AI-powered technology improves AML Anti Money Laundering programs to be more compatible, efficient, and effective solutions for analyzing and detecting suspicious transactions of high-risk accounts.

Current transaction monitoring programs process huge amount of data in seconds therefore system is prone to False Positive results which may lead lower operational efficiency while analyzing alerts of suspicious transactions. AI-powered technology helps to decrease False Positives by teaching the program to evaluate and expand with each False-Positive outcome.

AI-powered AML programs also benefit from Fuzzy Logic Algorithm, that is similar to human reasoning like detailed decision treemaps. Therefore, even a low-risk account can be detected to be involved in a suspicious transaction, which may not be listed with conventional transaction monitoring applications.

Tackling with financial crimes by Anti Money Laundering programs require careful consideration for results to take necessary actions such as issuing SARs if necessary. Even though current AML programs provide essential coverage to conduct compliance, False Positive results have risk to decrease operational efficiency because each alert that is resulted as suspicious transaction require cognitive decision-making process. Therefore, AI and Machine Learning can offer more reliable results with fuzzy logic and clustering algorithms, which may be similar to the cognitive decision-making process in human beings, as a complementary tool to reduce False Positives.

Nonetheless, can this technology detect new money laundering schemes before the technology becomes widespread? Artificial Intelligence and Machine Learning supported AML software that is currently on the market ultimately has the potential to give an advantage to the banks and other financial institutions.

Pattern recognition
Because AI learns from the datasets assigned to it rather than parameters set by its operators, it should be able to more accurately identify suspicious activity, allowing banking compliance units to focus only on the high-risk cases.

Fewer False Positives
One of the most widely discussed uses of AI in AML compatibility is its potential to implement a significant attribute to processes that are already automated and therefore eliminate time-consuming False Positives.

In most cases, AML systems are necessarily activated by raw triggers because the rule tends to be more secure than flexible to avoid any passed-over financial crime. This results in a high number of false positives – more than 90% by most estimates.

Artificial intelligence has the potential to catch not only more suspicious transactions, but essentially to correct innocent ones.

Identifying individuals

One of AML’s available tools is a global sanction list of individuals and organizations known to be involved in financial crimes such as money laundering or terrorism financing.

Currently, it may be possible for some financial criminals to escape from the sanctions network because current processes may not able to detect misspelled names or vague aliases.

However, with sufficient data, AML systems powered by AI and machine learning will be able to identify suspicious parties through a set of interconnected identifiers.