Generative AI in Banking: Early Use Cases and Emerging Risks
Early use cases for generative AI in banking are emerging alongside significant risks. A grounded assessment of where the technology adds value and where caution is warranted.
The arrival of generative AI in 2023 forced every bank to confront a simple question: what do we do with this? Global Banking Monitor's 55-page report provides an early but rigorous assessment of generative AI applications in banking - separating genuine opportunity from hype, and mapping the risk landscape that institutions must navigate.
Published at a moment when most banks were still formulating their response, the report offers a practical framework for identifying where to invest, where to proceed with caution, and where to avoid deployment altogether.
Key findings
- Three use case clusters are showing early promise: customer service automation, internal knowledge management, and developer productivity. Each offers measurable efficiency gains with manageable risk profiles.
- Productivity impact is real but often overstated. Early adopters report meaningful efficiency gains in specific workflows, but organisation-wide productivity transformation remains a longer-term prospect.
- The risk landscape is materially different from traditional AI. Hallucinations, data leakage through prompts, and the difficulty of auditing probabilistic outputs create novel challenges for compliance and risk teams.
- Governance frameworks have not kept pace with adoption. Most banks lack formal policies for generative AI use, creating shadow AI risks as employees adopt tools independently.
- Human-in-the-loop is not optional - it is essential. In regulated environments, fully autonomous generative AI outputs are not viable. The report outlines practical frameworks for maintaining human oversight without eliminating efficiency gains.
What the report covers
- Executive Summary - Early adoption trends and strategic implications
- What is Generative AI? - Core concepts and relevance to banking
- Early Use Cases - Customer service, knowledge management, and developer productivity
- Productivity Impact - Efficiency gains and workforce implications
- Risk Landscape - Hallucinations, data leakage, and compliance concerns
- Governance Models - Policies, controls, and human-in-the-loop frameworks
- Vendor Ecosystem - Technology providers and platform strategies
- Case Studies - Early adopters and lessons learned
- Strategic Considerations - Where to invest and where to avoid
- Outlook - Evolution over the next 2-3 years
Who should read this
This report is designed for CIOs, CTOs, Chief Data Officers, Heads of Innovation, and risk leaders navigating the early stages of generative AI adoption. It is equally relevant for board members and executive committees seeking to understand both the opportunity and the governance implications.
For enquiries about accessing this report, contact [email protected]
