QuantaVerse Reduces V&V-Triggered False Positives Created by Transaction Monitoring Systems (TMS), Slashing the Number of Unnecessary AML Investigations
CLEVELAND (December 1, 2021) – AML RightSource (“AMLRS”), the leading outsourced provider of Anti-Money Laundering (“AML”), Know Your Customer (“KYC”), and Bank Secrecy Act (“BSA”) compliance solutions, has added new AI models to its QuantaVerse Financial Crime Platform. These models predict volume and value across a wider array of transaction activity profiles, fine-tuning V&V analysis and further reducing false positives.
The QuantaVerse False Positive Reduction solution enhances V&V predictions by augmenting algorithmic content and broadening the number of signals against which it makes predictions. This provides higher accuracy and ensures that TMS don’t incorrectly flag innocuous transactions. False positives are therefore never even created by the TMS, which significantly reduces the number of required investigations.
For example, one of the metrics that the QuantaVerse solution examines is distributions of activity over time. This considers the number of transactions and the dollar value of transactions across given time periods. Credits, debits, and other transaction types can be examined independently. The solution utilizes this ensemble of models to provide predictions using a wider variety of signal types.
One QuantaVerse customer found that V&V was triggering roughly 50% of the alerts coming from their legacy TMS. With enhanced V&V modeling, the QuantaVerse False Positive Reduction solution was able to cut V&V-related false positives in half. Effectively, QuantaVerse prevented 25% of TMS alerts from being created.
“Transaction monitoring systems are important in the fight against financial crime, but their capabilities are limited and they generate overwhelming numbers of costly false positives that divert attention away from truly suspicious cases,” explained David McLaughlin, SVP Head of Technical Integration, AML RightSource. “Accurate V&V calculations are particularly important in identifying shell corporations that can be used to facilitate corruption and tax evasion as recently exposed in the Pandora Papers and the Panama Papers before that.”
Criminals are adopting sophisticated methods to bypass detection, which makes it increasingly difficult to identify illegitimate companies. To identify potential front and shell companies, firms need to actively monitor transaction volumes and values, while examining clients for other high-risk indicators such as locations or stakeholders, nature of payments, economic purpose, related parties, news mentions, etc.
QuantaVerse solutions cut compliance costs and lower risk by automating every phase of the AML process. The industry’s most complete financial crime investigation platform, QuantaVerse uses RPA, AI, and machine learning to automate data gathering, identify financial crime, and document findings in order to streamline reporting. The QuantaVerse solutions are proven to help regulated entities become meaningfully more efficient with their AML investigations and effectively reduce risk by identifying financial crime missed by legacy systems and processes. To learn more, please visit: www.quantaverse.net.