There are more than one trillion U.S. dollars are estimated to be involved in worldwide acts of bribery and political corruption each year. These crimes impede developing nations from achieving stability, recovering from disasters and realizing measurable growth by siphoning off foreign aid and stealing national revenue.
Undiscovered instances of corporate corruption and bribery also unleash destructive effects on otherwise law-abiding Western organizations. In recent years, the United States government has dramatically intensified its efforts to enforce the provisions of the Foreign Corrupt Practices Act (FCPA). Nearly $2.5 billion in penalties were assessed in 2016, making it the biggest enforcement year in FCPA history. This cost is too high for global corporations to ignore.
Modern technologies and advancements in data science such as artificial intelligence (AI) and machine learning are well suited to solve this problem. AI-based systems have progressed to the point where large volumes of transactional data from enterprise accounting and email systems can be culled, consolidated, analyzed, and scored for risk so suspicious activities can be identified and compliance teams can make faster, more accurate determinations.
AI-based solutions, such as those offered by QuantaVerse, can easily analyze massive amounts of corporate financial data, discern patterns, and quickly identify where exceptions or anomalies exist that can unveil FCPA risks.
As U.S. corporations engage in imports/exports, foreign transactions, and related business deals, there is potential downstream FCPA and UK Bribery Act risk at every juncture. AI and other data analysis can efficiently assess FCPA potential risk to ensure no hidden risks exist, speed up identification of anomalous behavior and make an ABC compliance program more proactive than reactive.
Click here to read our full paper that discusses the integration of AI into anti-bribery and corruption programs to mitigate FCPA risk.
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.