Published on July 26, 2018
As many of the industry analysis reports from McKinsey and BCG over the last few years point out, corporate banks have been the last to join the digital bandwagon. Corporate internet and mobile banking took off to some extent over the last decade. Straight through processing (STP) for various transactions through direct integrations between the ERPs and the banking systems has eased out a lot of friction in the business banking processes. However, the overall digitalisation is way less than the other lines of businesses in banking.
Even though many areas of corporate banking have undergone some amount of digital transformation, when it comes to customer experience, there is still a lot of dependence on the RM, even for simple queries starting from balance checks to financial transaction status. Such engagements serve the immediate requirement of the client but are of no real “value added” from a costly channel like the RM.
These engagements extend to contact centers through calls and emails which are all extremely high cost and high turn-around-time channels. The interactions are not exactly delightful when the client has to either wait in a call queue or be kept on hold for the agent to get the appropriate details to answer the query.
Most RMs engage with their clients through calls and other messaging channels which are outside the institutional purview. Thus, these interactions are not recorded in any way. Adding to that is the security consideration of such exchanges. The only form of authentication is the RM’s belief that he or she is speaking to the actual client on the other end of the channel. It is also well known that the attrition in the RM community is very high and along with the RM, all the insights on the clients, which should have been added to the institutional knowledge, also get lost to the institution.
Now cut to retail banking which has been at the forefront of digitalization. While “mobile first” was the mantra a few years back, today it is “AI first”. As various reports point out, AI investments in banks are the highest in the area of customer experience. We are seeing a big movement in retail banking customer experience where the interaction is moving from structured, form-based channels like internet banking and mobile applications, to unstructured and natural-language-based conversational engagements. There are multiple channels that the banks are opening up, ranging from chatbots on their websites and mobile applications, to going where the customers are, like Facebook messenger, WhatsApp and so on, to voice channels like Alexa and Google Assistant.
As mentioned earlier, corporate banks were always more into unstructured, natural language interactions. However, through these high cost channels and at times, channels that the institution has no insight into and with human authentication only, it ends up with no records. Moving to another conversational channel, which can be even lower cost than mobile and internet banking and available 24/7, can be the next big digital transformation for banks to serve their enterprise clients.
With AI based conversational banking, the banks can reach out to the business clients in a channel which they are already using. Most of them use the channel for their internal collaboration or to interact with their RMs, which means that the institutional data and knowledge will be saved. This will be in addition to internet banking and mobile banking application today. However, over a period of time, the conversational channels can see a much higher adoption. The AI based conversational channel should have the ability to seamlessly handover to the RM or a human agent when it cannot cater to the customer request, to ensure the service levels remain at a high level even in the early days when the system has had little training. In fact, the best experiences can be provided by such human-machine combinations instead of any one of them.
Conversational channels can be deployed as extensions of the website, internet banking and mobile banking, through additional channels like Skype, Lync, to name a few, that businesses use for their internal collaborations. Banking through Skype, Lync type channels by business clients essentially mean that the corporate users don’t have to open a different website or mobile application but instead continue with their banking from where they are collaborating with their colleagues for other purposes.
Besides these messenger channels, for immediate, short and critical queries of the CXOs, banks can create conversational experiences through voice channels like Alexa, GA and so on.
Conversational channels in business banking are relatively unexplored areas. On that note, it has great potential only if the channel specific customer experience (CX) design is completely reimagined for the channel and not replicated from web or mobile. Even the voice interface design (VUIs) should be differently thought of from the messenger conversations. Only then will the adoptions of these channels soar.
The conversation experience design should be a combination of the target persona in the corporate and the channel and must start from the requirements of the persona. A Juniper report shows that there is an average of four minutes savings per call when transactions are done over chatbot. In addition to that, a Medici report shows that 70% of customers today prefer a messenger channel over a call. Our belief is that a well-designed conversation engagement through any channel should bring down the time further and provide a delightful experience. The entire experience design should be on an extremely strong backbone of conversational AI, whose (1) natural language understanding (NLU) should be able to understand the nuances of complex human conversation and the corporate banking domain ontology, and (2) Machine Learning capabilities keep making the conversations more individualized to the corporate and the particular user.
One of the possible ways to start off the conversational channels for business banking is to analyze the high-volume interactions in the contact centers and with the RMs. While the contact center data will be easier to assimilate, the RM data points are equally important since that is probably the area that needs to be brought into the institutional purview asap. Such analysis will not only help the banks prioritize the type of interactions to provide in this channel, but the actual conversation data can be used for training the AI system on the utterances of the customers and the possible replies the bot can provide.
Providing a conversational channel for prospects (who are not yet customers of the bank) could be the very first step for business banking. This could be a completely new channel on the bank’s website to help out the prospects with products that they are seeking or providing the simplest and fastest way to frequently asked questions (FAQ). The experience can be truly delightful only if the AI engine understands the queries exactly and the replies can be given to the point with minimal redirection to elaborate documents. Wherever the queries are not clearly understood, a seamless and quick handover to a live agent could help keep the experience frictionless. This combination of AI based conversational channel and live agent, only in extreme cases, could be a great recipe for client conversion instead of an abandonment.
FAQs could be a good start for the conversational channel for existing customers as well. A lot of their generic queries do land up with the RMs and call centers. Having a simple self-help in their corporate messenger could be an immense help. The next phase could be to take up service requests, such as statements and certificates, and transaction approvals, like corporate multi-user approvals for high value transactions, through this channel. High volume financial inquiries like remittance status, beneficiary credit, invoice payment stage and so on could be the subsequent phase of the conversation channel. Based on the adoption, the conversation channel could provide all the high-volume interactions over a period of time.
Banks can also look at virtual assistants as a part of business internet banking that could be completely conversational. Corporate internet banking with its multitude of options could overwhelm the user and drive her to the call center. The conversational assistants can be invoked within the internet banking session that can help her navigate through the system or help her with a balance inquiry when she is in the middle of approving an ad-hoc payment without having to move out of the approval option. This can prevent that costly call to the bank and save her a lot of time as well.
In summary, we see that conversational channels based on powerful natural language capabilities and continuous machine learning can help banks provide their business customers a delightful, always available experience and reduce the usage of costly and cumbersome channels. That will essentially mean that they reach out to RMs and call centers for real value adds like advice on new facilities or better deployment of funds that can be more revenue generating for the bank… and a fringe benefit could be higher retention of RMs.
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