Generative Artificial Intelligence in Banking: Risk Management Frameworks for Responsible Deployment

Abstract

By means of improved customer interactions, automated content generation, and simplified internal procedures, generative artificial intelligence is transforming banking activities. Financial institutions are using GenAI solutions more and more, including code generation tools, customized marketing campaigns, automated regulatory reporting, artificial data creation for model training, and intelligent virtual assistants. While allowing privacy-preserving data use, these solutions provide major operational efficiencies and customer experience enhancements. Adoption of GenAI, meanwhile, brings considerable dangers, including output hallucinations, algorithmic bias amplification, data security flaws, and legal compliance difficulties unique to highly controlled financial markets. Good GenAI integration calls for thorough governance frameworks including human monitoring mechanisms, prompt engineering standards, model validation protocols, and strong auditability systems. Companies need to define precise risk tolerance levels, use constant monitoring systems, and create GenAI-specific failure mode-tailored incident response plans. Future developments suggest evolution toward domain-specialized large language models, real-time workflow integration, and advanced federated learning designs. Regulatory frameworks keep changing to handle GenAI-specific risks while preserving financial system stability. Banks reaching their best GenAI value realization exhibit systematic risk management, cross-functional teamwork, and proactive engagement with changing regulatory requirements, thereby preparing themselves for competitive benefit and operational resilience.