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- Outdated technology that runs tedious keyword searches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
Prior to the COVID-19 outbreak, the 2020 outlook was an encouraging one as the year was shaping up to be positive for many industries. At the mid-year point of 2020, the world has changed, and we continue to observe the pandemic’s impact on our communities, economy,...
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.