What is conversational banking?

What is conversational banking?

What is conversational banking
Conversational banking is exactly what it sounds like: it’s when a financial institution uses conversational AI to answer banking and insurance questions. Put simply, it’s a bank’s chatbot.
Conversational banking uses natural language processing (NLP), which is a fancy way of saying AI for words. Like any other chatbot, we built the NLP model using intents, expressions, entities and flows.

When a user asks a chatbot a question, we refer to that as an expression. For example, the user asks, “How do I cancel my credit card?” The bot processes the user’s expression and compares it to the different expressions within its NLP model. Each expression within the NLP model is associated with a specific intent, or answer to an expected question. The bot then assigns a confidence score to each intent, and based on the different thresholds and rules that we define, it responds to the user with the appropriate answer.
A look into how a chatbot assigns confidence scores

A look into how a chatbot assigns confidence scores

Why is conversational AI interesting for the banking sector?

Banking is the perfect business case for conversational AI because banks and insurance providers have thousands of customers who use their services multiple times a day. This inevitably leads to problems that need solving and questions that need answering.

Most of the incoming questions are repetitive, easy to recognize and easy to answer. This is the trifecta for conversational AI automation, making it a win-win for banks and customers.

Benefits for banks

💾 Save resources

On average, banks implementing a basic chatbot save between 10-20% of an agent’s time. The customer support agents can repurpose their saved time to focus on more complex cases that need human attention.

🎯 Reduce human error

Humans make mistakes; it just happens sometimes. Whereas a chatbot follows standardized, pre-programmed logic, making it almost impossible to make mistakes.

💬 Interact with customers where they are

A chatbot can live almost anywhere: on your website, in your app, WhatsApp, Instagram, Twitter, etc. By placing your chatbot where your customers already are, you widen your customer reach and reduce the barrier to connect.

💸 Focus on upselling

Instead of spending time answering the same question 20 times a day, a customer support agent can delegate these types of tasks to a chatbot. They then have the time to focus on resolving complex client issues and upselling clients.

Benefits for customers

⚡ Instant answers

A chatbot is available 24/7 and can answer a user’s question in seconds. This is a significant contrast when compared to more traditional question-and-answer approaches. For example, think about when a customer had to wait on the phone line for 10-30 minutes for a representative to pick up or send an email and wait a few days for a response. With a chatbot, your customers ask whenever is most convenient for them and they receive an instant response. There are no opening hours, no queues and no waiting times.

🎯 Personalized experiences

It’s easy to connect your chatbot to your CRM system. This allows you to identify your customers and tailor the conversation to their needs and profile. For example, if you’re logged into your banking app and trying to file an insurance claim, the bot can look up your existing policy and respond with an answer tailored to your insurance policy. Each user’s experience with the chatbot is customized and unique.

💬 Interactive communication

It’s much more enjoyable and efficient to have an interactive conversation rather than trying to fill out a form that doesn’t allow you to express your problem or fighting with an IVR system that doesn’t understand your request. Chatbots are flexible and trained to solve problems and answer questions.

🔮 Omni-channel accessibility

We can place a chatbot on almost any channel, making it much easier for your customers to connect with you via the channel that they prefer. This allows them to seamlessly switch between channels and makes it a more convenient experience for everyone.

What are the best use cases for conversational banking?

Like any other industry, a chatbot can support all types of departments and roles. Here are some different implementations for banking chatbots:
  • Answer frequently asked questions
  • Sell new plans and upgrades
  • Register and onboard new clients
  • Remind users of upcoming payments and deadlines
  • Coach and motivate users to stick to their budget and savings goals
  • Request a new card reader
  • Raise your daily limit
  • Plan your budget
  • Check your balance
  • Make a transfer
  • Pay your bills
  • File an insurance claim

If you’re eager to dive deeper into some actual business cases, check out our work for Argenta and Rawbank.

What’s next?

Banks will inevitably continue adopting conversational AI to automate frequently asked questions, execute transactions and streamline processes. Financial institutions that don’t make this switch won’t be able to sustain the increasing volume of incoming questions and remain competitive. 

Are you interested in implementing a chatbot at your company? Reach out to Alexis for a quick chat.

Sabam & Unisono | Login Help Assistant

Sabam logo

A chatbot that helps you troubleshoot
your login issues

A lot of the issues that a user faces at the login screen can be solved using a handful of different troubleshooting tips. We took these tips and built a chatbot that helps solve your own login problem in less than 3 minutes
An iPhone user that is unable to login to their Sabam account
The problem

Lost at the login screen

Sabam is the Belgian association of authors, composers and publishers, and Unisono is their online platform where users can apply for licenses. With a membership base of more than 40.000, they wanted to reduce the volume of customer enquiries.

After reviewing the data, it stood out that most of the requests were about problems logging into the platform. To help members login to Sabam and Unisono faster, we developed a chatbot that guides users through some troubleshooting exercises and ultimately login to the platform.

The solution

Virtual support when you need it

Working closely with Sabam and Unisono, we identified the most common login errors (ex. CAPS LOCK turned on) and used them to build a conversational flow. As soon as a user fails to login, the bot pops up and asks the user if they need assistance logging in. The bot then guides the user through the different conversational flows to help them troubleshoot their login error. 

In addition to the troubleshooting flows, we integrated 30+ other frequently asked questions in French and Dutch that users can trigger via the chat.

Screenshots of the different login chatbots that Campfire A.I. built for Sabam and Unisono
The results

Faster logins and less time wasted

The login chatbots handle 3.000+ conversations a month and solve 97% of the login issues in under 3 minutes. Not only are users happier and able to login in faster, but the Sabam and Unisono customer service representatives have more time to focus on answering questions that require human follow-up.






> 3 min.


Other work that will spark your interest

The natural evolution of mobile banking into conversational banking

The natural evolution of mobile banking into conversational banking

Rawbot answers Rawbank customers frequently asked questions
Rawbot is Rawbank’s virtual assistant that customers can chat with to complete financial transactions, as well as ask questions. But what makes Rawbot so popular? He’s easily accessible via the website, WhatsApp, Messenger, Instagram and Twitter.
The problem

How can we replicate mobile banking within a WhatsApp chatbot?

As the largest bank in the Democratic Republic of Congo, and with WhatsApp now the most-used messaging app in the country, Rawbank was looking for a way to bring traditional SMS banking to WhatsApp.* It’s important to note that the financial landscape in Africa is unique because it’s historically built on mobile banking. With very few physical banks and unreliable internet connections, SMS banking was and continues to be the norm for sending and receiving payments.

The project began purely focused on centralizing and automating questions coming from its +200,000 customers. However, Rawbank’s digital team quickly realized that they could also introduce financial transactions. The challenge then became how can we expand the MVP to:

    1. Enable secure financial transactions via WhatsApp
    2. Meet its customers where they are to answer their question

With these two criteria in mind, we worked together with Rawbank to develop an omnichannel, conversational banking strategy.
The solution

An omnichannel chatbot approach for mobile banking

Evolving from SMS to WhatsApp

When evolving from SMS to internet-based financial transactions, we first needed to overcome a few technical hurdles. Most importantly, there wasn’t an easy plug-and-play API to connect natural language processing (NLP) and bot-building software to the existing infrastructure. Instead, we built a customized microservice that acts as a translator between the newer cloud models and existing telco infrastructure.

One of the easier transitions from SMS to WhatsApp is that the mobile number remains the customer’s unique identifier. The other benefit is that by using WhatsApp there’s additional security and encryption built into the platform. It’s very difficult to hack WhatsApp and steal someone’s login– you’d basically have to steal someone’s phone.

For the MVP, we built two transactional flows. The first flow allows customers to check their balance in Congolese francs, euros and US dollars. The second flow allows customers to check their most recent transactions. The next version of Rawbot will include transactional flows that allow customers to send and receive money via WhatsApp.
Bank balance conversational flow

Introducing conversational AI to mobile banking

To start with conversational banking, we agreed on a scope of 50 intents that cover the most frequently asked questions from Rawbank customers. Since the vocabulary associated with banking is very specific and because many banking intents overlap with one another, we usually recommend working with entities, such as debit card, credit card, limit, etc. Using contextual entities instead of match entities, allows us to create a fallback system, so that instead of responding with “I don’t know,” Rawbot can clarify any doubts himself.

Intent and entity fallback system

If Rawbot’s between 70-85% sure of the intent, then he rephrases the intent as a question to confirm his suspicions. If he’s between 50-100% certain of the entity, he responds with a custom menu that proposes the most asked intents related to that entity. This approach enables Rawbot to resolve a majority of the incoming questions end-to-end so that only a small percentage of complex cases get passed on to Rawbank’s internal support team.

Tailoring the conversational design to match each channel

For Rawbot to be a success, it had to be easily accessible by Rawbank customers. The only way to achieve this was by adopting an omnichannel approach so that Rawbot was available on channels where Rawbank customers were already active.

Technically speaking, connecting a bot to different channels is quite simple. However, designing an omnichannel conversational strategy is much more difficult. It requires modifications and redesigns per channel so that the user has the best possible experience.

Every channel and platform is different. From the way the user interacts with the channel, to the context, interface, visualizations, technical restrictions, etc. For example, a chatbot begins the conversation by immediately introducing itself when the web chat is opened. However, on WhatsApp, the user must begin the conversation, which means that we had to foresee two different introduction flows.

Rawbot’s Whatsapp and Web introduction

Other differences are related to the channel interface and technical abilities. In a web chat, we can use carousels to visually highlight different options, but that feature isn’t available in WhatsApp, so we need to use a menu to present options.

Rawbot’s Whatsapp menu

Rawbot’s web menu carrousel

Or on Messenger, we can use buttons to help the user make quick choices, while on Twitter that feature doesn’t exist, so we need to use numbers.
The Results

Users are making the switch from SMS banking to WhatsApp banking

Rawbot chats with +6,500 customers a month. Half of those customers interact with him on WhatsApp, while the other half is divided among the website, Messenger, Instagram and Twitter.
These numbers continue to grow each month and we see that more and more customers are using WhatsApp to check their balances and most recent transactions. The transactional flows are triggered +800 times per month, and the next Rawbot release will include transactional payment flows where users can send and receive money through WhatsApp.


800 +



6500 +





Other work that will spark your interest

European Commission | Travel Chatbot

European Commission Logo

A chatbot can support travelers 24/7

Traveling is an activity that happens in different time zones and outside of the traditional office hours. However people sometimes need instant support. Artificial intelligence allows us to easily overcome the limitations of different time zones and traditional office hours by creating a chatbot that's available to support users 24 hours a day, 7 days a week, and 365 days a year.
Expand your comfort zone with Discover EU
The problem

Travelers need immediate support

Discover EU is an initiative led by the European Commission (E.C.) that helps 18-year-old E.U. citizens discover Europe by train. Many of these young Europeans are inexperienced, first-time travelers who find themselves in situations where they need immediate help (ex. I missed my train, what do I do now?). However, it’s costly and challenging to staff a large team of support agents outside traditional office hours and across different time zones.

To guarantee affordable, real-time support for young travelers, we worked with the E.C. to develop a chatbot that’s always available to answer young travelers’ questions.

The solution

A chatbot with a customized handover

We began the project by reviewing Discover EU’s database of user questions and identifying the 20 most frequently asked questions. This set of questions and answers became the foundation of the chatbot. 


We strengthened this chatbot implementation, by customizing the handover between the bot and a live agent. In cases when the bot can’t solve a user’s problem, we transition the user from the conversation with the bot to a conversation with a live support agent. During this transition, we use A.I. to tag the conversation with keywords and relevant information. These smart tags help the agents quickly understand and prioritize the conversations.

The results

Automated support that's always available

In the first month, the chatbot solved more 700+ questions and handed over approximately 150 questions to a live support agent. By automating 83% of the support requests, we solved travelers’ problems faster and gave the support agents the time and space to focus on more complicated requests. 







Other work that will spark your interest

European Parliament | Crisis Relief Chatbot

European Parliament logo

A smart chatbot can scale your crisis communication

Fast and accurate communication is critical during a crisis. But, it can be challenging to manage the mass influx of questions that accompany a crisis. We worked together with the European Parliament to build a smart chatbot that answers employee questions in real-time.
A screenshot of a Facebook post from the Crisis Center in Belgium about the Covid-19 crisis
The problem

Quickly communicating at scale

In a matter of hours, the Corona virus forced a majority of the European workforce to leave their offices and set-up a desk at home. Many companies lacked an official remote working policy, which only added to the confusion and stress caused by a global pandemic.


The European Parliament (E.P.) immediately recognized the importance of accurate communication during a crisis and briefed Campfire to build its first smart chatbot to help streamline and accelerate communication with its 11.000 employees stationed across Europe.

The solution

Real-time answers to questions

A.I. (artificial intelligence) is the best technology for answering a high volume of questions. By layering a basic chatbot with a robust A.I., we can ensure that the chatbot is “smart” and understands a wide variety of user questions. 


In less than a week, we published a first version of the chatbot the E.P.’s intranet. This bot could answer 50+ different questions about the Corona virus, the Corona crisis and the E.P.’s remote working policy. 


Because the pandemic evolved rapidly, we monitored the conversations daily. This allowed us to identify gaps in the bot’s knowledge database and improve the A.I. with continuous NLP (natural language processing) training. We also worked with health department experts to regularly update the bot with new information and regulations.


The results

Quick adoption and even quicker answers

In the first 6 months of the crisis, the bot answered 15.000 questions from E.P. employees. 

Using A.I. to build a smart chatbot meant that the HR Team and other internal experts could ensure that they answered employee questions in real-time and with accurate information. It also gave the E.P.’s internal team the flexibility to focus on more complicated cases that required individual follow-up. 







Other work that will spark your interest