Fuzzy algorithm-driven financial statement analysis and intelligent audit system design

The inherent ambiguity and rapid change in financial data make conventional rule-based methods employed in financial auditing ill-equipped. To keep pace with the ever-increasing complexity of financial statements, audit processes need to be more adaptable, transparent, and effective. To address a gap in the current auditing approaches, this paper introduces a novel framework based on FAHP. This methodology incorporates quantitative financial data with qualitative expert opinion to dynamically prioritize audit actions. The proposed approach overcomes the limitations of conventional models such as their inflexibility in the face of changing financial circumstances and industry insights by employing fuzzy logic to manage uncertainty and adjust audit priorities. According to the study’s findings, the FAHP-based approach increases audit accuracy and resource allocation while decreasing oversight and focusing on high-risk areas, thereby increasing efficiency. Empirical results reveal a 15% improvement in risk assessment accuracy, a 30% reduction in audit time, and a 20% reduction in oversight when compared to traditional methods. This model proposes an improved, more interpretable, and more effective method for contemporary financial auditing that enables risk-based decision-making in real time without sacrificing clarity or openness in the audit process.