Optimization and Practice of Isolation Forest Algorithm in Financial Anomaly Detection Driven by Computer Algorithms

The rapid growth of high-dimensional financial data and evolving fraud patterns pose significant challenges to traditional anomaly detection methods, which struggle with redundancy, noise, and computational inefficiency. This paper enhances the Isolation Forest algorithm through three optimizations: PCA dimensionality reduction eliminates noise while retaining key features; adaptive sampling dynamically adjusts to imbalanced data distributions; and parallel computing accelerates large-scale processing. Experiments show the optimized model achieves 11.2% higher F1-score and 8.3% precision gains on financial datasets, with 40% faster execution via parallelization. It maintains robust performance under 30% noise levels and adapts effectively to diverse financial scenarios. The framework provides a computationally efficient solution for real-world fraud detection systems, balancing accuracy and scalability in dynamic financial environments.