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, 27 companies were required to pay a paralyzing $2.48 billion in fines and penalties to resolve U.S. Foreign Corrupt Practice Act (FCPA) cases in 2016, making it the biggest enforcement year in FCPA history.
Accentuating the role of American businesses as government partners in the fight against global corruption, Deputy Attorney General Rod J. Rosenstein recently announced that the Pilot Program established by the United States Department of Justice in 2016 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 data science such as 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, such as our ‘CAE Checkup’ offering, allow organizations to witness first-hand how AI analysis can drastically improve their audit processes and outcomes. Our ‘CAE Checkup’ leverages the QuantaVerse AI Financial Crime Platform to analyze enterprise data and more efficiently and effectively identify insider threats, bribery, corruption, money laundering, fraud, terrorism financing and third-party risks that traditional internal audit investigations routinely miss.
For instance, included in our ‘CAE Checkup’ are client-specific audit working papers and reports outlining the most serious identified anomalies, as well as a visual presentation so results can be interpreted quickly. The ‘CAE Checkup’also includes the supporting documentation needed to conduct further investigations, if indicated.
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. In a ‘CAE Checkup’ engagement, 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 equips U.S. businesses and financial institutions with the predictive insights and quality data necessary to stay a step ahead of global corruption.
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?