Traditionally, the processing and investigation of anti-money laundering (AML) alerts at banks has proven to be a difficult and costly problem. Large investments are often made on teams of human analysts, who are tasked with managing and investigating alerts and making high risk decisions.
The wide scope of surveillance means that AML Transaction Monitoring (AMLTM) systems could potentially trigger as many as 200,000 to 300,000 alerts a month in extreme cases – requiring extensive resources to investigate and manage. Furthermore, differing review techniques across large teams of human analysts can lead to consistency issues and decision errors.
At a time when all industries are under pressure to reduce expenditure, the Nasdaq Automated Investigator for AML can help firms increase operational efficiencies in AML investigations by over 40% while dramatically improving quality assurance and saving costs.
How can Nasdaq Automated Investigations for AML help you?
Quality at Scale
100% of your alerts investigated in depth regardless of volume
Improved Investigation Quality
Always consistent and fully explained expert decisions
Increased Operational Efficiencies
~50% Fully Automated Investigations
~50% Highly Augmented
Specialised Investigator Agents (IA’s) ingest customer financial data.
Each IA is validated by experts to generate different types of financial risk evidence.
The Nasdaq Automated Investigator for AML has a Decision Module that assesses IA evidence and generates risk decision and rationale in seconds.
Generates human readable explanations and renders information directly in client Case Management System – no black box.
Automated mechanisms and tools ensure ongoing decision assurance, regulatory compliance and continual performance improvement.
Today banks have AML investigation teams running into the 100’s if not 1000’s. These resources are expensive and currently in short supply. As with any human capital resource intensive group, these departments are error-prone and the investigation process lacks consistency from one person to the next, creating inconsistencies in reporting.
The high cost of keeping large teams, abundance of errors and inconsistencies lead to regulatory exposure.
- The solution automates much of the current investigations process and in the majority of cases will produce a decision and a standardized report with supporting evidence, thereby reducing errors and improving operational efficiency.
- Because reports are generated through a machine, they have been proven to contain fewer decision errors and have less quality assurance issues, mitigating regulatory exposure and improving operational efficiency even further.
- The Automated Investigations Management space is a historically underserviced area of financial crime operations that presents a significant gap in the investigations management process for banks. Many techniques are used to launder money, causing banks to cast a wide net to catch perpetrators. The wide scope of surveillance means AML Transaction Monitoring (AMLTM) systems could potentially trigger as many as 200,000 to 300,000 alerts a month in extreme cases. In response, many banks have tightened the parameters within their AMLTM systems or added additional scoring mechanisms, thereby reducing alerts. However, when banks tune their models too tightly, they run the risk of missing criminal activity and exposing themselves to regulatory sanction. Even those that have tightened their parameters can experience 20,000 to 25,000 alerts in a month. Without capabilities like ours to intelligently manage through the backlog of alerts, with full audit trail capabilities and explainability behind machine learning models, 99% percent of laundered money could continue to illegally flow through the financial system for years ahead.
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