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Report: How Financial Firms Are Using Artificial Intelligence and Machine Learning to Meet AML Demands of Today and Tomorrow

It’s a well-known fact that the global pandemic caused a radical shift in consumer banking and payments behavior. What isn’t as obvious is how financial institutions responded behind the scenes. Fortunately, a new study helps shed light on the pandemic’s impact on the adoption of new technologies for anti-money laundering (AML) efforts.

850 ACAMS financial institution members worldwide were surveyed for the report titled “Acceleration through adversity: The state of AI and machine learning adoption in anti-money laundering compliance.” Each respondent was surveyed about their employer organization’s use of technology to detect money laundering. This is estimated to account in the range of 2% to 5% of the global gross domestic product or $800 billion to $2 trillion annually.

The key takeaway from the survey is that the radical shift in consumer behavior sparked by the pandemic made many financial institutions acknowledge that static, rules-based transaction monitoring strategies simply aren’t as accurate or adaptive as AI-powered systems. A COVID-driven surge in cyber fraud forced a third of surveyed financial institutions across the globe to accelerate their AI and machine learning technology for AML purposes.

The Impact of AI and Machine Learning Technologies

According to survey respondents, the primary drivers of AI and ML adoption are to improve the quality of investigations and regulatory filings (40%) and reduce false positives and resulting operating costs (38%). More than half (57%) have either deployed AI/machine learning in their AML compliance processes, are piloting AI solutions, or plan to implement them in the next 12 to 18 months.

Regulatory agencies and AML teams are in agreement that innovation and automation offer a path to improvement. There’s clearly shared hope that these tools will produce truly effective financial intelligence that weeds out the bad guys.

Proof That Size is Not the Issue

Technology adoption is not just limited to the largest financial institutions either. An impressive 16% of smaller financial (valued below $1 billion) view themselves as industry leaders in AI adoption. Compare that with the 28% of surveyed large financial institutions (with assets greater than $1 billion) who consider themselves innovators and fast adopters of AI technology.

Advanced technological solutions are clearly not beyond the reach of smaller financial organizations. Smaller and larger organizations alike are subject to the same level of scrutiny, so it’s important right now that these numbers continue to rise. What’s more, smaller institutions are more sensitive to the increasing costs associated with hiring more AML investigative staff to keep up with regulatory expectations as well as growth.

How QuantaVerse Findings Compare with the Survey

As the study notes, AI and machine learning are gaining serious momentum in AML compliance. The technology is helping to reduce false positives, ease caseloads, streamline reporting, and lower operational costs. The right AI and machine learning solution can be quickly integrated into existing compliance programs with minimal disruption. Early adopters of these approaches are quickly realizing significant efficiencies while helping their institutions comply with rising regulatory expectations.

Financial institutions using QuantaVerse solutions have seen false positives coming from the TMS drop by 25 to 40%.  In addition, QuantaVerse customers are experiencing a 70% reduction in time spent investigating the alerts that are produced. On average, QuantaVerse’s technology helps financial institutions turn a 15-minute review into a 5-minute triage, a 3-hour investigation into a 1-hour investigation, and a 5-hour HRE review into a 1-hour HRE review.

All data work and analysis are done by machines before presenting findings. Risk scoring, transaction details, typology, and counterparty risk are clearly presented to investigators for high-risk FCIRs and further supported by relationship visualizations. This shifts investigators’ time and focus where it’s most needed, and cases can be completed in a fraction of the time.

What’s more, 100% of QuantaVerse deployments have found previously unidentified risk (false negatives). This helps meets changing regulatory expectations, which indicate that mediocre AML outcomes are no longer acceptable.

What’s Next for AML Technology

According to Wealth Insight, it is estimated that global AML spend exceeds $8 billion annually. Despite the pandemic’s disruption, 29% of compliance professionals surveyed in the report stated that their AI/machine learning adoption plans will continue unabated. Having learned the lessons of 2020, and with incidents of financial crime continuing to increase globally, the biggest risk financial firms face right now is the risk of doing nothing to stay ahead of the criminals and in full regulatory compliance.

Don’t Get Caught Out or Left Behind

Learn more about QuantaVerse’s AI-powered AML solutions and how you can automate your financial crime investigations at: