Last year, 45 new FCPA-related investigations were publicly disclosed for the first time, making 2017 the most active year in history for new disclosures of FCPA-related investigations. Rewind one year and take into consideration 27 companies were required to pay a paralyzing $2.48 billion in fines and penalties to resolve FCPA cases in 2016, making it the biggest enforcement year in FCPA
These are but only two parts of a much larger force driving urgency around FCPA risk, which begs the question: “Why are current FCPA compliance efforts failing?”
We have dedicated this edition of On the Front Line with AI to discussing how corporations are adopting AI as part of their anti-bribery and corruption programs to mitigate FCPA risks.
Key highlights from the discussion with anti-money laundering (AML) expert and Founder & CEO of QuantaVerse, David McLaughlin, include:
- In recent years, the United States government has dramatically intensified its efforts to enforce the provisions of the Foreign Corrupt Practices Act (FCPA)
- There’s a historic increase in the number of companies under investigation and greater investigative resources are being deployed, including more FBI agents
- This sense of urgency goes beyond the U.S., as European regulators are following the U.S. model to crack down on corruption and are actively cooperating with U.S. enforcement agencies
The Problem with Current FCPA Programs and How They Can Be Improved with AI
- Current FCPA programs are ineffective and laborious because of their reliance on:
- Reports from whistleblowers
- Outdated technology that runs tedious keyword searches
- These programs are failing to find critical indicators of corruption such as accounting misappropriations
- The next evolution of FCPA is to leverage modern technologies and advancements in data science such as artificial intelligence (AI) and machine learning
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?