Listen: Conversational AI for banks should ‘orchestrate’ customer interactions

Conversational artificial intelligence (AI), such as chatbots and virtual assistants, should facilitate enhanced communications between financial institutions and their customers rather than function as tech for tech’s sake. [caption id="attachment_221279" align="alignright" width="300"] Photo by CanStock[/caption] Key to a successful conversational AI framework is the ability to “orchestrate” a conversation between an agent and customer, providing context for both parties and increasing interaction value, Tony Lorentzen, senior vice president of intelligent engagement at AI software firm Nuance Communications , tells Bank Automation News in this episode of “The Buzz” podcast. An example of this is an interactive voice response application (IVR) determining the intent of the call for the agent. “You shouldn't structure conversational AI as a blocker to an agent. What you need to be thinking about is how do you how do you orchestrate a conversation?” Lorentzen says. “If you're a high-level consumer who's transferring funds, you dial into an IVR and say what your intent is. The IVR can determine who you are, then send you to an agent.” Lorentzen shares techniques for successful conversational AI implementation, including voice biometrics and customer sentiment analysis, along with the importance of automation for customer and employee experience. Bank Automation Summit, taking place March 1-2 in Charlotte, is the first and only event to focus solely on automation in banking. The event will feature the brightest minds from across financial services on intelligent automation strategies and deployment. Learn more and register here for Bank Automation Summit 2022 . Subscribe to The Buzz Podcast on iTunes , Spotify , Google podcast , or download the episode .

The following is a transcript generated by AI technology that has been lightly edited but still contains errors. Alijah Poindexter 00:05 Good day, and welcome to The Buzz, a bank automation news podcast. I'm Associate Editor Elijah Poindexter. Recently I spoke with Tony Lorenza, Senior Vice President of intelligence engagement solutions with AI software for nuance. I spoke to Mr. Lawrence and about best practices for conversational AI for financial institutions, along with techniques for creating an enhanced customer and employee experience through AI, I thinkTony Lorentzen 00:31 the first thing is to do is you got to define what what AI means for the sector, right. And for us, it's really three different things. One is when you think about AI and conversational AI, number one, automation is necessary. Consumers, they'll they're going to prefer automated channels, before trying to connect with an agent or waiting queue or waiting in line, to speak with an agent. And what we've seen is about 81% of all customers attempt to take care of their matters themselves before reaching out to representative. And we know that 67% of a survey that was done prefer self service over speaking to a representative point is, you know, consumers are just getting more and more used to automation and will continue to use it. The second is what we're going to get more into this is around connected experiences, about 73% of consumers will use more than one channel, you know, just think about, you know, when you call into a financial institution, you may go on the website, you know, figure out, hey, I'm trying to apply for this mortgage, you run into a problem, and you need to pick up the phone and, you know, talk to an agent, but that phone's gonna hit with an IVR first, right? So having that that entire experience, you know, connected, you know, makes for a more dynamic conversation with with a brand. So, getting back to your question, right? So when you think about AI, it's on automating it, how do we connect the experiences? And how do we create the trust, so the technologies we use to do that is one is around conversational AI. And that is all around, you know, how we create dialogue, natural language, understanding, you know, speech to text and stuff, text to speech technologies. Second, is we actually apply those very same technologies to agents. So when you think about the agent population, you know, in providing, you know, information about next best action, or here's a the best offer, or really understanding potentially, that the consumer, you know, maybe a bit frustrated, so, you know, understanding sentiment, and how do we how do we change the dialogue, so applying those those technologies as well, not just from the consumer brand conversation, but also to the consumer agent conversation. And then the last set of technologies we're bringing, as I mentioned before, is around voice biometrics, and how do we provide, you know, focus on securing them? So the sort of three sets of AI that we typically bring to deployment then is around conversational AI, or agent AI, and around security AI? So those are the three that are are typical in any of those large enterprise financial services, deployments? Alijah Poindexter 03:05 Are there any specific use cases, you can point to that you can point to that short sort of display the the value add that this, you know, provides for financial services, call centers, be a banks or credit unions, financial institutions, fintechs, etc? Tony Lorentzen 03:20 Yeah, well across them, you know, not to name names, but you know, when you think about financial services, institutions, a cost per contacts, with a consumer can run anywhere from, you know, 10 to $15 per contact. So imagine if you can automate, you know, 80% of those, I mean, it's huge, huge, huge money, that contact center saving, you know, so we typically will see that we can automate 80% Of those, those interactions. And that, you know, a lot a lot of times when we work with financial institutions, you know, 1%, change and automation, you know, equates to, you know, a million plus dollars in savings. So, so that's really when you think about the the end outcome of what we're after, that's the ultimate of what we're doing there. The second is, is you know, as I mentioned before, we don't want to do is block consumer from speaking to an agent so, you know, conversational AI is used to collect a bunch a bunch of information about the consumer and actually pass that to an agent. So things like agent handle time, you know, we've seen reductions of anywhere from 45 to 60 seconds, by just authenticating and identifying authenticating the consumer in the automated channel, whether it be voice or digital, you know, in passing information over to an agent so they can, you know, pick up the conversation in stride, if you will. So, that's the other area that we see you know, saving. So, you know, shaving a large amount of the time. So, the agent can really focus you know, on why the the consumer may be reaching out in the first place. Then lastly, we, as I mentioned before about AI is leveraging the AI to actually prompt agents with next best action possible offers, you know, how do we you know, how do they have to change business that tone, because the sensitive, you know, you know, may require that. So so how do we do all of that under, you know, with our AI actually poppin agent to do that with the consumer? Alijah Poindexter 05:11 So are there any concerns either internally or externally about over automation in the space, Tony Lorentzen 05:18 the way we structure it is you shouldn't structure as conversational AI being a blocker to an agent, right? What you need to be thinking about is how do you leverage conversational AI? And AI and security is that we talked about earlier? And how do you how do you orchestrate a conversation? That conversation could be with the automated system or that conversation, you know, has to be with an agent. So you know, think about it, you know, if you're, you know, high level, a consumer who's, you know, getting into trying to transfer funds, you dial into an IVR for as an example, that IVR and one auto brands, like, I can just say, what my intent is what I'm trying to do, that IVR can then determine that, identify who I am, if I'm a high net worth call, you know, customer, maybe I need this, you know, send me directly to an agent, do we use things, we use things like age detection, you know, maybe it's a more or more older person or senior person. And we want to treat that just a bit more differently and sending them over to an agent. So, so we do many, many different things during the context of a conversation. And then our belief is we orchestrate conversations, right? So we orchestrate the conversation based on the intent, the personalization, the history, the relationship, where they are in their their journey, there's a bunch of different factors that get pulled into that. So again, coming from AI is not the end all be all, and you shouldn't be thinking about it as blocking, you know, a consumer from reaching over to an agent, but should be part of an overall agent or overall conversation orchestration to be able to do that. Alijah Poindexter 06:53 How can they help banks and financial institutions during any period of disruption? Be it a new COVID variant? Or maybe a economic downturn, something like that? I was curious if you had any insights there? Tony Lorentzen 07:05 Yeah. It's a great a great, great question. And just look at what's happening in the world. Today, just in the month of January, there were over 9 million people who are unemployed. And also, when you think about, you know, your everyone's probably heard the great resignations, you know, for four and a half million Americans have volunteered to leave the workforce, right. So really think about it from a financial services company point of view, they're probably having a large, I have a hard time staffing agents. And that's, that's this real time that that's happening today. So really, then conversational AI becomes strategically important to drive automation to reduce that the stress that's really put in the system with, you know, contact centers, just not having enough staff staff to handle calls, not having enough staff to handle, you know, customer inquiries. In addition to that, when you think about when COVID first started, and a lot of this is still happening today, we just saw a large increase of actually volumes, because things like stimulus checks, you know, you know, 401k, loans, other things that were happening in market, and many financial services, companies roll out different programs and different incentives for their base as well. So it's actually generating more volume. So the combination of, you know, them not having enough staff to handle requests, and really then consumers, you know, whether it's macro events that are happening, like, you know, government, you know, stimulus checks and such, are generating just an, you know, massive amount of incoming volume. As much as 50%. We've seen in some financial services institutions, it's become really, really important to drive up levels of automation. The other thing I would say is we've seen fraud, really, you know, just the just the rise in fraud, guys, we talked to our customers, many of them cite that fraud is just just on the increase just, you know, just by the sheer quality and just, you know, people trying to break in. So that's where our security and Biometrics is also, you know, played a role to help reduce fraud in that context. Alijah Poindexter 09:06 You've been listening to the bus, a bank automation news podcast. Thank you for your time and be sure to visit us and make automation news.com For more automation news. You can also follow us on Twitter and LinkedIn. Please don't hesitate to rate this podcast on your podcast platform of choice. Thank you

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Listen: Conversational AI for banks should ‘orchestrate’ customer interactions

Successful AI adds context, insights to customer and employee experience

Fiserv to acquire cloud-native banking solution Finxact

Regulatory compliance is tricky, ignoring it is costly

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