Conversational AI in Banking: Benefits & Examples

The banking industry has already been transformed by digital evolution. Where it was once a highly traditional sector with long-established brands holding most of the cards, it is now a more versatile space where independent, challenger banks powered by emerging technology can flourish. 

In this environment, it’s more important than ever for banks to prioritize excellent customer experience (CX). In the banking sector, the factors that contribute to better CX are:

1. Trust: In a survey of 75,000 banking customers, trust was the most important factor when considering a bank. 
2. Speed: 72% of customers want immediate service. In banking, where issues are often a matter of finance and therefore carry added stressors, providing rapid resolutions helps aid better CX. 
3. Omnichannel access: The digital era has set new expectations for how people access services. Most people expect 24/7 access and versatile options for how they browse. 65% of customers interact with banks through multiple channels, so banks must ensure they offer a coherent experience across various platforms/channels/devices. 
4. Personalization: Once they choose a bank, users want to feel they’re being catered for. 7 out of 10 financial services users reported that personalization was highly important to them. Personalization allows banks to tailor experiences around individualized customer data to create meaningful, personalized interactions that increase customer retention. 

Considering these factors, how can your bank improve CX and establish trusting, long-term relationships with customers – all whilst dealing with strict regulations around privacy and security? 

To answer that question, it’s time to explore the role of Artificial Intelligence, specifically regarding Conversational AI and Generative AI. In this article, we’ll explore how Conversational AI can help banks tackle all of the CX factors above whilst revolutionizing your customer interactions.

What Is Conversational AI in Banking?

The banking sector has embraced digital transformation, but some of this technological evolution has come at the expense of customer experience. Banks need a way to restore customer trust and facilitate a sense of personalized, prioritized service.

Conversational AI provides the answer. It is a type of AI powered by Natural Language Understanding (NLU), which allows it to understand human language input across text or speech. Not only can Conversational AI recognize intent and sentiment, but it can also create replies and engage in real-time dialogue with customers in a way that mimics human conversations. 

In the banking sector, AI Agents powered by Conversational AI can tackle the important CX factors we’ve already discussed. Across telephone and digital channels, Conversational AI helps you cut down on customer waiting times, speed up call resolutions, provide powerful personalizations, and act as an interactive cornerstone on an omnichannel level.

To put that simply, Conversational AI can replace or augment many of your customer-facing processes to facilitate intuitive, engaging customer conversations that drive higher CX.

What is the Future of Conversational AI in Banking? 

The banking industry is quick to embrace new digital technologies and AI is no exception. Adoption is still in its infancy, but we expect banks to begin integrating AI into core customer processes over the coming years. 

To demonstrate the impact this will have on the future of banking, let’s look at a study by Polaris Market Research. The study estimated the market size of AI in banking to be $19.84 billion as of 2023, growing to $26.10 billion by 2024 and then to an incredible $236.70 billion by 2032 – a compound growth rate of 31.7%. 

Benefits of Conversational AI in Banking

The benefits of Conversational AI in banking are incredibly appealing to decision-makers and even customer service staff alike. Far from worrying about AI replacing human workers, you should instead focus on the benefits Conversational AI drives across key service processes and how that leads to success at a business level. 

Enhances Customer Service Processes

Conversational AI is adept at handling direct customer interaction, whether that’s through a digital chat channel or over the telephone. In this role, AI is directly responsible for improving many of the critical CX factors mentioned at the start of this guide. 

Deploying an AI Agent to tackle customer service processes allows you to cut out customer waiting times, improve call resolutions, provide more personalized support, and ultimately save time and money. 

AI Agents are powered by more than just Conversational AI, often being paired with Generative AI and linked to your backend systems – all of which results in a complete AI system that can converse with customers and action their requests. This means AI Agents have the autonomy and understanding needed to automatically resolve customer inquiries wherever possible, which cuts down on call handling times, improves satisfactory outcomes, and frees up your human agent resources. 

If the AI Agent recognizes that a customer is frustrated or the task requires human intervention, it can route it to the appropriate agent and give them a complete summary, so the user doesn’t need to repeat themselves or explain the problem again.

Improves Team Efficiency

Human agents might naturally be worried about a business implementing an AI Agent, but they should instead be excited. AI Agents do not replace human workers – they augment them both directly and indirectly and make them more effective. AI Agent Management allows you to customize your AI solution to fit seamlessly with your existing team. 

In terms of indirect benefits, the AI Agent tackles all of the most mundane customer-facing processes like identification and verification. It can also record customer interactions and summarize them, meaning when it routes the call to a human, they will get full context and won’t have to go through the same basic processes again. This leaves your human staff free to focus on more rewarding problem-solving. 

On a direct level, AI Agents provide live Agent Assist which serves the human agent with live, context-relevant support during the call. If, for example, a customer asks about a certain loan product, Agent Assist can recognize the query and generate the answer to display on the human agent’s screen. This saves even more time and makes agents as effective as possible. 

Caters to Omnichannel Needs

AI Agents, unlike traditional chatbots, are not tied to a specific window or tool. They exist on a separate layer and can be deployed across multiple channels. Customers can interact with an AI Agent via their preferred method, whether that’s a voice call or messaging using WhatsApp or other social platforms. 

The AI Agent can handle the entire interaction within the same channel. For example, if a user is asked for a photographic ID to verify themselves, they would ordinarily need to either hang up or put the call on hold, take the photograph, find the requested upload mechanic, and then upload the photo. This process introduces lots of friction that could lead to a user dropping from the call. An AI Agent can handle the entire process in the same channel, vastly improving the chance of a positive outcome. 

Improves Trust & Compliance

Banking is one of the most tightly regulated industries, which can often cause issues for brands pursuing digital innovation. Any new tool or technology must be weighed against potential risks to customer data or privacy. 

AI Agents help improve customer trust by providing one-to-one conversational experiences that make users feel supported and catered for, whilst also ensuring regulatory standards are upheld in the background. During conversations, the AI agent records a transcript, which can be stored securely and used in future for data analysis and compliance reporting. 

Provides Powerful Data Capture

AI Agents create live transcripts and call summaries, which are generally used to help human agents but also create incredible opportunities for data analysis. In an era where data is crucial to decision-making, the power of Conversational AI to provide deep insight is hard to ignore. For example, a bank could analyze a year’s worth of customer conversations to spot common queries, identify problems, and isolate gaps in processes. 

Use Cases of Conversational AI in Banking

Exploring the benefits of Conversational AI in banking gives you an idea of why it’s useful. But it’s by examining use cases that we get the how. Here are some tangible examples of real-world use cases where Conversational AI can most impact banking brands…

ID&V

Almost every customer-facing business has some form of identification and verification process. In banks, this is usually a stricter and more controlled one that can involve multiple stages and require additional documentation from a user. 

Conversational AI can improve ID&V processes at both the most straightforward and most complex ends of the spectrum. At a basic level, the AI Agent can prompt the user to answer and verify basic security questions before either progressing the task or directing the now authenticated user to an agent. 

In more complex processes, where additional documentation is required, the AI Agent can again walk the user through basic question and answer stages before providing a portal for them to upload any necessary documentation – all without having to leave the conversation. 

Customer Support

Most customer support requests relate to basic questions about a customer’s account (what’s by IBAN?) or your bank’s products/services. Answering these mundane queries takes time and effort for your human team. If you intend to grow, these generic queries will only continue to increase, so you need a way to handle them without dramatically scaling up your available resources or wasting your team’s time. 

An AI Agent represents the perfect solution. Trained with industry data and your own knowledge base, the AI can field queries and find answers in a matter of seconds. The AI Agent will also have access to your backend systems, so it can access customer data once they pass verification. This means customers can get rapid answers to questions either about their account or about generic products/services. 

They can either call or message with their questions and be quickly directed to the relevant answer – all without any need for human intervention. For a Cognigy client in the financial services sector, using an AI Agent as a frontline solution to common customer questions led to a 15% lower average handling time and improved both customer and agent satisfaction. 

Self-Service

Banking apps have already led the way in terms of self-service, with users able to accomplish tasks that once necessitated a visit to a physical bank, such as setting up new savings accounts or transferring funds. 

Conversational AI takes self-service to the next level and empowers users to do even more. Rather than being locked to predefined processes and actions, the user can simply tell the AI what they want to do. The AI can recognize the intent and either help them accomplish this automatically, or identify a need for human intervention and tell the user that a human agent needs to get involved. 

It’s rare that a human needs to intervene, as Conversational AI is capable of fulfilling the majority of self-service requirements – including producing personalized plans based on customer data and the parameters you’ve set in advance. 

Agent Assist

AI Agents aren’t solely there to help customers – they’re also responsible for supporting human agents during cases. In many ways, this use case is the perfect starting point for AI integration as it’s not customer-facing. 

Agent Copilot takes a multi-pronged approach to supporting human workers:

1. Before handing it over to the agent, the AI will verify the user and get context for the call. This means the human agent is always aware of the issue and doesn’t need to ask a customer to repeat themselves. 
2. During the call, the AI listens and recognizes intent. It provides human workers with relevant resources and answers, displayed directly on their screens, enabling them to solve problems faster than ever. 
3. The AI Agent grants multilingual capacity to your team. It offers bidirectional, real-time translation during live chat so customers can speak in their native tongue. 
4. The AI helps protect human employees from falling victim to tricky compliance issues due to mistakes or errors in their responses. The AI pulls answers directly from your knowledge base, rather than a human making a mistake with their reply due to relying on memory. 

Payment Reminders & Processing

An AI Agent can help you remind users about upcoming fees or charges and can even help them make payments more efficiently. By notifying customers using WhatsApp or other messaging systems, the AI Agent can reduce the risk of a customer failing to make payments on time and can field any queries they may have related to the charge. 

As with document collection, the AI Agent can minimize friction by taking payment details securely from customers and processing the payment – all without them having to navigate to a new window or switch devices. 

Examples of Conversational AI in Banking

Use cases help demonstrate specific areas in which Conversational AI can help banking businesses better serve customers and aid their internal teams – but let’s look at a more concrete example of AI in action. 

Rentenbank Revolutionizes Customer Support

After getting involved in a federal program to provide low-interest loans to agribusiness clients, Rentenbank recognized that it needed a way to answer customer questions and present complex information in an accessible, customer-focused format.

Working with Cognigy.AI, Rentenbank deployed an AI Agent named Lara to replace an outdated, confusing system that relied on large PDF files. The AI Agent was able to answer direct customer queries and also find and present information to new customers to help inform them about the loan offer and any associated details. 

Lara immediately began solving customer problems and improving overall satisfaction. Every fourth request leads to Lara learning something new, which she can then put into practice in the future. As a result of the AI Agent, Rentenbank has extended its service offering and attracted excellent chat ratings from customers – with the future looking very bright as they continue to explore Conversational AI. 

Read more about this example in our Rentenbank case study. 

Best Practices for Conversational AI in Banking

Banks, more than any other type of business, need to make sure they minimize mistakes when adopting tech. When errors can lead to significant financial consequences, there’s no room for risk. Here are some great best practices when implementing Conversational AI to keep things on the right track. 

Identify Appropriate Use Cases

Though we’ve discussed a broad range of use cases in this article, many more may be specific to your bank. You should always start by identifying use cases and finding those representing the highest resource drain against the lowest complexity – this is where Conversational AI will have the most significant impact in the shortest timeframe. Once deployed for one use case, you can scale the AI Agent out to others and it will continue learning and evolving. 

Consider Agent Assist First

In light of everything we’ve just said about use cases, one of the best areas for a banking brand to get started is in exploring AI Agent assist features. When acting to support human agents, an AI Agent can still dramatically improve vital CX metrics – but crucially there is no customer-facing interaction, which may be sensible for banking businesses that have yet to map out risks properly. 

Plan For Human Intervention

Though some businesses can use Conversational AI to automate nearly every aspect of their customer service process fully, banks almost always require human intervention at some point. Whether that’s for compliance reasons or to negotiate a specific loan offer with a user, there is inevitably a point at which your human team needs to step in. 

Plan out your service processes and identify where automation will help and when a human needs to get involved. Use this with your AI provider to design an AI Agent that fits seamlessly into your ideal customer service process. 

Brief Human Agents

In an age where common media discourse focuses on how AI may replace jobs, it’s important to remind your human team that AI Agents are there to augment them rather than replace them. Consider hosting a Q&A session or providing a short training presentation to your team to demonstrate the value of AI Agents and how they can help agents improve their productivity and even drive higher rates of workplace satisfaction. 

Cognigy Can Implement Conversational AI Solutions for Your Bank

For banks, embracing digital innovation helps you stay ahead of customer needs and fend off competition from challengers and start-ups. Conversational AI is not a conceptual technology – it’s already here, and its impact is felt across all sectors. 

If your bank wants to explore how Conversational AI can transform your customer journey, improve overall CX, and ultimately drive revenue growth, book a demo with Cognigy.AI today. 

Frequently Asked Questions

What Is the Difference Between a Chatbot vs Conversational AI?

Traditional, non-AI ‘chatbots’ use pre-programmed rules to provide defined responses to set queries. Conversational AI uses natural language understanding to interpret text and engage in flexible, human-like conversations with users. 

How Does AI Improve Customer Experience in Banking?

AI Agents improve customer service in banking by addressing many of the most important elements for banking customers. They decrease waiting times, improve satisfactory outcomes, give users more personalization, and grant them autonomy to carry out self-service actions.  

Can AI Replace Banking?

No. AI is not a replacement for any specific industry or service and is instead best used to augment teams and improve processes for the end-user and your internal team.