The banking sector is vital in maintaining economic stability, with digital transformation increasingly powered by artificial intelligence (AI). Adopting AI enhances efficiency across front, middle, and back-office operations. However, it also presents significant challenges that require strategic management. This research explores the application of AI in banking risk management, focusing on credit risk assessment, fraud detection, and regulatory compliance. By utilizing technologies such as machine learning (ML), natural language processing (NLP), and deep learning, AI supports risk mitigation through real-time data analysis, anomaly detection, and predictive modeling. Despite the potential benefits, several barriers (e.g., ethical concerns, data privacy issues, and the limited interpretability of complex models) continue to hinder broader AI adoption. To explore these challenges, this research employs a mixed-methods approach, combining structured interviews and surveys with a systematic review of peer-reviewed literature to assess the current state of AI implementation in Armenia’s banking sector. The findings reveal AI’s transformative potential and implementation challenges, identifying key success factors for effective integration. Strategic recommendations are provided to guide financial institutions in optimizing AI deployment to strengthen risk management practices and promote financial system resilience.