David McLaughlin: How AI can nail a bribe paying scrap metal dealer
Wednesday, October 25, 2017 at 8:08AM
David McLaughlin in Artificial Intelligence, NLP, beneficial owners, internal controls

Last year 27 companies paid nearly $2.5 billion to resolve FCPA-related offenses. So far this year, 9 companies have paid more than $1.2 billion for FCPA-related settlements. Yet despite these staggering outcomes, existing tools deployed today by corporations are not capable of effectively mitigating FCPA risk.

Many solutions are extremely outdated, time-consuming, or lack the functionality to detect potential threats.

Traditional surveillance solutions have been found to be deficient in:

The next evolution in protecting against FCPA risk is to leverage advancements in artificial intelligence (AI), machine learning, and data analytics.

These new technologies can enable anti-bribery and anti-corruption teams, compliance staff, audit teams, internal investigators, and consultants to detect potential FCPA misconduct.

By employing a set of AI agents to query a firm’s core accounting/finance system, travel/expense reporting system, trade finance data, third-party vendor lists, and internal e-mail systems, an AI solution can extract actionable evidence from the data to detect and report instances of anomalous activity related to potential FCPA risks.

Some of the AI techniques used to identify FCPA risks include advanced entity resolution and verification, Ultimate Beneficial Owner analysis, deep Web analytics, NLP (Natural Language Processing) Web scraping, network analysis, and volumes and values analysis.

Here's how AI can work. A U.S.-based scrap metal recycling company transacts business with a steel manufacturer based in India. Analysis of the scrap metal recycling company’s employee travel and expense reports found anomalies associated with one employee as compared to the activity of similar employees.

Natural Language Processing analysis then identified a number of keywords in the employee’s emails and texts that indicated guarantees and suspicious foreign payments.

It was established that the employee was making improper payments and promises to the India-based steel company to induce them to purchase scrap metal from the U.S. company.

For responsible corporations serious about detecting potential FCPA issues, advanced AI and data analytics can assist the leadership in conducting root cause analysis to determine how the suspected corruption or bribery occurred, assess the effectiveness of its anti-bribery and anti-corruption compliance program, and identify internal controls that work and those that don't work.

Through the power of an AI-based risk mitigation solution, companies can easily analyze massive amounts of corporate financial data, discern patterns, and quickly identify where exceptions or anomalies exist that can unveil FCPA risks.

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David McLaughlin, pictured above, is CEO and Founder of QuantaVerse, an emerging leader in artificial intelligence (AI) and machine learning solutions, purpose-built for the identification of financial crimes. He can be contacted here.

Article originally appeared on The FCPA Blog (http://www.fcpablog.com/).
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