Research on the Application Effect of Artificial Intelligence Algorithms in the Detection of Financial Fraud in Listed Companies

Abstract

Accurate detection of financial fraud remains a critical challenge due to information asymmetry, high-dimensional data complexity, and evolving fraudulent behaviors. This study develops an artificial-intelligence-based financial fraud identification framework for listed companies by integrating multiple machine-learning algorithms. A financial indicator database covering profitability, solvency, cash flow, and governance characteristics is first constructed. Principal Component Analysis is employed to reduce dimensionality and eliminate multicollinearity, followed by the training of Random Forest, Support Vector Machine, and Neural Network models. A fusion-learning strategy with cross-validation optimization is further introduced to enhance model robustness and classification performance. Experimental results demonstrate that the fusion model achieves an accuracy of 94.7%, outperforming individual RF (83.2%), SVM (85.6%), and NN (89.1%) models while maintaining a false-positive rate below 5%. The proposed framework provides effective support for intelligent risk warning and financial supervision and offers methodological references for pattern recognition, data fusion, and intelligent decision-making systems.