Financial Services Firms Will Bank On Homegrown AI Training

Financial services firms are leading AI adoption, especially generative AI, focusing on real-world use cases such as fraud detection, claims automation, trading insights, and regulatory reporting. Unlike other sectors, these firms face strict compliance, high security needs, and rising cloud costs, driving a shift toward on-premises or co-location GPU clusters. Proprietary AI models are viewed as critical intellectual property, similar to trading algorithms. Advances in liquid cooling and dense GPU racks are making large-scale infrastructure viable. With agentic AI and supercomputing, financial institutions are set to define the future of intelligent, secure, and efficient financial systems.

Rapid AI adoption in financial services.
Key use cases: fraud, trading, compliance.
Shift from cloud to in-house GPU clusters.
Cost, security, and regulation drive strategy.
Proprietary models protect competitive advantage.
Emerging tech: liquid cooling, agentic AI.

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Has the company allocated budget for more computing power? *

Yes
No

How would you describe your role in the IT Decision-Making process? *

Main Decision Maker
Buyer
Influencer
Evaluator/Recommended
Part of a committee or team

Do your applications scale and benefit from more cores with in each CPU? *

Yes
No

Do you need more CPU work performed per watt of electricity consumed? *

Yes
No

Is there a need for wide range of servers and storage systems that contain the latest CPU? *

Yes
No

When do you estimate the need for new servers? *

Less than 1 Month
1-3 Months
4-6 Months
7-12 Months

If yes, what is an estimate?

$1M – 10M
$10M to $50M
$50M to $100M
$100M to $250M
$250M to $500M
Over $500M

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