Some of the key highlights from the discussion with anti-money laundering (AML) expert and Founder & CEO of QuantaVerse, David McLaughlin, include:
- Top Financial Crime Trends in 2017
- Cybersecurity and instances of cyberattacks, especially account takeovers and application fraud, were a major financial crime trend that spiked in 2017
- The industry is still suffering from bad results ($1 trillion in bribes, $2.5 trillion in money stolen and laundered through the financial system)
- The application of AI and machine learning into AML efforts was a trending topic in 2017
- Financial institutions are looking at how to improve efficiency and cutting costs while still reporting in a way that keeps regulators satisfied
- Individual accountability was ramped up on the enforcement equation with respect to FCPA and AML
- Top Issues to Tackle in 2018
- How to best operationalize AI and machine learning to fight financial crime within AML, audit, corruption and fraud efforts
- Some incorrectly view AI/machine learning as a black box; decisions that machine makes can be fully audited
- Regulators and legislators are starting to recognize that the existing capabilities of AML programs are ineffective
- In testimony to the U.S. Senate Committee on Banking, the president of The Clearing House said, “one of the most pressing needs in enhancing the U.S. regime is to enable financial institutions to innovate their AML programs…and AI and machine learning could revolutionize this area”
Jurisdiction Derivation, Powered by AI, Helps Financial Institutions Reduce Risk and Their Number of AML Investigations
Financial institutions are held accountable by regulators to ensure they are taking a risk-based approach in their AML/BSA compliance operations. As such, institutions must consider AML risk based on certain types of customers and transactions, including risky jurisdictions impacted by political or economic unrest.
The AI-powered QuantaVerse Automated Volume and Value (V&V) Transaction Analysis solution provides risk managers with better insights into variances in account activity that might indicate risks of financial crimes, or that suggest an account is being used for something other than its stated purpose. Analysis of this nature is a growing regulatory burden driven by the expectation that FIs understand the risk profile of clients as well as their clients’ clients.
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.