The Foreign Corrupt Practices Act (FCPA) is a critical area of concern for the Department of Justice (DOJ) and the Securities and Exchange Commission (SEC) as instances of corporate corruption and bribery continue to unleash destructive effects on law-abiding companies and consumers across the globe. In recent years, the Unites States government has dramatically intensified its efforts to enforce the provisions of FCPA.
The paralyzingly expensive penalties brought on by bribery, fraud, money laundering and other corporate corruption arising from FCPA enforcement actions continue to increase yearly. While violations for Bank Secrecy Act (BSA) and Anti-Money Laundering for banks totaled approximately $697 million in 2016, the cost of FCPA penalties are escalating at an even greater pace.
According to the FCPA Blog, $2.48 billion in penalties were assessed against 27 companies in 2016, making it the biggest enforcement year in FCPA history. The cost is too high for firms to ignore FCPA risks, which are often easily detectable through advanced data science and artificial intelligence (AI) technology.
By definition, FCPA makes it “unlawful to use mail or wires to pay, promise to pay, or authorize payment of money or anything of value to any person, directly or indirectly, to a foreign official to influence the foreign official in his/her official capacity, induce the foreign official to do or omit an act, or secure any improper advantage in order to obtain or retain business.” The law applies to domestic firms, foreign firms and persons who facilitate a corrupt payment within the United States.
Any U.S. firm that conducts any type of business touching a foreign country faces potential FCPA risk. From a small company in Pennsylvania shipping parts to Argentina, to a large Fortune 100 corporation obtaining a construction project in the Middle East, the risk lies with employees or agents of the firms paying a bribe or providing something of value in exchange for business, special consideration, etc.
Examples known and investigated by the QuantaVerse team include a freight forwarder and his agent paying a bribe to a customs official in Latin America to obtain the release of several containers of cargo to expedite local business.
The key to discovering hidden FCPA risk is the holistic analysis of company data. Some common red flags related to FCPA detection include, but are not limited to:
- Third Party/vendor reputational or relationship issues;
- Payments made to a charity to disguise payment purpose;
- Large front-loaded payments to vendors, agents, etc.;
- Payments in other currencies;
- Abnormalities in expense/travel reports to disguise payments;
- Invoice anomalies, and;
- Anomalies in OBI or transaction message fields.
Advanced data science techniques, including AI and machine learning, can assist compliance teams, audit teams, internal investigative teams, and consultants to detect FCPA red flags. By employing a set of AI agents to query a firm’s core accounting/finance system, travel/expense reporting system, trade finance data, third-party vendor lists, and internal e-mail systems, AI and machine learning solutions can extract actionable evidence from the data to detect and report instances of anomalous activity possibly related to potential FCPA violations.
Some of the data science and AI techniques used to identify FCPA risks include advanced entity resolution and verification, UBO analysis, deep Web analytics, NLP (Natural Language Processing) Web scraping, network analysis, volumes & values analysis, and more. With advanced data science, companies can prevent FCPA missteps by improving internal controls and providing feedback for corporate leadership with root cause analysis and process improvement.
Report: How Financial Firms Are Using Artificial Intelligence and Machine Learning to Meet AML Demands of Today and Tomorrow
It’s a well-known fact that the global pandemic caused a radical shift in consumer banking and payments behavior. What isn’t as obvious is how financial institutions responded behind the scenes. Fortunately, a new study helps shed light on the pandemic’s impact on the adoption of new technologies for anti-money laundering (AML) efforts.
Regulators and those handling compliance at covered institutions have long accepted the pitiful state of AML program efficacy, including: An estimated $2 trillion laundered through the global banking system annually 90+% of false positives coming from transaction...
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.