Serious about preventing financial crime, the anti-money laundering (AML) department of a large retail bank headquartered in Europe maintains a rigorous AML program designed to comply with legislation, regulations and guidelines stipulating the prevention of money laundering, terrorist financing and related financial crimes, as outlined by recommendations of the Financial Action Task Force (FATF) as well as Europe’s laws and regulations.
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.
After an extensive search and due diligence process, the bank selected QuantaVerse based on the company’s financial crime expertise and readily deployable AI-enabled AML solutions. The bank started with QuantaVerse’s Chief Compliance Officer (CCO) Checkup which is a quick and easy analysis used to test the effectiveness of its existing AML program. QuantaVerse worked with the bank to load and profile a sample of one month’s worth of transactions before the CCO Checkup analysis was conducted.
“The bank started with the CCO Checkup because it allowed them to evaluate, with minimal effort and no long-term commitment, an AI-powered solution’s role in detecting false negatives that were missed by its TMS,” explained David McLaughlin, CEO and Founder of QuantaVerse.
Through its analysis, the QuantaVerse Financial Crime Decision Engine risk segmented the transacting entities and ultimately found scores of high-risk and very high-risk entities (made up of both clients of the bank and counterparties of clients) that was roughly 2.5 times greater than what QuantaVerse has historically witnessed in data analysis from similar banks in similarly sized data sets.
As part of the CCO Checkup, QuantaVerse provided Financial Crimes Reports (FCR) detailing the top 10 cases determined to represent the greatest risk to the institution. The FCR included all necessary supporting documentation required by the bank’s AML investigation team and for the speedy creation of a suspicious activity report, if required.
Concerning behaviors documented in the 10 sample FCRs included:
- Bank customers were found to be transacting with risky jurisdictions including known war zones like Afghanistan
- Transactions from the Tri-Border area of South America to the EU appeared to be linked to Hezbollah financing
- Significant numbers of bank customers transacted with cryptocurrency exchanges. While some were registered exchanges, these transactions often demonstrated money laundering attributes that represent significant transactional risk to the bank.
- Broad instances of risky transaction typologies such as structuring, round dollar transfers, keyword risk, one to many, and invoice anomalies such as multiple invoicing
- PEPs (politically exposed persons) were large sum, round dollar beneficiaries of bank customers from transactions with no detail or derivable economic purpose
- Within the supplied transactions, the bank’s TMS was alerted only 31 times. Through QuantaVerse’s automated investigation process, it was found that the TMS alerts were all of insignificant risk, while the CCO Checkup uncovered more than 800 risky entities that previously went undiscovered by the bank.
Based on the CCO Checkup analysis, QuantaVerse Financial Crime Detection Engine identified hidden risk using only a very small set of provided transactions, and limited processing power.
With QuantaVerse, the bank is now well-equipped to find hidden risk, quickly eliminate false positives and automate 70 percent of its investigation processes. This enables the bank’s investigative teams to spend the bulk of their valuable time on evaluating and more closely considering its most complex cases.
With AI integrated into its AML compliance program, the bank is better protected from challenges such as regulatory fines, reputational damage, AML remediation and unnecessary consulting/monitoring costs. What’s more, it protects the bank’s market valuation from changes to its stock price given that AML violation announcements have shown to result in an immediate 10 to 15 percent decline in a bank’s stock price.
In the months ahead, the bank will expand the use of QuantaVerse AML solutions to curb the flow of illicit funds that enable human trafficking, drug running and terrorism:
- QuantaVerse Pre-TMS Entity Resolution & Risk Scoring solution to provide risk segmentations to all parties to the transactions processed by the bank
- QuantaVerse False Negative Identification and Investigator solution to identify financial crime risks missed by its existing TMS and to automate those investigations
- QuantaVerse Automated V & V Transaction Analysis solution to conduct analysis of foreign correspondent banking transactions and identify/provide risk scoring of all third-party clients, intermediary banks and counterparties
- QuantaVerse Alert Investigator to automate the investigation of alerts coming being produced by its existing TMS
Looking even further down the road, the bank anticipates leveraging AI-enabled solutions from QuantaVerse to address other financial crime concerns such as trade finance, sanctions, FCPA, and insider threats.
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.
Regulators and those handling compliance at covered institutions have long accepted the pitiful state of AML program efficacy, including: An estimated $2 trillion laundered through the global banking system annually 90+% of false positives coming from transaction...
While not mandating that firms invest in technology to automate financial crime investigations, regulators are certainly encouraging it. They are noticing that advanced BSA/AML teams are using robotic process automation (RPA) bots to gather data for investigations. They are aware that those same firms are using machine learning to analyze huge data sets, identify patterns, and pinpoint where exceptions or anomalies exist.