Artificial Intelligence Techniques for E-commerce Enterprises Financial Risk Prediction Based on Hierarchical Auto-Associative Polynomial Convolutional Neural Network Model: Trends and Challenges

Abstract

The rapid growth of e-commerce platforms has significantly increased the complexity of financial risk prediction, requiring advanced computational methods to handle high-dimensional, nonlinear, and dynamic data. Traditional statistical approaches often fail to capture intricate relationships within modern digital transaction ecosystems, highlighting the need for intelligent and scalable solutions. This paper presents a comprehensive review of artificial intelligence techniques for financial risk prediction in e-commerce, with a focus on hierarchical auto-associative polynomial convolutional neural networks. The proposed framework integrates convolutional neural networks with autoencoder-based learning to extract meaningful representations from large-scale financial data. The inclusion of polynomial functions enables modeling of higher-order nonlinear relationships among financial variables. The hierarchical architecture facilitates multi-scale feature extraction, allowing detection of both micro-level transaction anomalies and macro-level systemic risks. This approach is applied across various domains, including fraud detection, credit risk assessment, liquidity analysis, and supply chain risk prediction. Optimization strategies such as adaptive learning algorithms, regularization, and attention mechanisms further enhance model performance and stability. Empirical evaluations demonstrate improved accuracy and robustness compared to traditional and baseline deep learning models. Despite its effectiveness, challenges remain in interpretability, scalability, and regulatory compliance. This review highlights key advancements and future directions for developing reliable and intelligent financial risk prediction systems in e-commerce environments.