Application and Analysis in Machine Learning Algorithms for Financial Fraud Detection

Abstract

This study provides a comprehensive examination of machine learning models and deep learning algorithms for detecting financial fraud. We begin by introducing the basic concepts of commonly used algorithms, discussing their strengths and limitations in identifying fraudulent activities. The focus is on the unique capabilities of deep learning models, particularly Graph Neural Networks (GNNs), in handling large-scale and high-dimensional data, and their proficiency in enhancing fraud detection accuracy and efficiency through automated feature extraction. Furthermore, the study evaluates and contrasts the efficacy of various models in real-world scenarios, highlighting their performance in improving recall, precision, and overall robustness. We anticipate future advancements in fraud detection technology, including dynamic graph modeling and cross-domain transfer learning. Through detailed analysis and empirical validation, this study not only enhances our understanding of existing technologies but also offers valuable insights and guidance for research and practice in the field of financial security. The empirical results demonstrate that deep learning models, especially the Residual-Layered-Camouflage Graph Neural Network (RLC-GNN) model, significantly outperform traditional methods in terms of accuracy, recall, area under the curve (AUC) and F1 scores, indicating a promising direction for future developments in financial fraud detection.