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.
While this is debated, the problem persists as legacy AML technology such as transaction monitoring systems (TMS) have little to no ability to identify and assess risk created by shell companies. And while policies, procedures, and processes, if applied correctly, can protect financial institutions from becoming conduits for some fraction of money laundering, terrorist financing, and other financial crimes, identifying shell company risk continues to be elusive.
On the final day of the FIBA conference, QuantaVerse Founder and CEO, David McLaughlin, participated on the “Customer Profiling, Use of Innovative Technologies in Onboarding and Risk Assessment” panel. David Schwartz, President and CEO of FIBA, set up the panel discussion by emphasizing the importance and impact that innovative technologies have on risk assessment and the customer onboarding process.
CASE STUDY: AI-Powered Entity and Alert Adjudication Streamlines Financial Crime Investigation Processes
Based on these outcomes, the bank is moving QuantaVerse AI solutions into production. Moving forward, the bank will be better equipped to find hidden financial crime risk while automating 70 percent of its AML investigation processes, enabling investigators to focus their time and talents on the most complex cases.