The newly enforced Anti-Money Laundering Act of 2020 (AMLA) in the US makes it more important for financial institutions to clear their alert backlogs.
Financial institutions are often encumbered with long backlogs of anti-money laundering alerts, which has only been made worse by the Covid-19 pandemic. The new regulation can reduce the backlogs, but automation is the way to prevent them from happening, a new blog from Quantifind claims.
The AMLA was recently implemented in the US and one of its changes to compliance requirements puts a “risk-based” approach to alert backlogs that prioritises the highest-risk transactions. It also hopes to increase the adoption of technology for AML. Quantifind believes resource allocation will be paramount to address backlogs in financial institutions.
The company states financial institutions struggle with increased AML compliance requirements prior to the pandemic, with it being a time-intensive task requiring thousands of investigators ploughing through millions of alerts to identify those considered suspicious.
The pandemic forced firms to have their staff work remotely, making these processes even harder to handle. It said,” The sensitive nature of this data creates additional challenges, as global financial institutions begin shifting many of their workers to working-from-home, and as workers become ill or must take care of sick loved ones. Keeping the teams sufficiently staffed to handle the growing workload is a challenge further exacerbated by investigator burnout.” This has helped increase the backlogs of alerts.
Regulations require a firm to file Suspicious Activity Reports (SARs) within 30 days from the detection of evidence of suspicious activity. Banks will have to funnel thousands of customer records, accounts associated with customers and millions of transactions and interactions on the accounts all through an analytical engine. This will then seek any transactions that violate rules. The aim is to identify meaningful alerts that need to be investigated.
The blog said, “Reduced workforces and remote employees have negatively impacted an institution’s ability to process alerts. However, we have learned that money laundering and fraud, unfortunately, often increase during a crisis.”
Backlogs cause a huge problem for financial institutions and being unable to quickly complete processes can carry a risk of fines.
Quantifind states that technology is required to ensure a firm can efficiently work through their backlogs. The problem is, using a rule-based automation platform has limitations. The systems are unable to handle complex aspects of the regulation, such as proving there are no other high-risk activities involved with a report.
The blog post said, “A machine learning approach is needed that recognizes which negative news themes and context are predictive of risk-based SAR filing, and which are not, and can comprehensively search those sources that Google does not.
“Accuracy in terms of entity extraction is of course the baseline minimum for leveraging the power of public data. However, relevance in associating the correct risks is critical for being able to automate more of the process. This approach puts into practice the risk-based guidelines of the recent AMLA.”
Quantifind’s Graphyte platform offers firms with a solution to the backlog problem. It automatically screens the subjects of all alerts through P2P lists, sanction lists, negative news and adverse media. The platform claims to eliminate 90% of an alert backlog with a one-time batch screening.
Read the full article here.
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