AI-Driven Knowledge Sharing in the Banking Sector: Challenges and Approaches

Digitalization is becoming increasingly relevant for banking, hence the necessity for a practical Knowledge Management (KM) system that can facilitate rapid innovation and ensure proper regulatory compliance. While there is growing recognition of KM as a strategic driver of performance, it is implemented in a fragmented way within financial institutions and limited by technology, organizational and governance barriers. This paper presents a Systematic Literature Review (SLR) following the PRISMA 2020 protocol, aiming to identify, evaluate and synthesize the academic literature published between 2020 and 2025 on the integration of AI and KM in the banking context. Forty primary studies were examined via narrative synthesis to address two research inquiries: the challenges of AI-driven knowledge sharing (RQ1) and AI-based methodologies and best practices that enhance knowledge management processes (RQ2). The findings identify three main challenge domains data and technology infrastructure, organizational and human factors, and governance, security, and ethics constraints and six main solution categories, including NLP, machine learning, knowledge graphs, expert systems, human-AI hybrid collaboration, and ethical AI governance frameworks. These findings lead to the idea that the use of AI improves the accessibility of knowledge, learning and creativity and its efficiency depends on the capacity of the appropriate KM, absorptive capacity and maturity of governance. The study offers a cohesive framework linking AI innovation to organizational knowledge performance within the finance sector.