The natural evolution of mobile banking into conversational banking

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.

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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.

Rawbank Chatbot

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.

Rawbank Chatbot Carousel 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.

Whatsapp Chatbot menu
Rawbot's Whatsapp menu
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 +

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