Revolutionizing B2B E-Commerce: A Legal Study on the Transformative Impact of Hybrid AI Integration

The rapid expansion of B2B e-commerce has introduced new challenges in managing legal risks, ensuring contract compliance, and resolving disputes efficiently. This paper presents a novel Hybrid Model Integrating Transformer-based Architecture (HMTBA) using BERT and Variational AutoEncoder (VAE) for enhanced legal risk prediction in B2B e-commerce. The proposed hybrid deep learning model addresses the growing complexities in contract compliance, legal disputes, and transactional analysis by combining the strengths of BERT for textual understanding and VAE for dimensionality reduction of high-dimensional structured data. The model was evaluated against nine existing models, including CNN, RNN, GRU, LSTM, Transformer, XGBoost, BERT, VAE, and Hybrid CNN-LSTM, using a dataset of 4318 data points consisting of B2B transactions, legal case studies, contracts, and compliance reports. The proposed HMTBA model achieved superior performance with an accuracy of 95.82%, precision of 93.72%, recall of 94.35%, and F1-score of 94.03%. This significant improvement over existing models demonstrates the potential of HMTBA to enhance legal risk management and improve contract compliance in B2B e-commerce. The results also show that despite higher computational demands, the accuracy and robustness of the proposed model offer a transformative solution for legal analysis and compliance monitoring in e-commerce platforms.