Financial services firms have long looked to emerging technologies for competitive advantages and to increase ROI. So naturally, the finserv sector recognizes generative AI's (GenAI) potential to turbocharge employee productivity and improve customer experience.
In fact, 55% of global financial services professionals said they are actively seeking opportunities to incorporate generative AI workflows, according to NVIDIA's 2025 State of AI in Financial Services
As it happens, consumer banks rank among the biggest beneficiaries of these GenAI and large language models (LLMs) – including impactful uses cases for improving the customer experience, document processing, and report generation.
AI Simplifies Banking Operations
Banks have always looked at ways to improve operational efficiency and increase team member efficiency.
Banks appreciate GenAI's near-instantaneous content generation capabilities for creating sales and marketing collateral. Business analysts can better analyze proprietary information for new insights and generate more accurate reports faster. Software developers can automate code creation, accelerating the completion of critical IT projects.
Customer service representatives can prompt LLMs for critical content to better assist customers, reducing wait times and speeding time to resolution.
At the same time, banks are always seeking to provide the richest digital experiences possible as they court new customers. After all, the difference between a "meh" digital experience and a "wow," hyper-personalized digital experience powered by AI can be huge.
With banking experiences shifting to digital and mobile channels, virtual AI assistants have become the preferred interface for consumers for customer service interactions. Self-service banking tools powered by LLMs make it easier for customers to automate bill payments, initiate transfers and procure personalized financial advice and investment recommendations.
The fact that banks have leaned into GenAI should come as no surprise, as these organizations have long used machine learning algorithms to predict financial trends that will afford them a competitive advantage. Finservs have also leveraged AI as a critical tool for fraud prevention, using algorithmic anomaly detection to find and fend off fraud.
Build Your AI Strategy
It's also clear that generative AI and LLMs represent a different flavor of AI, one democratized for the masses rather than simply in the domain of data scientists. As such, it requires a well-articulated and actionable strategy.
A sound AI strategy starts with your organizations' data, which must be cleaned, deduplicated and prepared for consumption by LLMs and associated generative AI applications. In parallel, organizations must identify use cases, such as digital assistants for customer service, intelligent document processing, and personalized marketing content.
Banks must also modernize their technology stacks to support the current and future crop of LLMs. From legacy infrastructure to data management challenges, AI deployments can be fraught with complexity.
Organizations must modernize their infrastructure—including anything from upgrading compute clusters to parallelizing workloads—to bring greater efficiency to operations and improve customer services to gain a competitive edge.
Today the rise of "
The Dell AI Factory with NVIDIA is a modern solution suite and framework organizations can use to
Comprehensive, full-stack and service offerings such as the Dell AI Factory with NVIDIA will become more critical as enterprises wade further into AI. For example, advances in AI reasoning have increased interest in AI agents, which can make autonomous decisions to achieve organizational goals, ideally further enhancing employee and customer experiences.
For banks, the future of the financial industry is about more than staying profitable; it's about staying a step ahead of the competition and increasing ROI. Banks can scale AI enabled applications across every function and line of business with trusted partners such as NVIDIA and Dell.
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