NVIDIA’s State of AI In Financial Services Survey, reveals how AI is revolutionising financial services, driving revenue growth, cost savings, and innovation. With 70% of firms reporting AI-driven revenue boosts and 60% cutting costs, financial institutions are moving from experimentation to full-scale adoption, leveraging generative AI, trading optimisation, and automation for a competitive edge.
A recent survey by NVIDIA, titled State of AI in Financial Services, reveals that the financial sector is achieving significant milestones with artificial intelligence (AI). Companies are now moving beyond experimentation to successfully implement AI-driven solutions, resulting in increased revenue, reduced costs, and the creation of new business opportunities.
AI trends in financial services in 2023
Enterprises across industries are accelerating innovation and investment in artificial intelligence (AI) and machine learning (ML), and financial services is no exception. But how are banks, asset managers, insurers, and fintech firms leveraging these advanced technologies, and what impact are they having on business operations?
While NVIDIA collaborates with the entire financial ecosystem—from global banks to fintech startups—to develop AI-powered applications for thousands of use cases, it is essential to understand industry-wide perspectives to maximise AI’s potential.
To achieve this, NVIDIA conducted its third annual “State of AI in Financial Services” survey, gathering insights from approximately 500 financial services professionals worldwide about the latest trends, challenges, and opportunities in AI, ML, and accelerated computing.
The survey results highlight four significant developments in banking, insurance, asset management, and fintech:
- As the global economic landscape faces macroeconomic challenges, financial institutions are turning to AI-driven solutions to enhance risk assessment, streamline operations, and reduce costs.
- Firms are deploying AI-enabled applications at a faster pace. Over 20% of respondents’ companies have integrated AI use cases, and the number of firms considering themselves laggards in AI adoption has declined significantly compared to the previous year.
- Recruiting and retaining data scientists has emerged as the biggest challenge to achieving AI objectives. Demand for skilled AI professionals remains exceptionally high.
- Almost half of AI projects now operate on a hybrid infrastructure, making data portability, MLOps management, and software standardisation across cloud and on-premises environments a strategic priority.
AI drives revenue growth and cost efficiency
Financial institutions investing in AI are reaping measurable benefits. According to the survey, nearly 70% of respondents reported a revenue increase of 5% or more, with a notable rise in those experiencing a 10-20% boost. Additionally, over 60% of respondents stated that AI has helped reduce annual costs by at least 5%.
Around a quarter of the surveyed companies are planning to leverage AI to unlock new revenue streams and business opportunities. The most impactful use cases for generative AI, in terms of return on investment (ROI), include trading and portfolio optimisation (25% of responses) and customer experience and engagement (21%). These findings underscore the transformative role of AI in key business areas, delivering tangible financial gains.
Overcoming challenges in AI adoption
Overcoming challenges in AI adoptionThe survey highlights a significant shift in the industry’s approach to AI adoption. Half of the management respondents confirmed they have deployed their first generative AI service or application, with an additional 28% planning to do so within the next six months. Furthermore, the number of respondents citing a lack of AI budget has decreased by 50%, indicating a stronger commitment to AI development and resource allocation.
Early barriers to AI adoption, such as data issues, privacy concerns, and insufficient data for model training, are also diminishing. These improvements reflect the industry’s growing expertise and enhanced data management practices, enabling organisations to harness AI more effectively.
Generative AI expands its reach
Generative AI has emerged as the second-most-used AI workload in financial services, following data analytics. Its applications have expanded significantly, ranging from improving customer experience to optimising trading and portfolio management.
The use of generative AI for customer experience, particularly through chatbots and virtual assistants, has more than doubled, rising from 25% to 60%. This growth is driven by the increasing availability, cost efficiency, and scalability of generative AI technologies, which enable more sophisticated and accurate digital assistants to enhance customer interactions.
Over half of the financial professionals surveyed are now using generative AI to improve the speed and accuracy of critical tasks, such as document processing and report generation.
The rise of agentic AI
Financial institutions are also exploring the potential of agentic AI—systems that utilise vast amounts of data from diverse sources and employ advanced reasoning to autonomously solve complex, multi-step problems. Banks and asset managers can leverage agentic AI to enhance risk management, automate compliance processes, optimise investment strategies, and personalise customer services.
Building AI factories for innovation
Recognising the transformative potential of AI, companies are proactively investing in AI factories—specially designed accelerated computing platforms equipped with full-stack AI software. These platforms can be deployed through cloud providers or on-premises and are crucial for implementing high-value AI use cases.
By leveraging advanced infrastructure and software, financial institutions can streamline the development and deployment of AI models, positioning themselves to harness the power of agentic AI and other cutting-edge technologies.
A promising future for AI in finance
With industry leaders predicting at least a twofold return on investment (ROI) from AI initiatives, financial institutions remain highly motivated to implement their highest-value AI use cases. As organisations continue to allocate budgets and refine their data management practices, they are well-positioned to drive operational efficiency, enhance security, and foster innovation across all business functions.
The financial services industry’s embrace of AI marks a pivotal moment in its technological evolution, paving the way for sustained growth and innovation in the years to come.
Himani Verma is a seasoned content writer and SEO expert, with experience in digital media. She has held various senior writing positions at enterprises like CloudTDMS (Synthetic Data Factory), Barrownz Group, and ATZA. Himani has also been Editorial Writer at Hindustan Time, a leading Indian English language news platform. She excels in content creation, proofreading, and editing, ensuring that every piece is polished and impactful. Her expertise in crafting SEO-friendly content for multiple verticals of businesses, including technology, healthcare, finance, sports, innovation, and more.
