The Applications and Challenges of AI in the Banking Sector

Abstract

This study systematically explores the current state of artificial intelligence (AI) applications across three core scenarios in the banking industry: intelligent customer service, risk management, and wealth management. Based on empirical case studies, AI technology has significantly reduced labor costs in the banking sector, enhanced the efficiency of business processes such as credit approval, and enabled ordinary customers to access professional financial advice. However, AI also faces several limitations, including insufficient levels of intelligence, a lack of interpretability in model decision-making processes, risks of customer data leakage, and suboptimal system stability. In the banking industry—a sector where operational accuracy and security are paramount—the risks arising from these issues are particularly pronounced compared to those in other industries. In summary, the deep integration of AI in the banking sector has become an inevitable trend; the key challenge lies in how to achieve its deployment in a robust and controllable manner. Feasible approaches include: at the technical level, adopting methods that integrate multiple optimization algorithms to enhance the generalization capability and robustness of models; at the business process level, establishing a hybrid decision-making mechanism that integrates human and machine capabilities; and at the institutional and regulatory level, urgently formulating and refining specific regulations and standards tailored for AI-driven financial applications. Only through such multi-dimensional collaborative governance can the application value of AI in the banking sector be maximized, while its potential risks are confined within acceptable limits.