti Money Laundering (AML) and Combating the Financing of Terrorism (CFT) systems and regulations by governments and financial institutions. Another reason is that criminals are looking at innovative methods such as cryptocurrency to avoid detection altogether.
The emergence of newer automated AML IT systems could help combat this globally, helping institutions stay AML compliant. Financial institutions and banks have continued to invest in automated AML technology to monitor transactions and conduct background checks. However, it has not yielded the expected results- the Basel AML report also suggests that the effectiveness of AML/CFT measures across all countries fell further in 2022, from its already low level of 30% last year to 29%. The average score required for technical compliance stands at 66% as of 2022, and the global average has fallen well short of it. These existing AML systems are additionally complex and inflexible, further affecting compliance and customer experience and increasing costs. Manual risk assessments, transaction monitoring systems' efficacy, and data quality are the most significant issues plaguing existing AML systems in the banking and finance industry. Advanced analytics and reliance on advanced AI/ML for automated AML systems could be the next big step in combating money laundering in financial institutions.