Where Conversational Interfaces Belong in Banking - with Shankar Narayanan


So shankar. I wanted to start off talking about what elements of workflows within banking where we can really apply conversational interfaces today. I think there's a lot of claims about ai taking over customer service or some other functions but of course it's more nuanced than app when you take a look at where your technologies being applied and what you see in the landscape. How would you summarize wear conversational interfaces fit in in banking right. So there's a lot of hype around conversational So i would like to break that particular meant we are on the very early stages of conditionally. I am in the technologies just evolving as long so in terms of in banking. I think the key use case for conditionally is of several but let me talk about the customer engagement side am banks are looking at cutting costs on call centers and reputation calls which comes into the call center they move into some form of a flow for chat bots and chad votes has to be intelligent enough to understand that alonso's and respond appropriately the challenge. Which we've been seeing and which most of the companies thunder companies are evolving from celebre. Give you an example. This has been restarted. This company was that everything's moved conversation and unstructured data. And we just happening where you have people chatting or come on what they can ask anything. Because there's no structured work or the zone many shropshire that they can ask anything. So you'll you'll heavy lifting is done by your systems in entirely to understand. The piece has to be good enough to understand the intent and appropriate the answer Their tools at one is banks have to be pretty strict in terms of how they respond just to make sure that the brand is kept so the way. If if it's an ai which is open to training or training without any human interface. It can this phone and get trained and If based on property may give a wrong response so if we need to have a better control on that and stock has to be built in that so what we are seeing or the bureau of let me give you an example right when we started in twenty seven twenty eight when we launched our first services with a bank the workload pretty structured the opportunity impact build a lot of variations on radiance fall the the intense again the stroke of the nlp to understand how pavilions for that to respond a car in the food has to happen is let me give an example if i make a query that hey there's my checkbook i applied for it guest today so you may have multiple variants which built in and the system understands what you intend hits and response to it. We launched. We had of art. Sixty thousand interactions per day mid some of the banks on viet launched in india. Where the there are twenty million customers and the operational team was overwhelmed. Because you can't keep having team billions so we have to build a deep learning mortar so that it auto trains and the billions auto bill so this my team both so there was a lot of learning which we act do as we each rated in canonisation layer journey the customer engagement side. That's the sign the law of other use cases which is emerging will the last few years especially in a machine comprehension whether market documents which banks have and. Let's assume that you are a relationship manager and you just want to know that on. How is the apple Gonna be doing tomorrow. And what does the cio report. Amancio information office of has created and the relationship manager doesn't have time to read through it so you have a reading the document which is being fed understanding the intense and comprehending it and you can quit any queries and it will not give up particular on servile pick relevant answers and showcase whether human gan understand it and pick up the knossos. It's such plus plus right. So i see that as a segment which we are working on with some max banks so using a for internal processes you have the rpm which is basically. That's a separate were to complete version. But in terms of con- additionally is fell focus on you. See a lot of use gives us or hr all the mundane tasks which people have to communicate with. A human is being moved onto box or workflow base os and that starts the shift which is happening. And we're seeing that. I have data which shows the in fact last month a one of the banks did six million interactions in a month. Or the because it's amazing but the final. Wally masur doing now just to clarify chocolate. This is six million internal interactions. You're talking about this. hr faculty here. No no no. No your customer writ large of a lincoln howard phasing customer actions retail banking iraq jumps rea-. Now that makes sense humans out there but just imagine a call center will not be able to have that kind of scalable volume now. There are lots of unique interactions which are happening which banks looking through. So i'll give you an example while the banks had to adam. Api just tell where the credit card is going to be delivered on which day just going to be delivered because they didn't have the use case but customer Asking that i applied for credit card. Where is it. I haven't received it so bank said okay. I don't want this to go to the call center. I want to based on customers asking these questions. Why don't i give a particular times time kind of thing where i can tell where the where the credit card is share not share so what is happening with conversation is if banks can leverage and i think banks are slowly understanding the scale of it

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