On-premise GPT Deployment for Banking

Overview & Scope

Banks have mostly stayed on the sidelines of the Generative AI wave that has disrupted many industries. This puts them in a tricky position—they need to innovate to stay competitive, but they also have to manage the risks associated with handling sensitive financial data.

Most AI solutions, especially generative AI, are cloud-based, which presents a challenge for banks. They deal with sensitive customer data and must comply with strict regulations that vary by region. A breach or attack on external cloud servers could expose this data, making cloud-based AI too risky for many banks.

This is where on-premise GPT comes into play. It allows banks to leverage AI without sending their data outside their secure environment. By keeping everything on their own servers, banks stay in full control of their data while benefiting from the capabilities of large language models (LLMs) and generative AI.

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Current Perception in Banking

The banking industry's cautious approach to AI, particularly cloud-based solutions, isn't just anecdotal—it's backed by hard data.

Data Privacy and Compliance:

• 86% of organizations in regulated industries, including banks, cite data privacy and compliance as the top barrier to cloud adoption.【Source: IBM Security Report】

Regulatory Compliance Concerns:

• 56% of banking executives report concerns about meeting regulatory compliance standards on the cloud.【Source: Accenture Financial Services Cloud Survey】

Security Challenges:

• 94% of financial services organizations say security is a major challenge in their cloud strategy.【source: Deloitte Financial Services Survey】

Risk of Data Breaches:

• Financial institutions face a 35% higher risk of data breaches than other industries, making security in cloud environments a significant concern.【Source: IBM’s Cost of a Data Breach Report】

Vendor Lock-In and Operational Resilience:

• 68% of financial services firms worry about vendor lock-in, and 78% express concerns about operational resilience when using cloud providers.【Source: McKinsey & Company】

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How On-premise Deployment Works 

Let’s say a customer sends a query about a complex financial product. Here's how an on-premise GPT system processes this request:

Query Reception: The customer's query enters the bank's secure network through a customer-facing application.

Data Retrieval:

The system accesses relevant data from various internal sources:

1. Product databases
2. Customer information systems (CIS)
3. Regulatory compliance databases

All of this happens within the bank's firewall, ensuring data never leaves the premises.

Context Building:

The GPT system constructs a context for the query using:

1. The customer's profile and history
2. Current market conditions
3. Applicable regulations

This context is built using anonymized, aggregated data to protect individual privacy.

GPT Processing:

1. The query and context are fed into the on-premise GPT model.
2. The model, fine-tuned for banking-specific language and scenarios, processes the information.
3. This happens on high-performance GPUs located within the bank's data center.

Response Generation:

The GPT model generates a response, which is then:

1. Checked against regulatory compliance rules
2. Verified for accuracy against the bank's current policies and products
3. Personalized based on the customer's profile

Final Review and Delivery:

1. For complex queries, the system may route the response to a human expert for final review.
2. The approved response is then sent back to the customer through the same secure channel.

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Key differences between On-premise Vs. On-cloud

When it comes to implementing GPT in banking, the choice between on-premise and cloud-based solutions is crucial. 

On-premise deployment offers several distinct advantages:

1. Data Control: In an on-premise setup, all critical components—the LLM, data buckets, vector database, and user control plane - reside within the bank's infrastructure. This ensures that sensitive data never leaves the organization's control.
   
   
2. Model Integrity: On-premise deployment prevents external training or learning from the bank's data, maintaining the integrity and privacy of the AI model.
   
   
3. Scalability Without Per-Request Costs: As banks expand their AI applications, on-premise solutions allow for infinite scaling without incurring additional per-request or per-action fees. This becomes increasingly important as the organization's AI practice matures.
   
   
4. Customization and Integration: On-premise solutions offer greater flexibility for customization and integration with existing banking systems and data stores, including SharePoint and network drives.
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Real-Life Use Cases in Banking

A leading Caribbean bank, constrained by regulations, prohibited the use of cloud-based LLMs. With Fluid AI, they deployed an on-premise solution that now serves for both their internal and customer-facing applications, with new use cases deployed in as little as a week.

In customer support, a major bank implemented an on-premise AI solution that significantly improved their service delivery. The system went live in just 10 days, compared to the typical 2-month deployment time for cloud-based solutions.

This rapid implementation meant customers experienced enhanced support almost immediately. The AI now handles 45% of all customer queries across email, live chat, and phone channels, leading to faster response times and more consistent service quality. Human agents, now assisted by AI, have doubled their efficiency in resolving complex issues, allowing them to provide more personalized attention where it's most needed. Customers have responded positively to these changes, with satisfaction ratings for AI-assisted interactions reaching 4.7 out of 5. 

Looking ahead, the bank anticipates that as the system continues to learn and improve, it could potentially handle up to 80% of customer interactions, further streamlining the support process and allowing for even more focused human intervention on complex cases.

Read more about Gen AI use cases in customer service

Beyond customer service, on-premise GPT is enhancing sales and marketing efforts by helping teams upsell and cross-sell products more effectively through personalized recommendations. In operations, it assists branch and operational teams in navigating complex, highly regulated processes while ensuring compliance with all necessary rules and procedures. 

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Future of On-Premise GPT in Banking

One-size-fits-all approach would stop working. Each bank is likely to develop its own unique AI ecosystem that aligns with their specific needs and "bank language." This customization is already evident among early adopters, who are creating highly specialized AI models.

From a financial perspective, the traditional per-token pricing model is expected to become obsolete. The predictability of on-premise solutions is anticipated to be a major draw for banks. 

Regulation is another area poised for significant change. The regulators will be in catch-up mode for some time, with new AI-specific banking regulations likely to emerge in major financial hubs within 24 months. This regulatory change will present challenges for compliance teams, but will be seen as a necessary part of progress.

Customer experience is expected to see dramatic improvements. The expert envisions 24/7, highly personalized banking experiences as virtual assistants that will make current digital banking services seem outdated by comparison. 

However, the human element remains crucial. A significant skills gap is anticipated, requiring banks to prioritize workforce upskilling.  There is a prediction of a new type of banker emerging—a hybrid of data scientist and financial expert to gain a competitive edge.

In conclusion, while the path to widespread on-premise GPT adoption in banking won't be without obstacles - including technical challenges, regulatory hurdles, and a period of trial and error - the potential benefits are seen as enormous. 

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