In the ABA/ABA FinCEN (Financial Crime Enforcement Network) conference 2019, the acting director Ken Blanco discussed the introduction of new divisions for transforming the current AML/CFT regimes in the UK. There were entire new divisions of enforcement and compliance along with global investigations to restrict financial crimes. Until now the current Anti-Money Laundering (AML) landscape was struggling to gain some form of momentum. However, with the implementation of the recent Anti-Money Laundering Act 2020 and AML solutions with integration of AI and ML, hopefully, 2022 will be remembered as a year proven to be a turning point for financial institutes. It is surprising to note that in the year 2020, banks from all over the world paid a total of $15.13 billion dollars and the US held the first rank in those AML fines, a sum of $11.11 billion was paid.

Fueled by mobster movies and international espionage thrillers, the phrase has a mysterious, exciting edge to it. But as is often the case, the truth is far less appealing than the glitzy Hollywood version. In reality, money laundering is an activity that traps 40.3 million people in modern slavery, fuels political unrest, and finances terrorism across the globe. Considering the consequences, it's no wonder governments enact AML regulations. And just as money laundering crime grows more sophisticated, so too do the regulations. These regulations have honorable and important intentions, but there's no denying the ever-evolving compliance headaches they create for financial institutions.

In the financial services industry, innovations such as faster payments, together with global regulations and regulatory bodies1 raise a significant question: Do existing risk-rule-based money laundering detection systems really work? Financial institutions may also wonder about the effectiveness of fraud detection, the danger of false positives, structural costs, and the impact on customer transaction experience. According to Gartner, "if money laundering were an economy, it would be the fifth largest in the world." Strikingly, the UN also estimates that the amount of money laundered annually is equivalent to 2-5% of the global GDP. Another report from the Heritage Foundation suggests that complying with anti-money laundering (AML) rules, which require hiring the right talent needed to file suspicious activity reports, costs U.S. companies alone as much as $8 billion a year.

Bayesian inferencing enables calculation of the probability of a new event on the basis of earlier probability estimates of events in the past, derived from existing empiric data. With the Bayesian approach, one can use objective data or subjective opinion to specify a response. Once this is done, the earlier probabilities can then be used to make better decisions. AI-powered AML solutions thus derive meaning from complex or imprecise data, recognise patterns, and determine trends that most human beings or other computer techniques fail to identify. In addition, they become increasingly intelligent as more and more data enters the system, thus rendering them far more effective than conventional AML solutions in the long run.