Banks Accelerate AI Deployment in the Cloud, Touting Efficiency and Sustainability Gains

The financial services sector is increasingly turning to the cloud to advance its artificial intelligence (AI) and machine learning (ML) ambitions. One recent example is Lloyds Banking Group’s move to migrate major AI workloads to Google Cloud’s Vertex AI platform, thus equipping the bank’s data science and AI teams with enhanced tools including large language models (LLMs) and generative AI (GenAI). Both of these technologies are hugely resource-intensive and demand modern data center architecture, making them an ideal fit for cloud-based ecosystems.

However, the migration isn’t just about accelerating organization-wide development and deployment of AI systems. It also highlights a growing focus on economic and environmental sustainability. Lloyds reported saving 27 tons of operational emissions just by moving a small fraction of its AI systems from on-premises infrastructure to the cloud. Major cloud platforms like Google’s can help organizations quantify not only their emission reductions, but also their performance gains. Indeed, cloud adoption is increasingly justified by both operational efficiency and environmental responsibility.

The continuing shift to cloud technologies as critical enablers of AI-driven digital transformation also facilitates the democratization of advanced AI tools. For instance, cloud platforms make advanced AI capabilities accessible to a much wider audience, driving broader innovation throughout the finance and fintech sectors. On the other hand, reliance on major cloud providers can also introduce new risks, a concern increasingly flagged by industry regulators. As more financial functions depend on a smaller number of large tech firms, there’s a new systemic challenge the industry must navigate with care.

Balancing risk and reward in the era of cloud AI

There’s far more to migrating AI workloads than lifting and shifting. The process demands a thoughtful and strategic approach to maximize benefits while proactively mitigating potential risks. That begins by carefully assessing provider fit and capabilities and evaluating them based on their specific AI/ML service offerings. That’s all the more important in a highly regulated industry like finance. To make the best decisions, fintechs should focus on tools that specialize in their use cases, such as fraud detection and risk modelling.

Moving sensitive financial data and AI models to the cloud makes robust governance and security a must. While enterprise-grade AI solutions typically offer far higher degrees of control, privacy, and security than their consumer-focused counterparts, they should never be taken for granted. As such, every cloud environment must meet and exceed global regulatory standards and enable continuous monitoring, data retention, model integrity, and identity and access management (IAM). Only then can fintechs keep ahead of the competition without adding unacceptable business risk.