AI Factories Are Powering Next-Gen Finance

What Are AI Factories, and How Do They Work?

An AI factory is a tightly integrated environment where machine learning and AI models are built, trained, deployed and continuously improved.

Graphics processing units (GPUs) power the high-speed simulations and real-time model training needed to process nanosecond-level trading data. This enables high-frequency traders to build predictive algorithms that look for returns outperforming the broader market after adjusting for risk.

In algorithmic trading, real-time performance is critical, so the ability to iterate quickly and manage models at scale has become a strategic advantage.

“They are the new IT infrastructure,” Lange says, but instead of IT, its focus is on velocity, governance and scale.

“AI factories are all about how you reduce the latency between model idea and model deployment,” she says.

EXPLORE: A new era of digital banking is powered by AI.

How Hedge Funds and Trading Firms Are Using AI Factories

Hedge funds and trading firms are deploying NVIDIA AI Enterprise with DGX systems to run large-scale simulations, train models on time-series financial data and execute GPU-accelerated back-testing and inference.

The platform combines high-performance hardware and software in a turnkey package, with CDW integrating DGX into enterprise data centers and orchestrating end-to-end AI workflows.

Quantitative firms are leveraging Google Cloud’s Vertex AI to teach machine learning models how to run forecasting, sentiment analysis and automated risk scoring.

When paired with Google Cloud’s BigQuery, Vertex AI can predict market trends and help traders adapt to changing financial environments.

A Growing Investment in Algorithmic Trading Applications

By 2030, the AI-driven algorithmic trading market is projected to increase annually up to 12.2%. This is a sizable increase from 2021, when AI-driven algorithmic trading accounted for 70% of U.S. stock market volume and $15.5 billion of global market value, according to a March report from the University of Michigan.

“Financial firms want to automate and accelerate the entire ModelOps lifecycle so they can stay ahead of market movements,” says Lange.

For example, banks are turning to Microsoft Azure machine learning combined with fabric as a scalable MLOps platform that supports the full AI model lifecycle.

With integrated data pipelines from Azure Synapse Analytics and Power BI, the platform functions as an AI factory, enabling financial institutions to build, test and deploy risk models and algorithmic trading signals using real-time data feeds.

Lange notes that AI factories allow for constant experimentation without sacrificing control.

Click the banner below to keep reading stories from our new publication, BizTech: Financial Services.

“You can be running hundreds of experiments simultaneously, but everything is happening within a governed, repeatable framework,” she explains. “That’s what makes this model so powerful.”Intelligent Tools That Identify Fraud Faster

AI factories are also playing a growing role in helping financial institutions meet regulatory demands and improve fraud detection through traceability, transparency and monitoring.

IBM’s Watson Studio and Cloud Pak for Data is one financial AI factory. It helps banks integrate data, manage model lifecycles and build compliant, trusted workflows for credit scoring, fraud detection and financial planning.

Using advanced analytics, banks can detect fraud faster by analyzing behavioral patterns across transaction, customer, biometric and third-party data, enabling quicker investigation and prevention of financial loss.

“AI factories will have tools to trace and show transparency and show what’s happened across the lifecycle,” Lange says.

These also automate the model development process and offer clear audit trails for regulatory oversight. “All this traceability is built in as part of the AI factory so that it has transparency for the regulators,” Lange says.

That visibility is essential when it comes to fraud prevention or loan approvals.

“We don’t want bias in the models, or to be turning people down who shouldn’t be turned down for a loan,” she says, emphasizing the importance of fairness and oversight as financial services scale AI adoption.

UP NEXT: Five techniques for AI abuse and filter bypassing.

Infrastructure That Can Manage Mission-Critical Components

As AI factories become foundational to real-time trading operations, Lange says their value will hinge on efficient inference — the moment when trained models make decisions in production environments.

“It has to give the user a prediction; for example, when should they buy or sell?” she explains.

This operational stage must run on ultralow-latency infrastructure to remain competitive. As demand for inference grows faster than chip supply, optimizing the full AI stack becomes mission critical.

“You need this whole stack to work together — the hardware, the software, the data,” Lange says. “For these high-performance markets, you can’t be waiting 10 minutes for the platform to tell you when the trade is supposed to be right. By that time, the market has changed.”