The financial services industry is undergoing a transformation with the adoption of AI technologies. NVIDIA’s fourth annual State of AI in Financial Services Report provides insights into the current landscape and emerging trends for 2024.

The report is based on a survey of over 400 financial services professionals — including executives, data scientists, developers, engineers, and IT specialists — from around the world. This year’s results reveal the trends, challenges, and opportunities that define the state of AI in trading, banking, payments, and fintech.

The survey found that 91% of financial services companies are assessing AI or already using it in production to improve operational efficiency, identify new opportunities, and enhance customer experiences. Portfolio optimization, fraud detection, and risk management remain top AI use cases, while generative AI is popular with organizations keen to drive efficiencies.

Generative AI and Large Language Models
Reflecting a macro-trend seen across industries, large language models (LLMs) and generative AI have emerged as significant areas of interest for financial services companies. Over half of the survey respondents (55%) said they were actively seeking generative AI workflows for their companies.

Organizations are exploring generative AI and LLMs for applications ranging from marketing and sales — ad copy, email copy and content production — to synthetic data generation. Of these use cases, 37% of respondents showed interest in report generation, synthesis, and investment research to reduce repetitive manual work.

Customer experience and engagement was another sought-out use case, with a 34% response rate. This suggests that financial services institutions are exploring chatbots, virtual assistants, and recommendation systems to enhance the customer experience.

AI Across Departments and Disciplines
With 75% of survey respondents considering their organization’s AI capabilities to be industry-leading or middle-of-the-pack, financial services organizations are becoming more confident in their ability to build, deploy, and extract value from AI implementations.

The most popular uses for AI were in operations, risk and compliance, and marketing. Financial organizations use AI to automate manual processes, enhance data analysis, and inform investment decisions to improve operational efficiency.

They’re deploying AI to analyze vast amounts of data to enhance risk and compliance to identify suspicious activities and anomalous transaction patterns. They’re also using AI to analyze customer data to predict preferences and deliver personalized marketing campaigns, educational content, and targeted promotions.
When surveyed, 43% of financial services professionals indicated that AI had improved operational efficiency, while 42% felt it had helped their business build a competitive advantage.

The Number One Challenge
Previously, the main challenge reported was the recruitment of AI experts and data scientists. This year, a 30% increase in participants responded that data-related challenges were the primary concern. This includes data privacy challenges, data sovereignty, and data around the world governed by different oversight regulations.

Organizations are exploring new ways to streamline and protect data while ensuring regulatory compliance. These issues reflect the rigor of AI models, which require large, diverse datasets to train, as well as increasing regulatory scrutiny.

Recruiting and retaining AI experts remains a challenge, as do budget concerns. However, over 60% of respondents are planning to increase investment in computing infrastructure or optimizing workflows, indicating the importance of technology to build and deploy reliable AI to help overcome these barriers.

Increasing Investments
The survey results point to AI bringing greater efficiency to operations, personalization to customer engagements, and precision to investment decisions. Finance professionals agree; 86% of respondents reported a positive impact on revenue, while 82% noted a reduction in costs. Over half (51%) strongly agreed that AI will be important for business success, a 76% increase from last year.

With this outlook, 97% of companies plan to invest more in AI technologies in the near future. Focus areas include identifying additional AI use cases, optimizing AI workflows, and increasing infrastructure spending.
Executives are investing in AI infrastructure to improve productivity, enhance customer experiences, and increase revenues. To build and scale AI across the enterprise, financial services organizations need a comprehensive AI platform that empowers data scientists, quants, and developers to collaborate without obstacles.