Think chatbots, intelligent virtual assistants, and digital employees. These and other related technologies enable computers to engage in dialogue with people in natural ways using conversational artificial intelligence (CAI).
For banks, CAI makes it possible to respond to customers’ questions more quickly, cost-effectively, and consistently than they could with a traditional workforce. Many banks have embarked on the CAI journey by launching chatbots. For example, HSBC and Bank
of America have introduced digital financial assistants Amy and Erica,
Most chatbots, however, fail to meet the objectives for which they were designed. Why? The
success of a chatbot program depends almost entirely on whether the humans developing it have the appropriate specialized experience and skills to tackle some very important questions.
What level of CAI is right for my bank?
Not all chatbots are created equal. With dozens of providers, it is important for a bank to select a platform with the right level of sophistication to meet its needs and goals. Broadly speaking, banks can choose from:
- Scripted bots – A scripted bot provides static responses to scripted, keyword-based questions or statements. Developers need to program every possible question and syntax (for example, “What’s my account balance?” and “What is the balance of my account?”)
and the corresponding answer. A scripted bot is the most simplistic of the chatbots and is not really based on artificial intelligence (AI).
- Contextual bots – Want to provide your banking customers with more than just canned answers to simple questions? Contextual bots use natural language understanding (NLU) to understand what the customer is saying or asking regardless of the specific
syntax she uses. These bots also keep track of the context of the interaction. For example, if a customer asks, “What is that?”, the chatbot knows what was discussed earlier in the conversation and understands what the customer is referring to.
- Learning bots – All chatbots must learn which questions to expect and which responses are appropriate. But platforms that have machine learning (ML) capabilities automate much of the learning process. There are different levels of ML; for example,
learning bots can learn different forms of the same question or discover entirely new topics and patterns. However, what all learning bots have in common is that they can be taught
how to learn. Thereafter, ML developers feed the bot vast amounts of data (for example, previous real responses to actual questions) so that it can learn from the data on its own.
While scripted chatbots can be implemented more quickly, their capabilities are limited. And they may end up leaving banking customers feeling underwhelmed. On the other hand, CAI platforms with NLU, ML, and contextual capabilities generally take longer
to develop but can more effectively address customers’ inquiries. CAI refers to these more advanced forms of chatbots.
What training data should my bank use?
A bank’s policies and procedures form the basis for the responses that its CAI provides. However, when banks begin to build CAI, they often realize that some of those policies are not documented anywhere. And, if they are, the documents are often outdated
or exist in conflicting versions. Identifying and fine-tuning the source of knowledge for CAI is a task that can take many months, but it is one that must be completed before banks can teach their CAI anything. If banks don’t take the time to feed the CAI
platform the right knowledge, it can end up doing more harm than good, becoming a source of confusion and misinformation for customers.
How will my bank keep the training data current?
Banking services evolve, banking trends move, customer preferences change, and new technologies emerge. So chatbots must constantly update their knowledge. Regardless of the method used to teach the CAI platform, your bank needs a clear plan for how the
CAI will update its knowledge in a consistent manner. Otherwise, CAI can become outdated very quickly.
What channels should the platform support?
Just as human brains help people interact with one another in a variety of ways, such as through voice, writing, or movement, a CAI platform helps banks interact with their customers through a number of channels such as web, mobile, text, interactive voice
response, digital voice (including web-based and smart speakers), other IoT devices, and social media. At the same time, not all channels work the same way. Certain types of interactions work better in certain channels. For example, summary account information
can easily be provisioned to customers via smart speakers, while interactions involving detailed transaction information are best suited to channels with a screen. Having clarity on the delivery channels planned for your bank is essential so that the CAI
engine can adequately support all of them rather than become a ‘one-channel wonder’.
What core systems should the platform integrate with? And how?
For CAI to be effective, it needs to be able to interact with core banking systems to process customer inquiries and requests. However, banks often have systems that operate in silos or are based on older technologies. Integration of the CAI with such systems
must be carefully handled. Banks must be able to gather and update information ideally in real-time, while maintaining the integrity and reliability of systems of record. And it is particularly important for banks to implement security measures to ensure that
the chatbot doesn’t open the door for fraudsters to sneak in.
How should my bank manage the change?
CAI represents a change in the way of doing things for banking customers and customer service agents alike. As with any big change, it’s important to have a plan in place to ensure smooth adoption. The right expectations need to be established regarding
what CAI will and will not do, and how to interact with it.
The real challenge
When it comes to CAI, the technology itself is no longer the challenge. There are dozens of chatbot and CAI tools and platforms available. The true challenge is making the right decisions when answering strategic questions, and executing on those decisions
effectively. This requires specialized, human intelligence in CAI for banking, which usually comes from professionals with solid experience developing CAI and other solutions to address the opportunities and constraints specific to the banking domain.
CAI is becoming a business imperative for banks. Are you thinking about how to best prepare your bank for it? If so, let’s start the conversation.