Forensic Accounting Practices for Detecting and Preventing Financial Statement Fraud

Abstract

This research introduces a novel, hybrid methodological framework for forensic
accounting 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-Quantum
Forensic Accounting’ (NQFA) framework conceptualizes financial data as a complex, multi-dimensional signal. It employs a three-phase approach: (1) a ’Neural
Pattern 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 related
accounts (e.g., revenue and receivables) as entangled quantum states, where fraudulent manipulation in one creates detectable dissonance in the correlated other, even
in the absence of direct evidence; and (3) a ’Temporal Coherence Mapping’ phase
that visualizes the financial narrative over time, flagging periods where the accounting story exhibits quantum decoherence—a breakdown in logical consistency. We
applied this framework to a synthetic dataset simulating 5,000 corporate financial
statements 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 incipient
stage (within the first two reporting periods of manipulation), a capability largely
absent in current practice. This study’s originality lies in its radical reformulation
of financial data analysis, borrowing the language of quantum mechanics and neural networks to create a proactive, rather than reactive, forensic tool. The findings
suggest that the next frontier in fraud prevention is not merely more data, but
a fundamentally different lens through which to interpret the complex, non-linear
relationships inherent in financial information.