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This report contains guidance from the Coalition for Integrity on Using Machine Learning for Anti-Corruption Risk and Compliance and discusses how data analytics and machine learning can prove indispensable in combing through massive quantities of financial and non-financial data to identify potential patterns of corrupt conduct. The report is intended to provide companies in multiple sectors with guidance on what they should consider in developing or acquiring anti-corruption machine learning.



In every region of the world, companies of all types need to pay close attention to the perennial problem of corruption and how to avoid becoming entangled in it. While media reporting often focuses on cases in which major companies pay substantial criminal and civil penalties to resolve investigations into bribery of foreign government officials, bribery and corruption risks are by no means limited to transnational activities and senior officials. Companies in the United States and other countries may also encounter corruption risks from domestic officials at all levels of government, from within their own ranks, and in some cases, even from their competitors.

Moreover, the prevention of corruption is a societal interest “of the highest importance.” From a public-policy standpoint, it is important for companies to have effective anti-corruption programs not only to reduce their own risks but collectively to play a role with the public sector in limiting the pernicious effects of corruption.

To maintain effective anti-corruption programs, companies must be attentive to a variety of risk and compliance obligations. As reflected in guidance by the U.S. Department of Justice and the Securities and Exchange Commission (SEC), these obligations include establishing and maintaining a risk-assessment process that timely identifies potential bribery and corruption concerns; overseeing and monitoring gifts, travel, and entertainment expenses by employees; making and tracking charitable and political donations; and conducting due diligence on and engaging third parties to handle outsourced functions or provide various products and services.



Using Machine Learning for Anti-Corruption Risk


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