AI Unleashed: Revolutionizing CX in Insurance

Join us for an interactive session taking you on a journey into the future of insurance customer experience (CX). Immerse yourself in a dynamic scenario featuring a Virtual Insurance Advisor powered by cutting-edge AI technologies, including ChatGPT. Engage in lively discussions with peers in the industry as we explore the potential of technology for customer acquisition, underwriting efficiency, and customer-centric claims processing. Gain invaluable insights into AI-driven innovation in the insurance industry. Seize this unique opportunity to not only learn from industry leaders and innovators but also to actively shape the future of CX in insurance.


Transcription:

Amyn Rajan (00:08):
That's my cue. Hello. Hello. How are you? Thank you all so very much for coming to this session. AI Unleashed Revolutionizing CX and Insurance. My name is Amyn Rajan. I'm a Chief Strategy Officer at A-Max Auto Insurance. Our company is based out of Texas, but we serve several different states. We focus on non-standard auto insurance. If you don't know what non-standard is, talk to me afterwards if we can get into that. This will be a very interesting and a very different session because I'm going to do the least amount of talking. So I have the easiest job and you will do the most amount of participation. It's an interactive session. The way it's going to go is you're going to see a video just to spark an inspiration of what is a use case of AI in the insurance field. Then we'll do some polling to gauge your interest of what you want to talk about.

(00:59):
We'll have a discussion prompt, then your group within your tables. You're going to have a conversation within yourselves about what you know and about that particular topic. I have a few helpers over here. We have Caitlin, we have Alexis, and we have Grace somewhere over here. There she is. Okay. Just sort of facilitate some of these conversations. If you see on your table, you don't have a lot of people to see if you can join a different table so that the conversation is a little bit more lively. So with that, let's get started. Everybody ready to talk to each other? Okay. The hope is that this will spark enough conversations for you two, have a chat and then continue these conversations in your networking breaks. Alright, so let's go.

Video Presentation (01:58):
Hi, I'm Mary, your AI powered customer service representative. Are you shopping for coverage today? I am. I need renters and auto insurance. Great. If you give me some basic information, I can find plans, provide pricing, and help you purchase coverage. If you're purchasing today, here are some pieces of information you don't want to know.

(02:27):
I've been in a car accident. I'm sorry to hear that. Tom, is anyone hurt? Do you need me to call an ambulance? No, everyone is. Okay. I'm glad everyone is safe. Do you need me to call a tow truck? Yes, please. Okay, no problem. I've contacted the nearest AAA approved provider and given them your membership information. Whenever you're ready, I can help you with your first notice of loss. Oh, okay. Please take pictures of both vehicles, including closeups of the damage, take a wider shot of the general area so we can see if there were stop signs, stoplights, or anything else that's relevant to the incident. Thank you. Because of all of the information you provided during your onboarding, I should have everything I need to guide you through the reporting process. Then I'll introduce you to the human representative who will manage your claim with additional support from me. We'll have your back as a team to make the process as seamless as possible.

Amyn Rajan (03:28):
Alright, so this was a use case to see how AI can be used in purchasing a policy and then monitoring claims and then the post claim processing as well. Alright, so AI shaping the technology of the future of insurance. There's so many use cases similar to that that could be implemented in many different areas. These are some high level examples. As you can see. I'm sure you can think of several more as it happens to be that how can generative AI can be used in insurance. So here's a prompt for you. What aspect of NextGen CX insurance are you most interested in learning about? So if you can pull out your phone, go to that slider.com and put in the code and let's see what the group wants to talk about. All right. It seems like customer acquisition is definitely top of mind because of course that's where the customers are coming in. Alright, so here's the discussion prompt. What aspects of the virtual insurance advisor experience stood out to you from the video that we just watched? And how do you envision AI technologies like ChatGPT, transforming customer acquisition in the insurance industry? So we'll have about eight minutes or so within your groups to have this discussions amongst yourself and we'll have a few helpers sort of going around to see the facilitation is going well.

(05:17):
Your time starts now. Ashley, would you leave that in here?

Audience Member 1 (05:37):
I didn't know anybody was listening to our conversation. Okay. All eyes on you. What stood to me was, right, man, good discussion.

Amyn Rajan (05:50):
More of a discussion.

Audience Member 1 (07:00):
I'm actually a, I don't think it's obviously

Audience Member 2 (07:48):
Data to multiple platforms. And so the scenario we just saw, as we all know was extremely straightforward. But if you have a multi-part collision or if you do need medical help, or if you are trying to transition more of an umbrella policy over how does that meet that? And to your point, how does the data transfer properly to the appropriate systems and get it into the appropriate field? Otherwise, it's just kind of another layer. It's like getting to the center of truth. Do you can get what you need and then coming back. So how many layers do you get? Do you have to pass through before you actually get to what you need, which is reparation?

Audience Member 1 (08:34):
Well, and I think that's going to be the secret sauce for each of the tools. How do they complete, that's a two part sequence, right? You've got on the phone actually question, but then they're actually back end and the doing piece. How does that piece actually get done? That's where separate themselves.

Audience Member 3 (08:55):
Yeah. I think the conversational approach that AI example brought in is he asked for two products, right? I want auto and renters. So the fact that it's not like, do you want renters? It's not like a voice IVR, right? Yeah. Tell me what you want. It's very conversational, which I like. I feel that from a sales standpoint, because I've managed call centers and I've been on the carrier side at this point, what customers want is like, where's the price? Where do we begin? Right? So you can gather the preliminary information to at least bid out a price, and then from there, that's where you'll gather the VIN and the granular details. Because if you're already outside of the ballpark and they're paying a hundred bucks a month, you're 500. There's no sense in spending more time. So I think as an insurance agency, I want to maximize talk time or minimize talk time and maximize opportunities. So I think that this flow would allow you to get through there to deliver something to just see if there's an opportunity and should you invest more time

Audience Member 1 (09:52):
In this. We use drips that way.

Audience Member 3 (09:55):
Yeah.

Audience Member 1 (10:22):
Or APIs deliver that data. So what makes me so excited about this transition to, well, so I was going to call liberate innovations. We have a product very similar to what was displayed here on,

Amyn Rajan (12:19):
Alright, I hate to interrupt. I know the conversations are amazing, but I'll give you some more prompts and you can continue on these conversations as well. Okay. Alright. So next poll. What ethical concerns do you believe are most significant regarding the implementation of AI in the insurance industry? Let's try this. Okay. That's quite interesting. Yeah, please

Audience Member 4 (13:14):
More of a concern for the employee. Sorry. So I see the job displacement being more of a concern for the employee, whereas the lack of transparency or the data privacy concerns be more of a consumer concern and they're all concerns, by the way.

Amyn Rajan (13:31):
Yeah, no, that's quite interesting. You're right. You can look at it from various different way, but lack of transparency in the processes in itself. It seems to be quite relevant here. Alright, I would've done a second poll if there was a bunch of others, but there's only 10%. I'm going to pass on it. So your next discussion prompt is how can we ensure that AI algorithms are free from biases and are fair and equitable? If this topic works for the group, please go ahead and do that. If you continue on the conversation that you were already having, that's fine too. Alright. So once again, your eight minutes start now.

Audience Member 1 (14:32):
Right. So

Audience Member 5 (14:53):
In terms of what you were saying before, you said it was based

Audience Member 1 (14:57):
Aside from what she mentioned, dunno what

Audience Member 5 (15:00):
Name is, right. But you were going

Audience Member 1 (15:02):
To say something from the previous conversation and when

Audience Member 5 (15:06):
About it from the carrier perspective because as I'm sure

Audience Member 1 (15:09):
Agency, so think I guess.

Amyn Rajan (22:17):
All right, I'm up once again. I hope the conversations were interesting. We are almost towards the end of our session. I'm just wondering if there was something quite interesting that came out of your group conversations that somebody wants to just sort of share with a larger group anyway. Alright. We have no idea how to identify biases is a huge problem. How to even go about doing that before we can even solve for it. Great point. Anyone else? Any other conversations that came out in the group which could spark more discussions? All boring chats everywhere was, yeah, please.

Audience Member 1 (22:56):
Yeah. So

Audience Member 4 (22:58):
A lot of what we talked about was that the biases, and actually I would love to give credit for the great point to the young lady at our table. So the bias is inherent in the data. We believe that the data, that AI is only going to be as good as the data that's available to be used. And so today the bias is in the lack of data that has been collected around the sample size. Exactly. So as the data itself becomes more valid, more truthful, that problem hopefully corrects itself.

Amyn Rajan (23:30):
Anyone else? Alright, good. I really appreciate your Oh, sorry, go ahead please.

Audience Member 6 (23:39):
Since I'm in the consumer experience space, we just talked a lot about the importance of having the consumer or customer involved in the design process of these tools. Because my ongoing question is how often does this solution, this assistant solve the problem that you're trying to solve versus how often do you abandon the process because you're frustrated, you don't get an answer to your question or a solution to your problem. So I mean, we were even commenting on the visual aesthetic of the bot being designed by someone who would be attracted to that visual aesthetic versus not all consumers might be. So just making sure that you're considering the consumer perspective in the design of these tools, if it's a customer facing solution, is super, super important.

Amyn Rajan (24:35):
So you managed to identify bias in my presentation is what happened, so I appreciate that. Thank you all so very much for playing along with this experimental format to sort of spark some expertise within the group to have some conversations. And of course the goal is that it starts a conversation that you continue having as you go into your networking breaks too. Tomorrow we'll have the same exact session again at 11.45. If you want to meet a new group of people and have more conversations about that, please come on in or have somebody else come in and see if you can share your colleagues to come in and join the conversation. Thank you once again.