NVIDIA Report: Financial Services Double Down on AI Investment

1/26/2026

Artificial intelligence has decisively taken center stage in the financial services sector, moving well beyond the experimental "pilot" phase to become a primary driver of profit and operational efficiency. The sixth annual "NVIDIA State of AI in Financial Services" report, released on January 22, 2026, and based on a survey of over 800 industry professionals, confirms that AI usage is at an all-time high. From automating the intricate research behind algorithmic trading to more accurately detecting fraud and money laundering, AI is reshaping the industry's core functions. Financial institutions are no longer just testing the waters; they are deploying scalable solutions that improve risk management, expedite document processing, and, most importantly, deliver a tangible and meaningful return on investment (ROI). https://blogs.nvidia.com/wp-content/uploads/2026/01/finance-question-21-importance-of-open-source-by-role-white-1536x372.png ROI is the Reality: Revenue Up, Costs Down The survey data paints a compelling picture of financial impact. A staggering 89% of respondents stated that AI is helping to increase annual revenue and decrease annual costs. The magnitude of this impact is significant: 64% of respondents reported a revenue increase of more than 5%, with nearly a third (29%) seeing gains exceeding 10%. On the cost side, 61% said AI helped decrease annual costs by more than 5%, with 25% of professionals reporting operational savings of more than 10%. https://blogs.nvidia.com/wp-content/uploads/2026/01/finance-question-19-impact-on-business-operations-industry-top3-white-1536x540.png This clear ROI is fueling rapid adoption. Active AI usage has jumped to 65%, up from 45% in last year's report. Generative AI adoption has surged by 52% year-over-year, with 61% of firms now using or assessing it. Consequently, 73% of executives view AI as crucial to their future success, and nearly 100% of respondents plan to increase or maintain their AI budgets in the coming year. Creating operational efficiencies was cited as the largest improvement by 52% of respondents, while 48% pointed to employee productivity as a major benefit. The Strategic Shift: Open Source and AI Agents Two major trends define this year's landscape: the embrace of open-source software and the rise of "agentic AI." 84% of respondents cited open-source models and software as important to their AI strategy, with 43% calling it "very to extremely important." Helen Yu, CEO of Tigon Advisory Corp., notes that open source is fundamentally changing competitive dynamics. She argues that the "real value capture" happens when institutions fine-tune these models on their proprietary transaction data, customer interaction histories, and market intelligence, creating unique AI capabilities that competitors cannot replicate. Alexandra Mousavizadeh, co-founder and co-CEO of Evident Insights, adds that while open source helps banks close the gap with early movers and safeguard against vendor lock-in, proprietary approaches can still unlock superior performance for domain-specific tasks. "Leading banks need to demonstrate proficiency in both approaches," she states. Simultaneously, AI agents—systems designed to autonomously reason, plan, and execute complex tasks—are gaining traction. 42% of firms are using or assessing agentic AI, with 21% saying they have already deployed them. Dwayne Gefferie, payments strategist at Gefferie Group, highlights the tangible ROI in payment operations: "Agentic AI systems can now autonomously route transactions to the most optimized payment networks, dynamically adjust retry logic... and make routing decisions under 200-millisecond routing that traditional rule-based systems simply can’t match." He emphasizes that every basis point improvement in authorization rates translates directly to revenue. Investing in the Future Success is breeding further investment. Given the shift from running proof of concepts to deploying AI-enabled applications into production, the industry is significantly expanding budgets. About 41% of investment is targeted at optimizing AI workflows and reinvesting in working solutions, while 34% is focused on identifying additional use cases for expansion. Infrastructure is also a priority, with 30% of investment flowing into building access to AI infrastructure, whether on-premises or in the cloud. As the industry doubles down, the focus remains on treating proprietary data as a strategic asset to build differentiated, high-value AI products.