Report: The Future of Insurance
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Report: The Future of Insurance
DOWNLOAD NOWIn the latest episode of The Marketing Rapport, host Tim Finnigan sits down with Aarti Gupta, the esteemed head of insurance at Amazon Web Services (AWS). Aarti, known for her data-driven prowess, dives into the transformative power of AI in the workplace and its profound impact on industries, particularly insurance.
Aarti emphasizes the challenges faced by data-rich organizations, highlighting the paradox of having vast amounts of data but struggling to convert it into actionable insights. She underscores the importance of a robust data foundation and a modern cloud-based architecture to effectively harness this data for business growth.
Concluding the discussion, Aarti touches upon the evolving customer expectations in the digital era. From generational differences in customer interactions to the need for personalized digital experiences, she offers a glimpse into the future of customer engagement in the insurance sector.
Introduction of Aarti Gupta
Timestamp: [00:00:30]
Tim Finnigan introduces Aarti Gupta, highlighting her sought-after expertise and her recent participation in a panel discussing the impact of AI in the workplace. Aarti’s reputation as a data-driven professional from AWS, who has been a part of numerous speaking engagements, sets the stage for the episode’s discussions.
“Her panel was about the impact of AI in the workplace. I’d like to introduce the head of insurance at AWS, Aarti Gupta.”
Aarti’s Role at AWS
Timestamp: [00:02:35]
Aarti elaborates on her role at AWS, where she leads the insurance business development and go-to-market strategies for the North American region. She discusses her work with BNC and life insurance companies, emphasizing cloud adoption and collaboration with partners.
“My role at AWS is, I lead the insurance business development and go-to-market for the insurance industry in particular for the North America region.”
Aarti’s Career Progression
Timestamp: [00:04:05]
Aarti reflects on her career journey, starting as a data scientist at Progressive, moving through various roles in the insurance sector, and finally landing her position at AWS. Her diverse background in insurance positions her uniquely to understand and address the challenges faced by the industry.
“Yeah, you’re taking me down a memory lane, but I’ll make it quick. So as a math major, data analytics has always been close to my heart.”
Challenges in Data Innovation
Timestamp: [00:07:00]
Aarti identifies key themes she has observed in her consultations with various companies. She discusses the challenges faced by data-rich organizations, the importance of accessing and gaining insights from data, and the need to meet ever-evolving customer expectations.
“So one of the key things that keeps popping up in my conversations is having access to data, your own data within the organizations. […] So how do you not just access the data, but position yourself to gain insights from the data for better decision making.”
[00:07:49] Aarti Gupta: “Insurance companies, financial services organizations just cannot continue to ignore the experience that all of us as customers are having in the broader landscape.”
[00:11:09] Aarti Gupta: “Companies need to think about creating that culture of innovation and experimentation. Because again, I think we can all keep sitting here and talking about data and how to use it. But the real value is in getting out there and doing it and finding those POCs, the proof of concept experimenting, learning from it, fine tuning it and then continuing to build upon that.”
[00:07:49] Aarti Gupta: “Having that mindset of customers first, so that product-centric and customer-first mindset is really important. We’ve had a lot of discussions today, but it’s not just about your own business, and it’s not just about technology either. It’s about how that technology is going to impact your operations, first of all. And then secondly, working backwards from the customer problems to create that next set of products and next set of those delightful experiences on behalf of your customers. So really anchoring on that is key.”
Tim Finnigan: [00:00:00] All right, so when I talked about the different guests that we’ve had. What I’m really proud of is the diversity of the types of industries that we’ve had represented on there. And our next guest, who is extremely data driven. Um, what I liked about her so much is that she’s sought, she sought after to do speaking engagements.
She’s here, obviously. She’s done many conferences. Um, she was just recently on a panel. Which I think is one of the coolest things. And it’s sort of what we’re talking about today. Her panel was impact of AI in the workplace. I’d like to introduce the head of insurance at AWS, Aarti Gupta.
Tim Finnigan: [00:01:00] That was exciting. Like that was like a behind the scenes. What happens at a podcast and Doug, you’ve done 450. This is like my 12th. So we’re right on the same. level. Aarti, thank you for being a guest on the Marketing Rapport.
Aarti Gupta: Thank you for having me. It’s a pleasure to be here. And thank you for making me the guinea pig.
Tim Finnigan: I know you’re not a guinea pig. You’re being a good sport for doing this live and we cannot go back on any of this, you know, and that’s, I’m really happy to be here, but to be fair. Aarti and I have spoken like 75 times over the last three weeks and we’ve rehearsed this whole thing. [00:02:00] Aarti, I’m going to blow off every question that we’ve rehearsed.
Perfect. Okay. Um, one of the things that I liked, um, when, when we were talking about it, I was like, okay, let’s talk about a theme. Like what should this podcast be about? And you said, you know, Our discussions, you’re like, I think it should be called the data strategy of innovation, which really fits in with this whole day and what we’re trying to accomplish.
So when you think of that theme, how does that fit in with what you’re currently doing at AWS and give them an idea of, of like, your current capabilities right now.
Aarti Gupta: Yeah, thank you for the question. So my role at AWS is I lead the insurance business development and go to market for the insurance industry in particular for North America region.
And what that really means is I work with both BNC and life insurance companies across North America in driving cloud adoption. So that’s one of the components. The other piece is I also work. Uh, quite extensively with our partners, such as various [00:03:00] independent solution vendors, as well as system integrators in really helping kind of positioning the benefits off AWS technology.
So when we talk about innovation and business transformation, that’s one of the key things, um, that I drive, uh, with the line of business leaders and technology executives alike and really helping them understand how to move faster. I mean, today we were. Listening to all the esteem gets here on the stage about, you know, how to use data better, how to really move fast and how to make that, um, agile decision making business decision making.
That’s one of the key things that I solve leveraging technology in my role with the lens off insurance on top because of my background.
Tim Finnigan: So already, when you talk about your background, really interesting because you’re at AWS now. And you started your career as a data scientist, right, at Progressive. How, like, real quick, you don’t have to go through the whole thing.
Yeah, how much time do we have? Because you’re, we want to talk more [00:04:00] about me. We’ve got that at the end. But I thought it was so interesting, just talk about that career progression to AWS.
Aarti Gupta: Yeah, you’re taking me down a memory lane, but I’ll make it quick. So, um, as a math major, I’ve always been, you know, data analytics has always been kind of close to my heart.
So, um, after I completed my master’s, I was recruited by progressive, like you mentioned as a data scientist. So that was kind of my entry into insurance and I thought it was a great role because it helped me. Kind of apply my academic learnings into real world use cases. Um, obviously within the space of insurance.
And then from there on, I would say over the next 20 years, both had progressive and then I actually moved over to all state insurance. I was, I think one of the things, um, I, in hindsight, I’m very happy I did that. I was very intentional about, um, signing up for roles and taking on assignments that really helped me kind of broaden my understanding of the insurance space.
So, for example, I, you know, I started out as a data scientist. I spent a [00:05:00] number of years in the actuarial space. I moved into underwriting. I moved. I had P&L accountability, um, during my days at both companies and then also, you know, innovation and product management. So I think that really grounded me deep into all aspects of insurance.
So when Amazon reached out to me, it was an interesting role at the beginning when I first started, and they were really looking for somebody that could just dive deep into Insurance and really kind of hit the ground running. So I think that diversity of the background that I had really positioned me well, because again, this role really entails, like I was talking about, not just working with technology executives, but especially the line of business leaders and really understanding, helping them understand how to leverage technology to solve for the pain points.
I mean, several of the discussions we had earlier today. So that’s been kind of my journey and, um, it really has helped me kind of lean into that background I had and then lean more now [00:06:00] into technology to help, uh, solve for those problems.
Tim Finnigan: So Aarti, so you started off at Progressive, Allstate, and obviously AWS, and I had mentioned you are in numerous conferences and speaking engagements.
Coupled with all the, sort of, the companies and who you consult with, what are some themes that you can pull from that you’ve identified on how companies are driving data innovation?
Aarti Gupta: Yeah, that’s, uh, that’s a great question, and I’ll just build upon. I know we’ve had so many wonderful discussions prior to me here on stage.
I’ll just like to build upon that. So my specialty is insurance. So insurance in particular, but I would even say financial service organizations at large, right? They tend to be data rich organizations. Um, and data really is, um, they anchor to kind of really drive that transformation that we all talk about at enterprise level and have that meaningful business decision making with speed with velocity.[00:07:00]
So one of the key things that I know I obviously consistently like that keeps popping up in my conversations is first is just having access to data. Your own data within the organizations, right? And I’m sure you see that, too. And I know other speakers were talking about that. So how do you really not just access the data, but really?
Position yourself to kind of gain insights from the data for that better decision making. So that’s that’s number one. Um, that, you know, keeps coming up. The second thing I would say is customer expectations are at an all time high. And what I mean by that is Insurance companies, you know, I think Anand was talking about the experience, like if you’re just trying to buy car insurance.
I mean, that’s such a simple example, but really powerful. So insurance companies, financial services organizations just cannot continue to ignore the experience that all of us as customers are having, you know, in the broader [00:08:00] landscape. Um, and there are generational differences between the expectations that the customers have.
So, for example, My mom, you know, she always just likes to talk to a person, uh, or even meet in person. Like, that’s her idea of the, um, you know, the customer experience she’s looking for. My college going kids, if they ever, you know, like, call them, they think something is wrong. Somebody died. You know, like, they just prefer, like, a digital thing.
So point being, depending on who we are talking to, who the audience is, who the customer is, that customer experience needs to be different. And then the last thing on that I would say is Um, the thing the other trend that I’ve seen is we need to find new approaches to identifying risk. And what I mean by that is, um, you know, let’s talk just again from an insurance world, um, catastrophic exposure.
We all see that the high frequency, these massive events, you know, whether it’s the wildfires or the hurricane activity, those are happening much [00:09:00] more frequently compared to the historical model. So as much as You know, we use the historical data to forecast for future when these models are now, um, not really serving the purpose just because patterns have changed or during COVID, we couldn’t send people to get blood drawn.
As an example, we got to understand how we are going to leverage our internal data, but enrich it also with the third party data. Again, you know, I’ll, I’ll point an example. So an AWS has data exchange. So sources like that. How do you really enrich your data and then go for gaining insights from
Tim Finnigan: that?
Thanks, Aarti. That’s a really good point. I just want to maybe go a little deeper on when you talked about the data rich organizations and how they have all this data, but they’re still struggling to leverage those data into insights. Is that sort of why it’s a good idea to really have a great data strategy going into it?
Aarti Gupta: Absolutely. Um, [00:10:00] I mean, I’ll just build upon what I just said that, um, organizations, you know, again, to really gain those insights that we’re discussing, you need to have a solid data foundation. I cannot tell you how often it comes up when customers tell me that. The major if they had major blockers, they are blockers in the sense off having, um, really the foundation, the data foundation ready to support the research support the evolution, you know, the technology bring.
So, so that’s a great starting point to have the data foundation. The second thing I would say is, you know, the data strategy also entails. That, um, hopefully you have a more modern data architecture, and I’m not trying to get technical, uh, but hopefully you have that presumably in the cloud so you can move fast and you, you can have all the relevant data in one place.
Again, not using a buzzword like data lake and those kind of things, but those are real things that you want to have so you can. Um, leverage the [00:11:00] data in one central place and being able to actually use the data to drive the business outcomes. Um, so I think these are some of the key things I would say, um, that companies need to think about and reading, creating that culture of innovation and experimentation.
Because again, I think we can all keep sitting here and talking about data and how to use it. But the real value is in getting out there and doing it and finding those P. O. C. Is the proof of concept experimenting, learning from it, fine tuning it and then continue to build upon that.
Tim Finnigan: Thanks already. And so I want to pivot a little and it’s come up a lot today.
Even Second City brought up a I and machine learning in their skit. So what? As in charge of insurance for AWS. So what? What’s your point of view on how a I can transform the insurance industry?
Aarti Gupta: Yeah, I mean, we’re seeing our customers use that pretty extensively, especially in the insurance space as well, as much as [00:12:00] insurance has been slow to kind of take that leap into innovation.
But before I get into that, I’m also going to point out, you know, from an Amazon standpoint, what we have been doing. So let me ask you a quick question, Tim. If you are a customer of Amazon mm-hmm. , I hope you are. Um, what’s your favorite experience working with Amazon? Well,
Tim Finnigan: first of all, Aarti, thank you.
That’s the first time I’ve ever been asked a question. , I feel like you want my opinion. I would love that. I love, and I, you, you, you had told me this is gonna happen. So, um, but I do like that how the, it’s easy to take things back. For Amazon Prime, for example, I have three daughters. My wife, when one of my daughters is going to a homecoming dance, they’ll buy eight dresses.
And then they’ll have to return seven of those and then ultimately my wife will forget about it and shove them in the corner so then I have to bring them to Kohl’s, um, to do that. I’m not sure what my answer was, I was just more complaining about that.
Aarti Gupta: No, it’s, it’s, it’s a great answer. It’s really talking [00:13:00] about, um, a great customer experience that Amazon has created for you.
Um, on behalf on behalf of you. So so I do want to just take us kind of moment to talk about that. A. I. Machine learning, you know, in the space of Amazon, it’s really in the DNA off us, um, as employees as Amazon, because We’ve been working on it for 20 years, because if you think about the e commerce recommendations that you know, if you’re a customer of Amazon, you’re used to that the route picking from the robots in our fulfillment centers is all powered by machine learning.
So you can get those packages faster. Uh, the Alexa. Everybody’s You know, familiar with that. The speech synthesis intent recognition. Um, and yeah, the capacity planning and route planning that you’re talking about by creating those experiences for our customers. So just makes it easy. So point being machine learning has been a big part of that for over 20 years.
And as we kind of take that we’re working with our customers in AWS, especially in the insurance [00:14:00] space. I think a big focus has been how do you automate tasks? I think that’s been, uh, one of the key areas, um, of innovation of applications of AI machine learning. So, for example, in the space of underwriting, um, we want to make sure that we can use the underwriters, um, kind of knowledge in more of strategic insights rather than just parsing through.
Documents after documents, because again, insurance tends to be a very paper driven business. So AI machine learning models can, you know, extract the relevant information, interpret the data, and that really speeds up business decision making. This and the same actually use case can apply to claims as well.
The other area we’re seeing a lot of traction is fraud detection. Um, because again, you know, humans are when you have volumes of data, humans, um, struggle with, you know, finding those patterns manually, but machine learning models are known to provide a lot of insights into detecting those [00:15:00] fraudulent behaviors and patterns.
So those are some of the examples I would say we are seeing our customers jump on in the insurance space.
Tim Finnigan: And so when you think of strategic planning, when you bring all this up, so what are some pitfalls or what should companies be thinking about when they’re doing strategic planning?
Aarti Gupta: Yeah, for strategic planning.
You know what? I always when I talk to my customers first, I would say having that mindset off, um, customers first so that product centric and customer first mindset is really important. We’ve had a lot of discussions today, but it’s not about just your own business, but it’s about and it’s not just about technology either.
It’s about even how that technology is going to impact Your operations, first of all, and then secondly, really working backwards from the customer problems to kind of create that next set of products and next set of those delightful experiences on behalf of your customers. [00:16:00] So really anchoring on that is key.
Um, I talked about experimentation, so I will kind of re emphasize that kind of creating that culture of innovation in your organization where you can continue to experiment, learn from it. Um, The second thing I would also say is education in terms of having a clear vision within your organization, because again, it’s not just about the senior executive leadership on what they are thinking the vision is, but it’s also.
Folks in your organization that are chartered with that agenda of driving forward and then also your end users. So especially when it comes to the combination of business and technology, because technology is just an enabler in your organization. It’s going to help you. make decisions faster. So whether it’s your users that are the end users that are consuming the output from the technology, or it’s the, you know, the users that are developing the technology.
I mean, we’ve talked a lot today about gen AI [00:17:00] example. So you need to make sure that whatever technology you’re going to use is also how it’s impacting your people in the organization. And you have that talent ready. So you need to be able to pivot that talent to make sure They are good with, you know, using it and continue to build upon that.
Um, and then one last thing, Tim, if I could also add having a level of technical flexibility, because again, I think sometimes we all get married to like a single approach, or we feel like we’re too invested in, um, some kind of thought or process, but having a flexible mindset and being able to because technology is moving really fast.
I mean, just in the last six months, we have seen so many models keep coming out, and it’s not going to stop, right? It’s going to change. So having that technical flexibility in your approach is also from a strategic planning will be helpful to keep it nimble and cost effective.
Tim Finnigan: Great. So already for our last, my last question.
So I know you’re [00:18:00] heavy into insurance, but with what trends do you see that can be applicable to other industries, not just the insurance space?
Aarti Gupta: Yeah, I think knowing your customer and I know on the panel before we were kind of laughing about the personalization and how much can you know about the customer.
So I think again, capturing the data on your customer, but the relevant data and how are you going to use that in the context? And I’ll just use a quick example that if somebody is a First time homebuyer versus somebody who is, you know, looking for insurance as a vacation home. Very different versus, you know, a first time home car insurance purchase versus if you’re buying car insurance for your Children, right?
So making it at the time of need is really important. And I think that applies to other industries, too. So whatever you’re marketing, making it relevant to the customer, but also the situation that they’re in. I talked about experimentation, so I think [00:19:00] having that clear vision, um, on the high level. So like we say at Amazon, being stubborn on the vision, but flexible on the details.
So I think how you get there is, um, it should be flexible. But the vision needs to be clear in the organization and then really leveraging technology to, um, um, capturing the data, storing the data. Are you even using the right data for the right use case? And wherever you can bring automation in your processes, I think that would be the key.
So some of those things again, you know, the data strategy really anchors it all. So I would say those all trends are up. Applicable to other industries, too.
Tim Finnigan: Great. Thanks, Aarti. I tricked you. We do have time for one more because I wanted you to hurry up through there. Cause I want to ask you what, what do you like best about me?
Uh, well, maybe we’ll do a whole new episode on the Tim and Aarti show, but I would love for you guys to give Aarti a round of applause for a great job and being a great sport on the first ever live marketing report [00:20:00] podcast.
Aarti Gupta: Thank you so much for having me. It was a pleasure.
Tim Finnigan: Okay. And we’re going on tour.
You and I for sure. All right. That’s it.
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