Advanced AI in Modern Banking: Strategy, Risk Management, and Operational Resilience

This Research Topic investigates the transformative role of advanced artificial intelligence in modern banking, focusing on strategic applications in credit decisioning, customer analytics, process automation, stress testing, model governance, and model risk management. While prior research has addressed classical AI approaches to risk assessment, fraud detection, and regulatory compliance, this collection moves the conversation forward by examining how privacy-preserving methods, agentic AI systems, and blockchain-enabled infrastructures change what is operationally possible across institutions. Particular emphasis is placed on GenAI copilots that strengthen operational resilience and regulatory adherence, and on cross-institutional collaboration that does not require direct data sharing. The aim is to advance AI applications that not only optimize banking operations and risk frameworks but also foster sustainable finance and autonomous agentic systems with robust privacy safeguards.The financial services industry faces increasing demands for strategic innovation, risk management, and operational resilience amid growing complexity and regulatory scrutiny. This Research Topic aims to explore how advanced AI technologies, including generative AI, agentic systems, privacy-preserving methods, and blockchain integration, can reshape modern banking at the operational and strategic level. The focus is on enhancing credit decisioning, customer analytics, process automation, and model governance through approaches that classical AI methods cannot easily support, such as federated model development across institutions and autonomous workflow execution under strict privacy constraints. The goal is to foster transparent, secure, and decentralized financial infrastructures that improve operational efficiency, regulatory compliance, and sustainable finance, while enabling autonomous AI systems to manage complex banking tasks with robust privacy and risk safeguards.This Research Topic invites contributions on a broad range of advanced AI applications in modern banking, with particular focus on strategy, risk management, and operational resilience. We are particularly interested in submissions that explore, but are not limited to, the following areas:- Privacy-preserving and agentic approaches to credit decisioning, including federated learning and alternative data.- AI across core banking functions: customer analytics, process automation, stress testing, model governance, MRM, regulatory compliance, GenAI copilots, and operational resilience.- Intelligent banking applications using conversational interfaces, automated compliance, and agentic AI for autonomous financial workflows.- Cross-institutional fraud detection using federated graph neural networks, differential privacy, and secure multi-party computation.- AI for climate finance and ESG integration, including standardized ratings, risk quantification, and automated sustainability reporting.- Privacy-preserving AI methods that enable collaborative model development across institutions without data exposure.- AI and blockchain convergence for decentralized financial systems, verified AI decisions, and immutable audit trails.We welcome original research, review articles, and case studies that present novel AI methodologies, as well as practical applications that advance operational efficiency, regulatory adherence, sustainable finance, and the secure deployment of autonomous AI systems in banking. Topic editors: Asma Cherif, Maha Driss, Anna Visvizi Source: https://www.frontiersin.org/research-topics/79593/advanced-ai-in-modern-banking-strategy-risk-management-and-operational-resilience