Scaling up banking customer service with chatbot
- 1.5 million
Euro saved in customer interaction
- 50 percent
Human interaction handeled by chatbot
Increase in customer satisfaction
A large European banking client found itself having limited capability when handling customer service function manually by answering phone calls. Customers need wait in lines and wait for long periods and lowering the consumer satisfaction and loyalty. When the bank decided to implement artificial intelligence to build chatbot at scale, Kili Technology became its trusted partner to facilitate training datasets to enrich the quality of the chatbot, and monitoring in production.
The need of chatbot to accompany customers
Like most banking institutions at the time, a large European bank client found itself need to handle customer service to answer daily questions or complaints from customer through phone calls by customer service operators. The old usual process was typically illustrated by a customer dialing in to a customer service number and had to wait in line until the operator picks up – and had to listen to a random “waiting song” repeated several times over. As the volume of customers dialing in are always far greater than the operators handling the calls, to customers this “waiting song” is playing in loop without end.
“We conducted a survey to our banking customers, and a major feedback was given to how long they had to wait just to ask questions. There was a barrier – we were unreachable to them.” explains Emma, the Head of Customer Success at the bank.
When these customers have urgent queries, this unreachability became a pivotal point against the bank, as they see their customers thinking to turn heads to alternatives. To solve this issue, an artificial intelligence (AI) chatbot development at scale was considered as a highly attractive alternative to the old-fashioned customer service calls. This AI chatbot would erase the high barrier between the bank and customers easily, responding to each one of customer’s question and message in real time, at all times. Additionally, chatbot also allows selfcare development, in which customers can get their answers quickly themselves and only escalate the conversation to be delegated to human operator only when there is value added.
The challenges of building AI chatbot at scale
Building an AI chatbot is not without its challenges. First, it is important to have a comprehensive list of intent that is clearly well-defined and is distinctive in each one to be recognizable by the chatbot. The challenge in this list of intent then comes to creating the variation of intent from the original list, that covers wide range and exhaustive enough for the AI model to become robust.
Moreover, other challenge is to facilitate a supervision function to the conversation conducted by the chatbot, be it cold supervision or live monitoring by a person – with a room for even the supervisor to jump in. This supervision would involve a grading to the chatbot conversation quality –whether the conversation was relevant – and the quality of the bot, whether the chatbot could handle the conversation properly, and if the bot could escalate the issue by delegating to a human customer service.
…the point is to be able to investigate where the AI bot got lost and tell whether this part of conversation should have been understood well. If we find gaps between intent, we shall add this missing intent so next time the bot will perfectly understand.
– Anthony, Head of AI Labs.
To address these challenges, the bank then needed to make sure that the AI chatbot is trained rigorously with bulk amount datasets that are accurately labelled entities covering the variation of intent in multiple languages.
Why Kili Technology
As the bank was already aware, data annotation process is vital to produce high quality datasets to train the chatbot to understand variety of intent. Based on the challenges mapped, the bank was looking for a data annotation company that could provide a simple tool with rigorous quality management and flexibility in collaboration. After testing several tools offered in the market, the bank considered Kili Technology to be superior against its competitors.
Working with Kili, the bank found it useful to leverage on advanced features such as consensus and honeypot to control of the data quality to make sure utterances are interpreted well into the correct intent. On top of that, Kili platform also simplifies the collaboration between data scientists and annotators during the labelling process, making learning and iteration quicker.
“It’s definitely worth it.”
Using chatbot, the bank sees a huge cost savings. The cost with customers when conducted with human operator costs 3 euro / interaction. The bank was conducting 1 million interaction as a basis to produce another 4 million incremental interaction. With human operator, this would cost EUR 3 million. When implementing the chatbot, it reduces the need to human interaction by 50%, therefore saving costs by EUR 1.5 million.
The impact is massive. We receive customer compliments praising our better customer service. We got around 60% ROI, that is pretty well. It’s definitely worth it.
– Emma, Head of Customer Success
- Labelled data plays a vital role in determining the accuracy of an AI chatbot, hence investing time and effort in data labelling is the utmost importance to ensure the success of project
- Human in the loop during the chatbot training and production is absolutely critical to create impact: save cost and improve productivity.
- It is important to select the suitable data annotation partner for your chatbot development. Factors such as robustness, quality management, and simplicity of collaboration are key.
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