Forensic Accounting 2.0: How AI is Revolutionizing the Fight Against White-Collar Crime

Abstract

This study explores the transformative role of artificial intelligence in modernizing forensic accounting to combat white-collar crime. As financial fraud grows in complexity and scale, traditional investigative methods face limitations in identifying subtle anomalies hidden within vast datasets. The purpose of this research is to assess how AI-driven tools enhance fraud detection and risk management in forensic accounting practices. By employing a mixed-method approach that integrates a systematic literature review, case study analyses, and empirical data from financial institutions, the study examines the effectiveness of machine learning algorithms, advanced data analytics, and automation in uncovering fraudulent activities. Findings indicate that AI significantly improves the identification of irregular patterns, accelerates the investigative process, and reduces operational costs. The evidence suggests that AI not only enhances the accuracy and efficiency of fraud detection but also enables a proactive stance against potential financial misconduct. In conclusion, the integration of AI into forensic accounting practices represents a pivotal advancement, providing a more robust framework for detecting and mitigating white-collar crime. The implications of this research advocate for the adoption of AI technologies by organizations to strengthen internal controls and compliance measures, while also informing policymakers on the need to update regulatory frameworks in line with technological progress.