Preventing criminals from laundering illicit proceeds, funding terrorism, and committing other financial crimes is as critical as ever. Vast amounts of financial crimes go through banks unalerted and are therefore uninvestigated and unreported. With trillions of dollars in laundered money still channeling through the global banking system, AI and machine learning can put an end to banks being unwitting partners of criminality, corruption, and terrorism.
Intent to strengthen its anti-money laundering (AML) compliance program, a large bank with locations across multiple U.S. states, sought to implement risk mitigation measures, streamline its investigation processes, and improve efficiency. The AML team looked for AI-powered solutions that could better identify money laundering and other financial crimes while maximizing their existing AML resources.
After a thorough competitive analysis, the bank selected QuantaVerse solutions including:
- QuantaVerse Pre-TMS Entity Resolution & Risk Scoring: This solution reduces the false positive alerts created by TMS by classifying the risk of each transacting party, down to the pseudo-client level before TMS rules and models are applied. By solving both the data problem and the risk segmentation problem, QuantaVerse enables existing TMS rules and models to work as promised. This solution has been proven to reduce the costs associated with investigating false positives by 20 to 40 percent.
- QuantaVerse Alert Investigator: By replicating much of the human investigative process that analyzes entities, transactions, and their intention (or economic purpose), more than 80 percent of an AML case investigation can be completed by QuantaVerse. In addition to handling alert investigations at much higher volumes and levels of consistency, the results of investigations are made fully transparent through the QuantaVerse Financial Crime Investigation Reports (FCIR), freeing investigative resources to focus their time and talents on the most complex financial crime risks.
- QuantaVerse False Negative Identification and Investigator: This solution examines transactions for financial crime risks that a TMS has missed. The solution can query multiple years’ worth of related entity transactional data and can enable the efficient examination of transactions, including ones considered as Below the Line (BTL).
The QuantaVerse FCIR helps automate alert investigations, detailing suspicious activity, and providing risk scores for both entities and transactions. The QuantaVerse FCIR includes the supporting documentation necessary for the case to be efficiently analyzed by an AML investigator and for creating a suspicious activity report if indicated.
Work began with QuantaVerse conducting an adjudication analysis that looked at the bank’s recent entity and alert transaction data. In the initial analysis, the QuantaVerse platform was able to:
Identify Undiscovered Risk
- QuantaVerse determined seven percent of the entities evaluated were “High” or “Very High Risk”
- More than 10,000 unalerted entities were identified by QuantaVerse as warranting further investigation
- Transactions worth more than $100 billion were associated with previously unalerted entities
- 24% of the risky unalerted entities were the bank’s own customers
Deliver Investigative Efficiencies
- QuantaVerse automatically cleared 75% of alerted entities and 72% of transaction-related alerts
- For cases that required investigation, QuantaVerse reduced the time spent on investigation and SAR filing from an average of 90 minutes per case to 20 minutes per case
- The bank’s AML investigators can now complete 120 challenging cases and reports each week instead of the 26 they were generally able to address using legacy technology and processes
By virtually eliminating the 45-60 minutes devoted to investigating alerts that are cleared and reducing approximately 70 minutes investigating and reporting on challenging cases, CCOs are able to estimate the potential savings realized by engaging the QuantaVerse Financial Crime Platform. In a given time period, assuming there are 1,000 alerts deemed “Investigation Not Recommended” and 300 “Investigation Recommended” alerts that are streamlined, an AML team could calculate savings of $135,000 in investigative costs.
Based on the successful adjudication analysis, the bank is moving QuantaVerse AI solutions into production. Moving forward, the bank will be better equipped to find hidden financial crime risk while automating more than 80 percent of its AML investigation processes, enabling investigators to focus their time and talents on the most complex cases.