Starting back in 1970 to present day, there has been a progressive increase in anti-money laundering (AML) regulations. As depicted in the graphic below, the ability of financial institutions’ (FIs) to respond and react to increased regulations has not been keeping pace with regulatory implementations.
State and federal AML regulations will continue to challenge FIs, and the only way to close the gap is to implement proven, advanced data science solutions powered by artificial intelligence (AI) and machine learning. The new technologies of AI and machine learning can enhance existing AML processes in places such as transaction monitoring systems (TMS), Correspondent Banking (CB) systems, Customer Information Program (CIP), Know Your Customer (KYC) systems, fraud, and other investigative and regulatory challenges.
Increased Regulatory Requirements have Surpassed FIs’ Capabilities to Respond
Filtering millions of transactions through a TMS and routinely dealing with a 95% false positive rate is just one of the many obstacles AML investigators are faced with overcoming. FIs need to ensure their human talent is investigating the most serious cases and working on only the most important compliance projects, instead of being forced to rely on human capital to sort data on spreadsheets and manually extract information for management briefings. Personnel resource allocation for model development, validation, data quality, complex investigations, fraud, and other key financial crime concerns can be enhanced through advanced data science application. Advanced data science platforms are capable of cleansing, sorting, and analyzing terabytes of data almost instantly thereby providing investigators actionable intelligence and data at the start of their compliance duties.
Compliance leaders should be receptive of and concerned about regulatory pressure, as evidenced by the approximately $697 million in penalties assessed in 2016, and the approximately $48 million so far in 2017 by federal and state regulators for BSA and AML shortcomings. Filtering out the “TMS noise” should be an integral part of any FI’s plan to address regulatory gaps in their compliance strategies. Advanced data science solutions, including AI and machine learning, can help FIs close the gap on regulatory response and compliance.
Automating High-Risk Entity Reviews to Reduce Errors, Improve Efficiencies, and Ensure CDD Compliance
Federal bank regulatory agencies require that financial institutions regularly review and segment all their customers based on risk. While the Customer Due Diligence (CDD) Rule, which amended BSA regulations in 2018, does not stipulate how often reviews should be...
On January 1, 2021, the Anti-Money Laundering Act (“AMLA”) was enacted by Congress as part of the National Defense Authorization Act (NDAA). The AML Act has made emerging technologies, such as AI, machine learning, and quantum information sciences, a national...
Disruptive technologies are no longer the stuff of science fiction. They’re not even the “next big thing.” Successful firms are using them right now to automate manual processes in their AML programs.