Impact of Artificial Intelligence on the Detection and Prevention of White-Collar Crime

Abstract

AI is rapidly changing the face of white-collar crime detection and prevention. It offers some powerful new tools to combat the often-intricate offense. White-collar crimes, as a broad category of illicit activities, include financial fraud, insider trading, embezzlement, money laundering, and Ponzi schemes, and are often considered complex and very sophisticated in execution, making the traditional methods of investigation often insufficient. These crimes are often committed within organizational structures and across complex financial systems, and thus require analysis of a huge amount of data so as to find very subtle patterns of wrongdoing. With the use of advanced algorithms and machine learning techniques, AI-driven tools can revolutionize this field by offering unparalleled capabilities in the analysis of large datasets, identifying anomalies that would likely go unnoticed even by very skilled human analysts, and detecting subtle patterns that would be indicative of criminal activity. These tools can sift through huge volumes of data in financial transactions, communications, and other areas at a rate and accuracy unattainable by humans. Still, these are just bright promises that cannot yet be materialized in a practical application that will combat white-collar crime effectively (K. Singh et al., 2023).  This paper explores the current state of AI's usage in white-collar crime prevention by looking at its strengths and weaknesses, identifying important challenges that must be overcome for its successful application, and laying out critical research and development needs to unlock its full potential for the prevention of these pervasive and damaging crimes.