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.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.read more
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.read more
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?read more