Generative AI continues to transform the way businesses interact with customers. Platforms like ChatGPT, developed by OpenAI, leverage large language models (LLMs) to produce human-like responses and showcase the potential of generative AI. While some businesses may use ChatGPT directly for tasks such as analysis, content generation, or data visualization, others implement their own custom generative AI solutions tailored to their unique needs. However, as impressive as these capabilities are, integrating generative AI systems or using platforms like ChatGPT presents unique challenges, particularly in addressing data privacy and security concerns.

If you’re a legacy enterprise trying to keep up with newer fintech companies, you’re probably feeling the pressure to reinvent your tech stack. The advent of Generative AI, in particular, makes you feel like you’re racing to the finish line to keep up with more innovative brands.

There’s just one problem: without proper safeguards, your data may not be secure. Depending on the large language model (LLM), which is the main component of Generative AI, and how it’s implemented, your customer data — and your company’s — could be at risk of exposure.

Fortunately, there are secure solutions that keep your proprietary information safe. Here’s everything you need to know about the risks of LLM’s when implemented incorrectly and the rewards when done right. ChatGPTleverages large language models (LLMs) and operates through OpenAI’s managed infrastructure. While significant security improvements have been made over the past year, relying on third-party providers requires careful consideration. Exploring alternatives that allow direct control over infrastructure can provide additional security assurances.

Where generative AI can create opportunities and solutions

With those risks in mind, there are still numerous opportunities for secure generative AI functionality. The list of use cases continues to grow, offering innovative solutions tailored to various business needs. Here are a few key areas where it can make a meaningful impact:

Personalizing your customer experience

Generative AI interfaces streamline customer interactions, offering users an experience akin to engaging with a financial professional. These conversational chat solutions can guide customers through tasks such as onboarding, applying for loans, or building investment portfolios—all with efficiency and ease.

Furthermore, generative AI enables deeper personalization by aligning the tone and voice of chat features with your brand’s identity. This capability ensures that customer interactions feel cohesive and authentic, reinforcing trust and loyalty.

Risk management

Generative AI tools can be utilized to monitor transactions, detect anomalies, and flag potential threats before they escalate. These technologies, building on advancements in machine learning (ML), provide valuable capabilities for enhancing security, detecting fraud, and reinforcing customer trust.

Financial decision-making

AI’s risk assessment capabilities also mean it can optimize your customer’s portfolio and assess which investments are worth the assumed risk. By leveraging expansive datasets and advanced analytics, AI provides data-driven insights that improve decision-making and enhance portfolio optimization, although outcomes still depend on external factors and market conditions.

Building secure generative AI solutions for financial services

Integrating generative AI into your digital platforms can transform customer experiences while safeguarding proprietary information. With the right strategies and tools, privacy and security become foundational, ensuring peace of mind for both you and your customers. Here’s how to achieve it:

1. Deployment location / service

ChatGPT and data used within the platform are not accessible by the public or OpenAI; however, it does operate within OpenAI’s infrastructure, which can raise data privacy concerns for specific applications. By contrast, deploying generative AI within a private cloud environment, such as Microsoft Azure or AWS, allows for greater control over proprietary data. This setup ensures enhanced security through features like encrypted storage and access controls, giving organizations the ability to dictate how data is utilized and safeguarded.

For example, without leveraging a private location for your generative AI, uploading sensitive company documents into an LLM could lead to unintended data exposure.

Additionally, incorporating a vector store—a specialized database for accurate retrieval in LLM applications—can ensure that responses are contextually relevant and tailored. While not a direct security measure, vector stores enhance the precision of AI outputs by retrieving only the most appropriate data for user interactions.

2. Keep Iterating with AI

While dramatically improved since 2022, Generative AI is still rapidly evolving technology. This presents challenges in determining the best ways to leverage its capabilities while ensuring sensitive data remains protected.

At the same time, staying competitive in a saturated marketplace requires keeping pace with technological advancements and identifying innovative use cases.

So be prepared to iterate and find new and better processes to secure your data and improve the experience. The overarching question to ask is: how can you leverage AI to improve your entire company’s experience, internally and customer-facing? Think creatively about potential strategies, even if you don’t have an exact roadmap to implementation. Doing so gives your institution a competitive edge.

One way to keep up with new use cases is to partner with a digital experience consultancy like O3. O3 stays up-to-date on opportunities in AI and builds integrations that align with your customer needs and business goals.

Interested in learning more about how to integrate these use cases into your own financial products? Let’s chat!