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- Outdated technology that runs tedious keyword searches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
While not mandating that firms invest in technology to automate financial crime investigations, regulators are certainly encouraging it. They are noticing that advanced BSA/AML teams are using robotic process automation (RPA) bots to gather data for investigations. They are aware that those same firms are using machine learning to analyze huge data sets, identify patterns, and pinpoint where exceptions or anomalies exist.
How Artificial Intelligence Can Help Financial Institutions Put an End to Cartel’s Illegal Border Business Bonanza
Current estimates suggest that Latin American cartels who facilitate illegal U.S. border crossings net $400 million each month. These, and related windfalls, must be laundered through the financial system to facilitate other cartel business and fund the lavish lifestyles of the cartel kingpins and their senior management.
Automating High-Risk Entity Reviews to Reduce Errors, Improve Efficiencies, and Ensure CDD Compliance
Federal bank regulatory agencies require that financial institutions regularly review and segment all their customers based on risk. While the Customer Due Diligence (CDD) Rule, which amended BSA regulations in 2018, does not stipulate how often reviews should be...