By Roy Urrico
Finopotamus aims to highlight white papers, surveys, analyses and reports that provide a glimpse as to what is taking place and/or impacting credit unions and other organizations in the financial services industry.
Nvidia reports that although generative artificial intelligence (AI), which is capable of creating text, images, or other media, has barely been on the financial industry’s radar for a year, it dominated the results of the company’s State of AI in Financial Services: 2024 Trends study. Santa Clara, Calif-based Nvidia, which specializes in accelerated computing, completed its study based on a survey of approximately 400 global financial services professionals.
The report also found the financial services industry is undergoing a significant transformation with the adoption of AI technologies. Portfolio optimization, fraud detection and risk management remain top AI use cases. In addition, generative AI is rapidly gaining popularity with organizations seeking to discover new efficiencies.
Some of the Key AI Trends
The Nvidia report revealed generative AI emerging as a particularly promising tool, with 43% of respondents already using it in their organizations and its ranking in the top five of AI workloads, which depend on high-performance computing nodes, in financial services. The use cases powered by generative AI range from internal applications, such as analyzing vast amounts of data and yielding investment insights, to external facing use cases, such as marketing and delivering personalized banking experiences. Another 46% of respondents are already using large language models (LLMs), sizable deep learning models, in their organization.
LLMs are a kind of generative AI technology that have grown significantly in recent years. LLMs offer context and memory proficiencies, while generative AI allows the creation of appealing responses.
Outside of generative AI, financial services — like other industries — seeks to automate repetitive tasks, deliver new products, and reduce costs with AI. In the financial services industry, financial institutions, asset managers, and fintechs strive to use AI to transform the way they work and the services they offer to investors and account holders.
Additional analysis of the survey found:
· Financial services organizations gaining confidence in their ability to identify AI use cases and extract value from AI deployments.
· As the complexity and size of AI models have grown, data concerns have eclipsed recruiting experts as the biggest challenge to achieving AI objectives.
· Financial services firms exploring new ways to build and deploy trustworthy, secure AI, including federated learning, whereby organizations can train AI models on a decentralized platform without transporting data, and confidential computing.
Changing Fast
Over the past 12 months, financial service organizations have worked to improve their understanding of AI use cases, the required computing infrastructure, and deployment options, according to the Nvidia trends report. As a result, companies feel confident in their position in the AI race, with 75% considering their AI capabilities industry leading or in the middle of the pack.
Survey results also show that management has a favorable assessment, with 30% viewing their organization as industry leaders, versus 20% of non-managers. While financial institutions and asset managers are applying AI to industry-specific challenges such as risk management, regulatory compliance, and fraud detection, AI is driving speed and efficiency across disciplines and job roles.
The study noted AI is unlocking data insights, and enabling highly customized marketing campaigns. The report pointed out, “It is giving sales teams more targeted approaches to identify and prioritize leads, while generative AI lets them personalize emails and advertising offers. And AI-powered automation is taking over repetitive tasks to free up human resources and streamline operations.”
According to the survey data, financial services firms are deploying AI across core business areas. For example:
· To improve operations, they are using AI to automate manual processes, optimize resource allocation, and enhance efficiency.
· AI-powered chatbots handle customer inquiries and provide real-time support, reducing the need for human intervention and improving response times.
· In risk and compliance, organizations are using machine learning to analyze vast amounts of data to enhance fraud detection, improve anti-money laundering (AML), and ensure regulatory compliance.
· Marketing departments leverage customer data to generate personalized recommendations, targeted advertisements, and tailored marketing campaigns.
· Sales teams are benefiting from AI-optimized lead generation, customer relationship management, and sales forecasting.