A Comprehensive Review of E-commerce Enterprises Financial Risk Prediction Based on Hierarchical Auto-Associative Polynomial Convolutional Neural Network Model

Abstract

The rapid growth of e-commerce platforms has significantly increased the complexity of financial risk assessment, making accurate prediction essential for enterprise stability and decision-making. Traditional statistical models often fail to capture nonlinear, high-dimensional, and dynamic patterns present in modern financial data, highlighting the need for advanced deep learning approaches.

This paper presents a comprehensive review of financial risk prediction techniques, focusing on hierarchical auto-associative polynomial convolutional neural networks (CNNs) for e-commerce applications. The proposed framework combines hierarchical feature learning with autoencoder-based unsupervised representation, enabling the extraction of meaningful patterns from large-scale transactional data even with limited labeled samples.

The integration of polynomial convolutional operations allows the model to capture higher-order feature interactions, improving its ability to model complex nonlinear relationships within financial datasets. This architecture effectively addresses challenges such as data imbalance, temporal variability, and heterogeneous feature distributions.

Empirical evaluations on benchmark datasets, including Lending Club and UCI credit risk datasets, demonstrate improved predictive performance over traditional methods and standard CNN models, particularly in terms of classification accuracy and AUC scores.

Despite these advancements, challenges such as model complexity and real-world deployment remain. This review highlights key innovations and provides insights into future directions for developing robust, scalable, and intelligent financial risk prediction systems in e-commerce environments.