Generative AI has revolutionized computing, with at least 30.7% of Americans having used an AI chatbot by the end of 2023. The applications are nearly limitless, especially when it comes to improving customer experience (CX). Regardless of the business sector, it’s not difficult to identify ways generative AI can help companies increase customer satisfaction. However, the impact is perhaps greater in the financial services sector, where clients constantly have questions and need information about accounts, investments, and financial management. This is where Large Language Models (LLMs) designed for customer service can make a significant difference. Whether you use them as AI agents for online banking, interacting with customers via live chats, or as a human-like knowledge repository, the right model can drastically improve customer experiences.

Identifying the Challenges of Adopting Open-Source AI Chatbots

Using an AI chatbot like ChatGPT isn’t a simple, plug-and-play maneuver. Even if you provide your financial institution’s data to a generative AI system to turn it into a banking chatbot, you will face several challenges.

Security Issues

You don’t have control over your data once you submit it to a generative AI chatbot like ChatGPT. Once an open-source model has your information, you have no control over how it’s kept secure or if your data is used to train the model further.

Using conversational AI for banking comes with even more risk because you also need control over what happens to the data customers share with the system. In addition to presenting data privacy challenges, this could expose their sensitive information to attackers.

Limited Functionality

In the financial sector, the customer journey is shaped by the convenience with which they can access important information. A typical language learning model (LLM) can’t automatically interface with a database that has the information customers need. This could significantly hinder your CX strategy.

For example, if a customer asks a ChatGPT-based chatbot why their account balance isn’t reflecting a deposited check, the model may reply, “As a Large Language Model, I don’t have access to real-time account information. Please email our accounts department for more information.” Or even worse, hallucinate and provide inaccurate information. This could further aggravate a customer during an already stressful moment.

Scalability Limitations

When using a Large Language Model like GPT-4, you don’t have any control over how the system performs during peak usage times. Your customer service automation software could be impacted if the system gets overwhelmed with requests.

For example, suppose you used GPT-4 for a contact center automation solution. During Christmas time, users around the world inundate it with requests. You have no choice but to hope for the best because the system doesn’t scale to meet your specific needs.

Generative AI Solutions

Fortunately, there’s a better way to create customer service chatbots for the financial sector. Moveo.AI’s pioneering proprietary LLM is a game-changer for organizations that must overcome implementation and privacy obstacles.

Moveo’s Approach to Security and Data Privacy

Moveo.AI puts you in control of the security of the sensitive information that your LLM accesses. Not only does Moveo.AI interface securely with customer information, but you can also use an on-premise implementation. This way, you can control the networks the system connects to — if any — and the security tools you use to protect data.

Moveo’s Adaptability

With Moveo.AI, you get an LLM trained in business conversations specific to financial services. This ensures that customer interactions are both natural and professional. For example, a customer could request banking information in various ways, and Moveo.AI’s LLM would still understand the requests. This makes it easier for customers not well-versed in “banking speak” to have natural, helpful interactions with your LLM.

In addition, Moveo.AI has dropped its hallucination rate, making it significantly lower than GPT-4. Moveo.AI can also respond with follow-up questions to ask for clarification. For instance, if a customer asks for a loan, Moveo.AI would ask for clarifications regarding the loan they’re interested in. By contrast, a GPT-4 model would reply with a massive wall of text outlining the different types of loans available.

💡 Read more about Moveo’s LLM vs GPT-4 for CX.

Take Advantage of Industry-Specific Features

Using Moveo.AI’s LLM, your CX strategy does not need to adjust to the model’s limitations because it can be tailored to meet your customers’ needs.

Returning to the example of a GPT-4 model refusing to answer an account question that requires real-time data, your Moveo-powered LLM agent can provide the exact information your customer needs. For instance, if asked about the status of a deposited check, the LLM could reply with, “The check was cleared earlier this morning, and it should be reflected in your account balance within the next 20 minutes.”

This is possible because Moveo.AI integrates with your company’s back-end systems. Using the data from your back end, our AI agents can provide dynamic information and execute personalized functions, such as checking account balances.

Leverage the Power of LLMs in the Financial Sector Now

Moveo.AI eliminates the limitations you face with other Large Language Models, enabling you to create authentic, helpful, and convenient customer experiences on their terms. Because you can customize Moveo.AI to provide the information your customers need, it can offer a wide range of information — whenever the customer wants and without compromising data security standards. Learn more by connecting with Moveo.AI today!