The eager and rapid adoption of artificial intelligence (A.I.) by financial institutions (F.I.s) may surprise those outside this otherwise cautious industry. However, the industry consensus is clear that intelligent technologies such as A.I. are major factors in the race to differentiate and establish market share. For example, a survey conducted last year by the World Economic Forum found that 85% of F.I.s had implemented A.I. somehow, and 77% of all respondents anticipated A.I. to possess high or very high overall importance to their businesses within two years.
Compliance departments at F.I.s are poised to benefit from integrating A.I. into their anti-money laundering (AML) programs. Unfortunately, previously adequate, legacy rules-based AML systems have become antiquated. They lack the sophistication needed to recognize nuances of rapidly evolving criminal patterns and keep up with new products and consumer behavior. The result is high false positive and low detection rates that sap an F.I.’s resources by requiring the need to secure more costly, experienced compliance staff. The high false positive and low detection rates stemming from rules-based monitoring are why chief compliance officers (CCOs) at F.I.s are turning to intelligent technologies such as A.I. to manipulate data more effectively across their AML programs. But, how can they do so responsibly?
Continue reading: https://www.finextra.com/blogposting/20830/responsible-artificial-intelligence-for-anti-money-laundering-how-to-address-bias
Compliance departments at F.I.s are poised to benefit from integrating A.I. into their anti-money laundering (AML) programs. Unfortunately, previously adequate, legacy rules-based AML systems have become antiquated. They lack the sophistication needed to recognize nuances of rapidly evolving criminal patterns and keep up with new products and consumer behavior. The result is high false positive and low detection rates that sap an F.I.’s resources by requiring the need to secure more costly, experienced compliance staff. The high false positive and low detection rates stemming from rules-based monitoring are why chief compliance officers (CCOs) at F.I.s are turning to intelligent technologies such as A.I. to manipulate data more effectively across their AML programs. But, how can they do so responsibly?
Continue reading: https://www.finextra.com/blogposting/20830/responsible-artificial-intelligence-for-anti-money-laundering-how-to-address-bias
