Factors analysis of commercial bank digital transformation with random neural network algorithm

With the deepening of global economic and technological integration, digital transformation has emerged as a key factor for enhancing operational efficiency, improving customer experience, and reshaping the strategic and organizational frameworks of commercial banks. Existing literature often treats digital transformation as a derivative process influenced by external technological trends. This paper argues that digital transformation is an original and strategic operation that requires user-centric design and adaptive technological integration. By leveraging an independently developed digital platform, and employing artificial intelligence algorithms and optimization models, this study constructs a comprehensive model to analyze the key factors influencing a bank’s digital transformation from the perspective of considering user-platform interaction. Experimental results show that, in terms of interpretability, the proposed model demonstrates a 9%/7% improvement in $R^2$ and adjusted $R^2$, respectively. In terms of prediction, the proposed model shows a clear advantage, providing more accurate and stable results. The findings from this study offer a scientifically grounded reference for commercial banks, enabling them to better understand and address the challenges and opportunities associated with digital transformation, ultimately leading to more efficient and sustainable development.