Top Financial AI Inference Use Cases You Can Bank On

The era of open-outcry trading on bustling exchange floors has long passed. Likewise, actuaries now rely on advanced supercomputers to determine insurance premiums with greater precision. At banks, traditional batch processing methods have been replaced by real-time payment systems like Zelle, enabling instant, peer-to-peer fund transfers between customers and institutions.

Finance today is an AI-forward industry – early tech adopters experimenting and proving way better ways to deploy AI with an eye for ROI. Highly regulated investment firms, banks and insurance providers are extremely data-driven, while dollar-driven accuracy is a necessity. Mistakes and security incidents cost big money. New tech is always pioneered and piloted to meet or exceed customer expectations and address problems.

Now, with artificial intelligence (AI) at the forefront of business technology, how are financial services firms improving their operations with AI tools? What challenges do they face, and how can they overcome them? Let’s look.

AI: FinTech’s Next Evolution

AI solutions are emerging that can parse financial data and provide results with unprecedented speed and accuracy. Financial services firms, naturally, understand ROI. More and more, financial customers want the most efficient infrastructure that maximizes ROI while scaling bigger and complex AI workloads. The key to this puzzle is in – driving up processing power and performance while reducing the cost and power per AI mega token – lies in AI inference.

AI’s bandwidth and processing needs are far higher than those of other business-critical computing applications. That is why to run them, AI-optimized hardware is a must, and on-premises infrastructure for finance is making a comeback. AI inference servers are appearing in in-house tech stacks, both enabling AI to work effectively and potentially giving companies more data security and control than distant, third-party cloud servers do.

Where are these tools proving effective when deployed from the right hardware? Here are some emerging use cases.

banks, insurance, investment firms

Fraud Detection

Financial services firms are prime targets for fraudsters, where attacks inflict significant financial and reputational damage. While traditional AI excels at spotting known malicious patterns, generative AI now enhances fraud detection. For instance, it can synthesize realistic but fraudulent documents or transactions that existing systems might miss, enabling more robust training and identification of novel forgeries in visual verification. Names as big as Capital One are boosting fraud protection with AI. Crucially, these firms also aim to eliminate costly false positives, which can disrupt legitimate transactions and erode trust.

Customer Care: AI-Powered Personalization

AI streamlines customer service, from directing calls to AI-assisted voice agents and text chats. Customers easily get answers and help via call, text, or online chat about their accounts. AI's 24/7 availability and ability to manage millions of queries at once boost efficiency. Personalization is expanding with Agentic AI. HSBC's AI assistant, for example, resolved over 1.5 million inquiries in its first year, demonstrating AI's capacity to manage significant customer interactions and free up human agents.

Credit Risk and Underwriting

Credit decisions hinge on assessing risk for lines and loans. AI rapidly handles diverse, large datasets, enabling sharper underwriting via smart analysis. For instance, for mortgages, smart analysis blends credit scores with data like utility payments and social media for better risk insight. A TransUnion study shows alternative data can lift risk prediction accuracy up to 30% for those with little credit history. This helps lenders responsibly offer more credit while limiting defaults. Smart analysis forms the bedrock for more advanced generative and agentic AI applications, which can create new scenarios or autonomously apply insights in the underwriting workflow.

Algorithmic Trading

Maximizing stock portfolio performance depends on knowing when to buy, sell, or hold. With AI’s ability to process massive amounts of market data, it stands to enhance automated trading, figuring out characteristics of the market missed by individual analysts or simple rules-based automation.

Insurance Claims Automation

Lengthy back-and-forth in insurance claims can frustrate customers and harm insurer reputation, while inflating customer service costs. AI offers solutions to streamline these bottlenecks and accelerate processes. Leveraging computer vision for detailed image analysis of vehicle and property damage, companies like Tractable report their technology> can slash claim cycle times by up to 80%, providing swift damage assessment and initial cost estimates. This not only enhances customer experience but also significantly lowers operational expenses for insurers by automating traditionally manual inspection and estimation stages

new inferencing needs as ai workloads scale

For finance firms to derive value from AI, they need to run their solutions on hardware that can handle it – such as the new generative and agentic AI-ready NR1® AI Inference Appliance. It’s compact, easy-to-deploy, and up and running in under 30-60 minutes – significantly reducing complexity, cost, power consumption, and space requirements for AI inference at scale. Purpose-built for on-premises AI inference serving at scale, the NR1® Appliance is already deploying with financial services customers.

NeuReality: The Choice for Finance AI

At the heart of the NR1® Appliance innovation is our AI-CPU — a revolutionary new class of processor built for AI inference orchestration. For years, GPUs evolved to meet AI's demands, becoming faster and more powerful. But traditional CPUs—designed for the Internet Era not the AI Age—have remained mostly unchanged, creating a critical bottleneck as AI models grow increasingly complex. Until now.

Our NR1® Chip is the first true AI-CPU specifically engineered for inference orchestration. It subsumes the CPU and NIC into one but with 6x the processing muscle. It features a low-power engine that combines essential general-purpose CPU functionality with dedicated media and data processors, the hardware-based NR1® AI-Hypervisor™, and comprehensive networking and connectivity IP. The NR1 super boosts GPU utilization to nearly 100% – 2.5x that of general-purpose CPUs –delivering drastically better performance and business ROI.

With NR1® Modules coupled with Qualcomm® Cloud AI 100 Ultra accelerators, our first financial customers are running pre-optimized AI models securely on-site, ensuring data privacy, regulatory compliance, and full control. Trained AI models deploy seamlessly, delivering best-in-class price/performance without the resource drain or scaling challenges of traditional CPU and NIC architectures.

You can choose your own AI accelerator (GPU, FPGA, ASIC) for further customization as proof of concept.

Finance’s Future is AI Inference

As AI keeps revolutionizing industries and bringing new conveniences to customers and functionalities to business, expect to see more finance firms using it to improve security, accuracy, certainty, and speed. According to Statista, spending on GenAI in finance is expected to exceed $84 billion by 2030. For that future, finance firms will want to invest wisely. That means using specialized AI inference servers like the NR1.

If you want to redefine your silicon-to-software foundation to build and scale your business-critical AI, contact us for more details on getting started with AI inference hardware.