Abstract
White-collar crime poses a significant challenge to the economic and social fabric of India, as it involves non-violent financial offenses committed by individuals or organizations in positions of trust and authority. This research paper critically examines the landscape of white-collar crime identification in India, shedding light on the types of offenses prevalent, the mechanisms employed for detection, and the complex challenges faced by law enforcement and regulatory bodies. The study begins with an exploration of the various types of white-collar crimes observed in the Indian context, including fraud, corruption, embezzlement, money laundering, insider trading, and cybercrimes. Real-life case studies are presented to illustrate these offenses and the methods employed by perpetrators. Detection mechanisms are a central focus of this paper, with an in-depth analysis of financial audits, forensic accounting techniques, and digital monitoring tools used to identify and investigate white-collar crimes. The role of technology, particularly artificial intelligence and data analytics, is examined in enhancing detection capabilities.
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