In many parts of the world, acts of bribery and corruption have long been accepted as the cost of doing business. However, global law enforcement is becoming increasingly cooperative in investigating these violations as countries realize how massive amounts of their sovereign wealth is stolen from their economies through these crimes.
In fact, 14 companies were required to pay a paralyzing $2.9 billion in fines and penalties to resolve U.S. Foreign Corrupt Practice Act (FCPA) cases in 2019.
Accentuating the role of American businesses as government partners in the fight against global corruption, a pilot program was established by the United States Department of Justice to incentivize companies to self-report violations of the Foreign Corrupt Practices Act (FCPA) will be made permanent with certain revisions.
The revisions are designed to encourage American businesses to be more proactive in the fight against global corruption. Revisions such as voluntary self-disclosure and full cooperation during the investigative process incentivizes companies to do a better job of monitoring the effectiveness of their internal controls and compliance programs.
While organizations and individuals who self-report to the DOJ and SEC can reduce their risk of violations, the responsibility of protecting their organization from financial criminals falls on not only the compliance and legal departments, but the internal audit investigative teams as well.
Therefore, it is time for legislators, regulators, and covered institutions to step up and adopt modern technologies in the fight against global financial crime, such as fraud, bribery, corruption, and money laundering linked to human trafficking, drug smuggling and the funding of terrorism.
Given the technology historically available to them, Chief Audit Executives (CAEs) and internal audit teams had to limit their investigations to representative samples of data and conduct a manual search for abnormalities or anomalous activity within that data. These representative samples require the development of a time-consuming sampling methodology, which coupled with smaller data sets, can lead to unnecessary risks and potentially missed financial crimes.
Modern technologies and advancements in artificial intelligence (AI) and machine learning were born to solve this problem. AI systems have progressed to the point where large volumes of transactional data (think expense reports and email) can be culled, consolidated, analyzed, and scored for risk so suspicious activities can be identified and investigators can make faster and more accurate determinations.
AI- and machine learning-based solutions allow organizations to witness first-hand how AI analysis can drastically improve their audit processes and outcomes. With the QuantaVerse AI Financial Crime Platform, enterprise data is analyzed more efficiently and effectively to identify insider threats, bribery, corruption, money laundering, fraud, terrorism financing and third-party risks that traditional internal audit investigations routinely miss.
To put the benefits of AI analysis into perspective, let’s consider the following case study:
If an internal team was tasked to audit an international electronics company’s line of business (LOB) with Uzbekistan, the team would manually review 150 travel and expense reports for anomalies, one month’s worth of core accounting system records of financial transfers to/from Uzbekistan, and 30 days of vendor payments as they relate to possible FCPA or other insider threat or corruption risk. The internal audit team might identify 1-2 cases in which employees submitted questionable travel expense reports. The internal audit team could have leveraged AI to examine thousands of combined data points to holistically screen all LOB data related to Uzbekistan for known and unknown financial crime red flags truly worthy of their attention.
The key takeaway for audit leaders is that financial crimes are an extremely complex issue that continue to escalate in sophistication. Financial crimes take place within our corporations and financial institutions, and, if left unaddressed, pose significant operational, financial, legal, regulatory, and reputational risks. AI-based solutions equip U.S. businesses and financial institutions with the predictive insights and quality data necessary to stay a step ahead of global corruption.
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...
On January 1, 2021, the Anti-Money Laundering Act (“AMLA”) was enacted by Congress as part of the National Defense Authorization Act (NDAA). The AML Act has made emerging technologies, such as AI, machine learning, and quantum information sciences, a national...
Disruptive technologies are no longer the stuff of science fiction. They’re not even the “next big thing.” Successful firms are using them right now to automate manual processes in their AML programs.