Enhancing automated financial statement analysis using fuzzy logic algorithms

Financial statement analysis is crucial to organizational performance, but quantitative and rule-based methods often fail to capture nonlinear relationships, uncertainty, and rapidly changing market conditions. A hybrid ANFIS-QIGA-DFRL framework that integrates Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for interpretable reasoning, Quantum-Inspired Genetic Algorithms (QIGA) for efficient optimization of fuzzy rules and membership functions, and Deep Fuzzy Reinforcement Learning (DFRL) for continuous adaptive policy learning from real-time data is proposed to address these challenges On the CSMAR dataset of Chinese firms, the framework improves convergence time by 40%, mean squared error by 0.017, and prediction accuracy by 91.2%. Compared to ANFIS and ReNN-PSO, it has a policy stability score of 0.86 and decreases regret by over 70%. These findings demonstrate the model’s improved financial risk forecasting and decision support scalability, interpretability, and adaptability. The proposed approach uniquely balances optimization efficiency with dynamic learning, ensuring faster convergence and more reliability under uncertain financial situations than hybrid fuzzy and reinforcement learning systems. This study introduces a scalable, self-improving, and interpretable automated financial analysis tool for real-time health assessment, risk management, and investment strategy support in emerging markets.