This research examines the effects of digital innovations, regulatory frameworks, and advanced risk management on banking system stability, alongside social media sentiment’s impact on electric vehicle companies’ financial choices. Employing a mixed-methods approach analyzing financial documents, licensure records, and social media data, the study uses econometric measurement and qualitative framework inspection. Sentiment analysis of electric vehicle data from 2022-2023, utilizing NLP tools, reveals an increasing positive attitude towards EVs, although security and infrastructure concerns remain. Key banking findings indicate that AI-driven risk assessment algorithms achieve an 89% measurement success rate, surpassing traditional methods at 72%. Furthermore, GPU acceleration in AI-based financial models improves execution efficiency by a factor of four. Sentiment analysis shows a strong positive correlation (0.85) between the sentiment index and market stability indicators. Digital Twin simulations demonstrate a 91% accuracy rate in forecasting financial crises, significantly higher than the 76% accuracy of historical models. The research suggests that banks implementing digital transformation and regulatory adaptability achieve stronger financial stability, with AI for risk management improving organizational performance. Policymakers should create adaptable fintech regulations and utilize robust risk control methods. Emerging tech firms and financial institutions should leverage sentiment trends for strategic planning. This research establishes novel connections between traditional finance risk management, digital transformation, and sentiment-driven analysis, offering a sustainable framework for stability and technology adoption, and proposes new methodologies for risk and consumer pattern analysis.
Rena et al. (Mon,) studied this question.