In an earlier post, we outlined five approaches financial institutions can take to get AI working in their AML compliance program. For most banks, packaged AI solutions offer the most expedient way to meaningfully improve AML outcomes.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators. Plus, packaged solutions that include neural networks continuously discover new financial crime patterns and leverage that intelligence for the benefit of all the AML teams using the solution.
After determining that a packaged AI for AML solution is the best approach for your organization, how do you know you are choosing the solution that will accomplish your goals?
Develop Your Packaged AI Shopping List
When evaluating packaged AI solutions, consider solutions with the following capabilities:
A comprehensive approach…
- Effectively ingests and analyzes data from a variety of necessary sources – transaction networks, investigative databases, sanctions lists, deep web, social networks, media coverage, etc.
- Provides holistic scoring of entities and their related activities so cases can be quickly evaluated and escalated as indicated
- Automatically produces complete case reports that incorporate the data required for accurate appraisal and filing regulatory reports if needed
Avoidance of headaches…
- Deploys easily and quickly on-premise or from the cloud
- Integrates with your existing transaction monitoring systems (TMS) and other AML technology
- Deployments proven to be easily validated if required for regulatory purposes
- Adapts easily to your existing investigative and regulatory processes
- Offers trials for proof-of-concept testing
Delivery of measurable results…
- Meaningful reduction of false positives produced by TMS
- Reduces risk exposure by identifying false negatives missed by TMS
- Automates manual processes in AML investigations, reducing the time investigators spend on generic cases and focusing their hours on more complex and high-risk cases
QuantaVerse’s Packaged AI Solutions
QuantaVerse has developed productized AI solutions specifically for combatting financial crime in various lines of business such as correspondent banking, U.S. dollar clearing, corporate and commercial banking, retail banking, trade finance, money service businesses, and alternative and emerging FinTech payment providers.
The company offers four distinct packaged AML solutions, which can work individually or in conjunction with each other, and can be applied for risk assessment, transaction monitoring, case investigation, reporting, and SAR adjudication and filing. QuantaVerse works where other solutions fail because its technology can discover patterns and discern anomalies in data that are regularly missed by current approaches.
Its proprietary tools and methods make it easy to access, acquire, ingest and analyze huge amounts of structured and unstructured data. QuantaVerse solutions integrate with institutions’ existing rules-based TMS and standard industry processes, and can be deployed within 60 to 90 days (after data is fully migrated).
QuantaVerse’s AI solutions help banks radically improve their AML efforts. The QuantaVerse solutions have been proven to:
- Reduce false positives by approximately 40 percent
- Identify instances of false negatives in 100 percent of AML programs QuantaVerse has reviewed
- Eliminate more than 70 percent of investigative efforts through automation
- Help customers cut investigation costs by 20 to 40 percent
Click here to schedule a demo of QuantaVerse AI solutions.
CASE STUDY: How QuantaVerse’s AI Tech Helped a Forward-Thinking Commercial Bank Cut Costs While Reducing False Positives
Financial institutions have for years banked on rules-based transaction monitoring systems (TMS) to root out money laundering and other financial crimes, only to be served up copious false positives that result in paralyzing inefficiencies, runaway investigation costs, and unseen false negatives that represent serious risk to the institution.
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.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?