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.
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?