Financial Firms Love Gen AI. But Can Their Infrastructure Keep Up?

How is Gen AI Being Used in Financial Services 

According to Statista, the generative AI market in finance is expected to grow from $1.09 billion in 2023 to over $12 billion by 2033. While Gen AI could deliver between $200 billion to $340 billion in annual value for the global banking sector alone by accelerating productivity and enhancing decision-making across front, middle and back-office operations (according to McKinsey and Company).

Financial service organisations are adopting Gen AI across a wide range of use cases:

• Customer service automation: Chatbots and virtual assistants fine-tuned on institutional knowledge and client data are reducing call centre volumes while improving customer satisfaction.

• Risk assessment and underwriting: Gen AI models summarise complex financial documents and detect subtle indicators that help underwriters make faster, more accurate lending decisions.

• Fraud detection: With synthetic fraud scenarios and training advanced models, firms are catching fraud faster and reducing false positives.

• Market analysis and trading: Some hedge funds use Gen AI to interpret unstructured data such as analyst reports, news articles and earnings calls in real time to inform investment strategies.

For example, Morgan Stanley used OpenAI’s GPT-4 to build an internal chatbot fine-tuned on over 100,000 internal documents, so wealth management advisors can instantly retrieve insights, research and recommendations. This improved advisor productivity and reduced the time taken to locate key information (from 20% to 80%) across the firm’s wide knowledge base. Morgan Stanley also reported a great improvement in client service delivery and an accelerated decision-making process, especially in high-pressure client meetings. OpenAI’s zero data retention policy also helped address major security concerns, ensuring Morgan Stanley’s proprietary data stays protected and confidential.

Why Data Residency and Sovereignty are Important

Just like Morgan Stanley, other applications of Gen AI in financial services also rely heavily on access to large and diverse datasets, including historical transactions, communications logs, contracts, customer profiles and financial statements. This data is often highly sensitive and tightly regulated, especially in regions like the EU where strict data residency laws such as the GDPR apply.

Because this information frequently includes personally identifiable information (PII) and confidential customer records, compliance with data residency requirements is critical. Gen AI models typically need access to this data during training or fine-tuning, which creates significant risk if that data is stored or processed in unauthorised jurisdictions.

To mitigate this, organisations must ensure their infrastructure supports regional deployment options, giving them control over where data is stored and processed. By working with Sovereign Cloud providers, financial firms can adopt Gen AI and remain compliant with regulatory mandates like the UK Data Protection Act, the EU GDPR and the Financial Conduct Authority’s (FCA) operational resilience frameworks.

What are the Infrastructure Challenges in Financial Services

Financial institutions have left no stone unturned in adopting Generative AI. They are investing heavily in pilots, chatbots and training custom models. But that investment may not be equal to “market-readiness” as they face various challenges when adopting Gen AI at scale. These include:

• Data privacy and compliance constraints: Strict regulations like GDPR and FCA rules limit how and where sensitive financial data can be used. Gen AI workloads require infrastructure that supports full data control and residency.

• Legacy systems integration: Older systems often lack compatibility with modern AI tools, making it difficult to integrate Gen AI into existing environments without major overhauls.

• Massive compute and storage needs: Large models demand powerful GPUs, fast storage and low-latency networking. Traditional infrastructure wasn’t built to handle this scale or speed.

• Limited in-house AI talent: Even when the right tools exist, many financial services teams lack the AI-ready infrastructure knowledge required to deploy and manage Gen AI at scale.

This is where high-performance infrastructure becomes your “best buddy”, not only to adopt Gen AI but to do so quickly, securely and efficiently. European cloud providers like NexGen Cloud support high-performance and compliant-ready Gen AI deployments. Unlike traditional cloud providers, NexGen Cloud understand the growing need for “compliant and AI-ready” solutions for organisations working in regulated areas like finance.

How NexGen Cloud Helps

Considering the sensitive nature of financial workloads, NexGen Cloud offers Sovereign AI Cloud deployment option to meet compliance and performance standards:

• Single-tenant deployments: Your infrastructure runs in a completely isolated environment, with no shared resources, ensuring greater control, higher security and full ownership of compute capacity.

• EU/UK hosting: Deploy in GDPR-compliant regions such as the UK and EU to maintain data residency and meet legal requirements around financial data localisation.

• Private access control and audit trails: Implement granular access restrictions with full activity logs, giving you clear oversight of who accessed what, when and from where, ideal for audit readiness.

• No shared tenancy or hidden subprocessors: NexGen Cloud does not rely on third-party subprocessors or shared backend layers, ensuring full transparency and minimising regulatory risk.

• Enterprise NVIDIA GPU clusters: Choose from powerful GPU clusters for AI like NVIDIA HGX H100, NVIDIA HGX H200 and the upcoming NVIDIA Blackwell GB200 NVL72/NVL36 for training, tuning, and scaling large Gen AI models.

• NVIDIA Quantum InfiniBand and NVMe: Ultra-fast networking and high-performance NVMe storage allow for low-latency communication and rapid data access, ideal for large-scale Gen AI operations.

• Full lifecycle support: We support every phase of the Gen AI journey, from infrastructure provisioning and model training to deployment, updates and scaling. So your team stays focused on outcomes.

• Kubernetes and API-driven orchestration: Run AI workloads at scale using optimised Kubernetes clusters and programmatic access via APIs for smooth integration with existing workflows and automation tools.

Conclusion

Generative AI holds massive promise for financial services but only if the infrastructure is ready to support it. Firms that invest in scalable, secure and compliant AI platforms now will be the ones to lead tomorrow’s market. 

In regulated industries like finance, control over your data, infrastructure and operations is of utmost importance. When you rely on third-party platforms, you sacrifice that control in exchange for convenience. Hence, the choice to go Sovereign for Financial AI operations becomes imperative.

Build on What You Control. 

Stay Sovereign. Stay Secure.

FAQs

What is Generative AI?

Generative AI refers to models that create original content via text, images or code based on patterns learned from large datasets.

What is Generative AI in financial services?

In finance, Gen AI automates tasks like customer service, underwriting, fraud detection and research by generating insights from complex data.

How are financial institutions using Generative AI today?

Banks use Gen AI for chatbots, document summarisation, fraud scenario modelling and market research to improve efficiency and service.

What are the benefits of Gen AI in finance?

Gen AI in finance can reduce operational costs, accelerate decision-making, enhance customer experience and help uncover deeper insights from unstructured financial data.

What infrastructure is needed for Gen AI in finance?

Gen AI requires powerful GPUs, fast data storage, low-latency networking and infrastructure that meets strict data privacy and residency regulations.

Why is data residency important in Gen AI for finance?

Financial data is sensitive and regulated. Infrastructure must ensure data stays within authorised regions to comply with laws like GDPR and FCA.