Survey Reveals Financial Industry’s Top 4 AI Priorities for 2023
For several years, NVIDIA has been working with some of the world’s leading financial institutions to develop and execute a wide range of rapidly evolving AI strategies. For the past three years, we’ve asked them to tell us collectively what’s on the top of their minds. Sometimes the results are just what we thought they’d Read article >
For several years, NVIDIA has been working with some of the world’s leading financial institutions to develop and execute a wide range of rapidly evolving AI strategies. For the past three years, we’ve asked them to tell us collectively what’s on the top of their minds.
Sometimes the results are just what we thought they’d be, and other times they’re truly surprising. This year’s survey, conducted in a time of continued macroeconomic uncertainty, the results were a little of both.
From banking and fintech institutions to insurance and asset management firms, the goals remain the same — find ways to more accurately manage risk, enhance efficiencies to reduce operating costs, and improve experiences for clients and customers. By digging in deeper, we were able to learn which areas of AI are of most interest as well as a bit more.
Below are the top four findings we gleaned from our “State of AI in Financial Services: 2023 Trends” survey taken by nearly 500 global financial services professionals.
Hybrid Cloud Is Coming on Strong
Financial services firms, like other enterprises, are looking to optimize spending for AI training and inference — with the knowledge that sensitive data can’t be migrated to the cloud. To do so cost-effectively, they’re moving many of their compute-intensive workloads to the hybrid cloud.
This year’s survey found that nearly half of respondents’ firms are moving to the hybrid cloud to optimize AI performance and reduce costs. Recent announcements from leading cloud service providers and platforms reinforce this shift and make data portability, MLOps management and software standardization across cloud and on-prem instances a strategic imperative for cost and efficiency.
Large Language Models Top the List of AI Use Cases
The survey results, focused on companies based in the Americas and Europe, with a sample size of over 200, found the top AI use cases to be natural language processing and large language models (26%), recommender systems and next-best action (23%), portfolio optimization (23%) and fraud detection (22%). Emerging workloads for the metaverse, synthetic data generation and virtual worlds were also common.
Banks, trading firms and hedge funds are adopting these technologies to create personalized customer experiences. For example, Deutsche Bank recently announced a multi-year innovation partnership with NVIDIA to embed AI into financial services across use cases, including intelligent avatars, speech AI, fraud detection and risk management, to slash total cost of ownership by up to 80%. The bank plans to use NVIDIA Omniverse to build a 3D virtual avatar to help employees navigate internal systems and respond to HR-related questions.
Banks Seeing More Potential for AI to Grow Revenue
The survey found that AI is having a quantifiable impact on financial institutions. Nearly half of survey takers said that AI will help increase annual revenue for their organization by at least 10%. More than a third noted that AI will also help decrease annual costs by at least 10%.
Financial services professionals highlighted how AI has enhanced business operations — particularly improving customer experience (46%), creating operational efficiencies (35%) and reducing total cost of ownership (20%).
For example, computer vision and natural language processing are helping automate financial document analysis and claims processing, saving companies time, expenses and resources. AI also helps prevent fraud by enhancing anti-money laundering and know-your-customer processes, while recommenders create personalized digital experiences for a firm’s customers or clients.
The Biggest Obstacle: Recruiting and Retaining AI Talent
But there are challenges to achieving AI goals in the enterprise. Recruiting and retaining AI experts is the single biggest obstacle, a problem reported by 36% of survey takers. There is also inadequate technology to enable AI innovation, according to 28% of respondents.
Insufficient data sizes for model training and accuracy is another pressing issue noted by 26% of financial services professionals. This could be addressed through the use of generative AI to produce accurate synthetic financial data used to train AI models.
Executive Support for AI at New High
Despite the challenges, the future for AI in FSI is getting brighter. Increasing executive buy-in for AI is a new theme in the survey results. Some 64% of those surveyed noted that “my executive leadership team values and believes in AI,” compared with 36% a year ago. In addition, 58% said that “AI is important to my company’s future success,” up from 39% a year ago.
Financial institutions plan to continue building out enterprise AI in the future. This will include scaling up and scaling out AI infrastructure, including hardware, software and services.
Empowering data scientists, quants and developers while minimizing bottlenecks requires a sophisticated, full stack AI platform. Executives have seen the ROI of deploying AI-enabled applications. In 2023, these leaders will focus on scaling AI across the enterprise, hiring more data scientists and investing in accelerated computing technology to support training and deployment of AI applications.
Download the “State of AI in Financial Services: 2023 Trends” report for in-depth results and insights.
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