Have you fallen into the trap of thinking chatbots and conversational AI are the same thing? Don’t worry, you are not the only one.
It might come as a surprise to many to realise that, although sometimes used interchangeably, the terms “chatbot” and “conversational AI” don’t actually mean exactly the same. In fact, these two terms don’t even fall into the same classification! Chatbots are computer programmes. They can be rule-based or AI-based. So conversational AI is just one of the different technologies that can power chatbots.
What you are really trying to understand is how rule-based or keyword-based chatbots compare to AI-led chatbots.
To spare you from making the same mistake again, we have broken down here what rule-based chatbots and conversational AI chatbots really are, and where their difference lies.
What are rule-based chatbots and conversational AI chatbots?
Chatbots are computer programmes that simulate a human conversation. They are the pop-up chat that appears on the corner of a website, your bank’s customer support chat, the automated voice which asks you to state your issue before directing you to the right human agent when you call a contact centre. Ultimately, chatbots and conversational AI are the new way in which customer service is automated, providing you support around the clock.
However, rule-based chatbots and the ones using conversational AI differ in their abilities and how they are programmed. They both work differently from one another. Here is how.
How do chatbots work?
An easy way to understand how keyword-based chatbots work is thinking of them as flow charts. Chatbots works thanks to a team behind the bot that feeds it with keyword questions and predetermined answers to those. Each answer is automated in advance to lead to the next response.
This way, the chatbot will know, for instance, that when you request to see your bank account it should offer you a login link or a forgot password option. Depending on which you take, the next display of answers will vary. For example, selecting the forgot password option might trigger the chatbot to offer a recovery option via email or phone, and what you choose will lead to another pre-programmed action.
This works well for simple tasks like finding certain information within a bank’s website. However, keyword-led chatbots do not allow for queries that fall out of their programming and so the team must have foreseen and programmed the bot for any possible customer query.
Traditional chatbots are keywords-based and they do not understand a customer intent if it is not formulated exactly as the team has programmed it. Thus, chatbots can result in customers being frustrated as the limited scope of the bot means it is not always capable of resolving the customer’s issue.
How does conversational AI work?
Conversational AI chatbots use Natural Language Processing (NLP) and Automatic Semantic Understanding (ASU) to understand the clients’ needs no matter how they phrase it. Unlike the traditional chatbots, conversational AI does not require each exact query to be preprogrammed and it will understand what the client wants no matter how it is worded. This means that the bot using AI will understand that you want to see your bank statements either if you typed “bank statements” or “I’d like to see my accounts for the past month”. It will even understand you if you mispelled something in your message!
Conversational AI will also learn the more it interacts with clients. This means that, with time, it will understand language better and be more efficient at providing customers the assistance they need.
What are the differences between keyword-based and conversational AI chatbots?
How traditional and AI-powered chatbots work results in a substantial difference between them. Here are the main differences you should know:
| Traditional | Chatbots | Conversational AI-led |
|---|---|---|
| Keyword-based | How do they work? | Natural Language Processing |
| Limited scalability | How scalable are they? | Unlimited scalability |
| Must be trained explicitly | How are they updated? | Learns from each interaction |
| Button-focused interaction | How do customers interact with them? | Text and voice |
| Only what it has been pre-trained to | How much can them understand? | Almost anything, thanks to Automatic Semantic Understanding |
What are the use cases of conversational AI in banking?
The use cases of conversational AI in banking are many. Consider the following:
- Helping customers speed up and reduce friction in their credit cards or loans applications.
- Manage personal finances: from viewing your account balance, to making transactions or paying bills, conversational AI can help you do this easily.
- Alert customers of possible fraudulent activity in their accounts and validate with the client this unusual activity. In the case of confirmed fraud, the bot can automate the cancellation and issue of a new card.
- Offer personalised prices and rewards to customers to nurture customer loyalty.
- Provide basic answers to customer queries, helping them navigate the bank’s website. This can range from “how to apply to a loan” or “where to login”.
- Send reminders to customers for when their bills are due, for example.
- Provide insights into the customer’s expending habits, recurring expenses and more.
What are the benefits of conversational AI in banking?
The ultimate benefit of conversational AI in banking is a better customer experience. This is enabled by 3 interconnected factors.
- A better interface for queries
Conversational AI allows for an enhanced customer experience thanks to its ability to allow customers to engage via text, now one of the preferred communication methods. It also allows for customer support to be available 24/7, and it is simple and efficient to use.
- Shorter waiting times
By automating a high-volume of customer interactions, conversational AI frees up capacity for human customer service. This all results in shorter waiting times for both the customers talking to the bot and those in line with a human agent. Shorter waiting times means happier customers.
- Efficient human support
As a result of all of this, human agents can resolve more queries in less time and have a constant stream of information that can aid them in their duties. Overall, support to the client is faster and tailored to their needs and preferences.
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Go a step beyond: learn more about Conversational AI in banking with CFTE’s short online course
A technology that is already enabling for a more efficient and improved customer experience in banking should not be overlooked now that banks are challenged by incumbents offering a much friendlier banking experience.
Discover more about conversational AI programmes and how you can implement them in your business with our short online course on Conversational AI in Banking.
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