AI-driven Risk Management in Banking and Insurance: A Conceptual Perspective

ABSTRACT Artificial intelligence (AI) is increasingly transforming how financial institutions perceive and manage risk. Banks and insurers face a risk environment that is not only broader but also more dynamic than ever, spanning financial, operational, cyber, and reputational domains. This paper develops a conceptual understanding of how AI-driven approaches—including machine learning, predictive analytics, and natural language processing—reshape risk identification, assessment, and mitigation. Building on risk management theory, institutional perspectives, and the resource-based view (RBV), we propose a framework that explains how AI enhances resilience by improving detection, enabling automated responses, and supporting adaptive governance. We also emphasize that the benefits of AI are contingent on strong oversight, transparency, and regulatory alignment. The study concludes with managerial implications for practitioners and policy directions for regulators, along with research opportunities in emerging markets and sector-specific contexts. Keywords: Artificial Intelligence, Risk Management, Banking, Insurance, Machine Learning, Corporate Resilience, Governance