NVIDIA’s sixth annual report, State of AI in Financial Services, shows that AI adoption across the Financial sector has reached its highest level to date, signaling a shift from experimentation to scaled deployment.
The report draws on responses from more than 800 financial services professionals and finds that 65% of organizations are actively using AI, up from 45% the previous year. Generative AI adoption has accelerated as well, with 61% of respondents saying they are using or assessing it, representing a 52% year-over-year increase. These gains reflect broader organizational confidence in AI systems that can be integrated into production environments and tied to measurable business outcomes.
Respondents report clear financial impact. Nearly nine in ten said AI has contributed to increased revenue and reduced costs, with 64% reporting revenue gains above 5% and 29% exceeding 10%. On the cost side, 61% reported reductions of more than 5%. Document processing, customer engagement, algorithmic trading, and risk management were cited among the most consistent sources of return. Operational efficiency and employee productivity emerged as the most common areas of improvement, underscoring AI’s role in automating high-volume, time-sensitive workflows.
The report also highlights growing interest in agentic AI, systems designed to autonomously reason and act toward defined objectives. Forty-two percent of respondents said they are using or assessing agentic AI, while 21% have already deployed AI agents in production. These systems are being applied to areas such as payments, where real-time decision-making and optimization can directly affect authorization rates and revenue performance.
Open source models and software play an increasingly central role in enterprise AI strategies. Eighty-four percent of respondents said open source is important to their AI approach, with organizations citing flexibility, cost control, and reduced vendor lock-in as key advantages. Fine-tuning open source models on proprietary data is emerging as a primary method for building differentiated capabilities, though respondents acknowledged that proprietary models may still outperform in highly specialized domains. As a result, many institutions are maintaining hybrid strategies that match model types to specific workloads and risk profiles.
Rising confidence in AI performance is reflected in budget planning. Nearly all respondents said their AI budgets will increase or remain steady over the next year. Investment priorities include optimizing existing AI workflows, expanding successful use cases across the organization, and building out supporting infrastructure in cloud and on-premises environments. Continued spending on AI agents is also expected, as institutions look to automate increasingly complex processes while maintaining governance and reliability.
Taken together, the findings suggest that financial services firms are entering a more mature phase of AI adoption, focused less on proving feasibility and more on scaling systems that deliver consistent operational and financial results.