"Data is the new oil." Originally coined in 2006 by the British mathematician Clive Humby, this phrase is arguably more apt today than it was then, as smartphones rival automobiles for relevance and the technology giants know more about us than we would like to admit. Just as it does for the financial services industry, the hyper-digitization of the economy presents both opportunity and potential peril for financial regulators. On the upside, reams of information are newly within their reach, filled with signals about financial system risks that regulators spend their days trying to understand. The explosion of data sheds light on global money movement, economic trends, customer onboarding decisions, quality of loan underwriting, noncompliance with regulations, financial institutions' efforts to reach the underserved, and much more. Importantly, it also contains the answers to regulators' questions about the risks of new technology itself. Digitization of finance generates novel kinds of hazards and accelerates their development. Problems can flare up between scheduled regulatory examinations and can accumulate imperceptibly beneath the surface of information reflected in traditional reports. Thanks to digitization, regulators today have a chance to gather and analyze much more data and to see much of it in something close to real time. The potential for peril arises from the concern that the regulators' current technology framework lacks the capacity to synthesize the data. The irony is that this flood of information is too much for them to handle.

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.

A key data source available to FINCEN is reports of large cash transactions made to the Treasury according to terms of the Bank Secrecy Act. FAIS's unique analytic power arises primarily The most common motivation for criminal behavior is profit. The larger the criminal organization is, the greater the profit. By disrupting the ability to profit, law enforcement can focus on a vulnerable aspect of large criminal organizations. Money laundering is a complex process of placing the profit, usually cash, from illicit activity into the legitimate financial system, with the intent of obscuring the source, ownership, or use of the funds.

The Financial Crimes Enforcement Network (FIN-CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS integrates intelligent human and software agents in a cooperative discovery task on a very large data space. It is a complex system incorporating several aspects of AI technology, including rule-based reasoning and a blackboard. FAIS consists of an underlying database (that functions as a black-board), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed. FAIS's unique analytic power arises primarily from a change in view of the underlying data from a transaction-oriented perspective to a subject-oriented (that is, person or organization) perspective.