Behavioral Analytics and Forensic Accounting: Understanding the Human Element in Fraud

Abstract

Financial fraud remains a persistent problem in the increasingly complex digital economy, requiring a paradigm shift beyond traditional forensic accounting. The study integrates behavioural analytics - leveraging natural language processing (NLP), machine learning, and sentiment analysis - to unravel the human drivers behind fraud in three landmark Enron, Wirecard, and FTX cases. The results show that behavioural indicators (e.g. evasive communication, CEO overconfidence) precede financial irregularities by 6 to 24 months, and machine learning models are 93 percent accurate in detecting fraud. In theory, we extend Cressey's fraud triangle to the behavioral-financial feedback loop, emphasizing how psychological rationalization and organizational culture interact with financial irregularities. In practice, the study shows the potential of AI to improve risk scoring, while also highlighting ethical trade-offs, such as the challenges of GDPR compliance in employee monitoring. Despite limitations, including survivorship bias and data availability limitations, the findings support a human-centric dashboard that integrates behavioral and financial metrics. Future research should give priority to longitudinal studies of artificial intelligence tools in real-world environments and cross-cultural analyses of fraud mitigation. This work provides a scalable framework for preventing fraud through cross-sectoral innovation, reconciling technological progress with ethical governance.