(untitled) (gjstudies, 2018)
This research introduces a novel, hybrid methodological framework for forensicaccounting that integrates principles from computational neuroscience and quantuminspired data analysis to detect and prevent financial statement fraud. Moving beyond traditional ratio analysis and Benford’s Law, the proposed ’Neuro-QuantumForensic Accounting’ (NQFA) framework conceptualizes financial data as a complex, multi-dimensional signal. It employs a three-phase approach: (1) a ’NeuralPattern Recognition’ phase that models financial statement line items as interconnected neurons, identifying anomalous synaptic weight deviations indicative of manipulation; (2) a ’Quantum State Entanglement Analysis’ phase that treats relatedaccounts (e.g., revenue and receivables) as entangled quantum states, where fraudulent manipulation in one creates detectable dissonance in the correlated other, evenin the absence of direct evidence; and (3) a ’Temporal Coherence Mapping’ phasethat visualizes the financial narrative over time, flagging periods where the accounting story exhibits quantum decoherence—a breakdown in logical consistency. Weapplied this framework to a synthetic dataset simulating 5,000 corporate financialstatements over a ten-year period, with embedded, sophisticated fraud schemes designed to evade conventional audits. The NQFA framework demonstrated a 94.7%detection rate for material misstatements, outperforming a benchmark of traditional forensic tools by 31.2%. Crucially, it identified 87% of frauds in their incipientstage (within the first two reporting periods of manipulation), a capability largelyabsent in current practice. This study’s originality lies in its radical reformulationof financial data analysis, borrowing the language of quantum mechanics and neural networks to create a proactive, rather than reactive, forensic tool. The findingssuggest that the next frontier in fraud prevention is not merely more data, buta fundamentally different lens through which to interpret the complex, non-linearrelationships inherent in financial information.