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”
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.
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?