(funky electronic music) >> From Phoenix, Arizona, theCUBE, at Catalyst Conference. Here's your host, Jeff Frick. (music muffles) >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in Phoenix, Arizona, at the Girls in Tech Catalyst Conference. About 4,000, or excuse me, 400 people, kind of a small conference, fourth year, growing in size. Going to be back in the Bay Area next year. Wanted to come down, check it out, always like to get, you know, kind of early on some of these conferences and really see what's going on. And we're really excited for our next guest, Monique Morrow, the CTO of New Frontiers Engineering inside of Cisco, welcome. >> Thank you very much, it's a pleasure to be here. >> So we've had a ton of Cisco guests on over the years, but I never heard the New Frontiers Engineering title, so what is New Frontiers Engineering? >> So New Frontiers is exactly what you think. You can imagine it's really forward thinking in terms of technology and research. This combinatorial intersection, if you will, with economics, and what could be potential portfolio for the future business of the company, so that's what I look at. You know, that's a special position, I could say, because you really want to make sure that you're not too far out to your core business, and you care about your core business always. >> Right, I was going to ask, how much of it's kind of accelerating the core versus, you know, kind of green field? I know, you know, we've had some of the team from the UCS group, and, you know, it's a growing business inside of Cisco, not really kind of core, what you think about, in terms of core switches, and stuff. It's servers, and a data center for structure beyond just the network. Is that some of the stuff that you guys look at? To go, kind of out on new branches? >> Well, certainly cloud, so data centers, with that is cloud computing, and then you've got mobile, and you have video. I would also say you have cyber security, internet of things, very, very important business analytics. So that's core business. And it could be accelerating what we have, but it also could be creating a new business opportunity. So the modus operandi, or the modality, if you will, is not to steer too far away from your core, the network does count. Software is going to be very, very important for us, service is absolutely important. So, you know, it's really steering the ship mid way, in such a way that you de risk what you're doing as you look forward. >> If only McNeely had said the cloud is the computer, (laughing) the network is the computer, right? >> So true. (laughing) >> So I want to touch base on your talk, Changing the Landscape of the Digitized World. >> Yes, yeah. >> What was that all about? >> So, you know, setting the landscape, there are several points that I wanted to make during that presentation, and really, to fire up the audience. One is that 51% of the global population are women, and women do count. That is change is extremely, it is exponential, probably always has been. That this is all about how do you keep your skills up at the end of the day? This is all about it is never too late to understand what's happening out there, and hear the skills buckets. So cyber security, analytics, what you do with data, mobility, collab, collaboration is probably the 21st century currency in anything that we're going to do because we're so global. The notion of what you do with other components here, not only the internet of things. And with the internet of things, you've got interesting aspects with privacy and how you handle privacy, privacy engineering, privacy by design, and all kinds of modality of cyber security. Because, you know, companies and customers are very concerned about ransomware, so think about phishing attacks. And I would say that that's just a start. >> Right, right. >> But, you have to juxtapose that with critical thinking skills, and something that we call T skills. It's interdisciplinary skill sets that are going to be asked for in this century, along with intergenerational teaming. So it's not just about working with millennials, but it's about working with people who've been in the business, it's the power of the and here, and that's really, really the focus. >> We're going to run out of time way too early, I already know this. But there's so many things you just touched on, specifically back to your skills comment. What's interesting is the technology is changing so fast, it's the new skills that are the kind of the driving new programming language, that you're almost in an advantage if you don't kind of have the legacy behind you. Because everyone is learning all these new languages, and these new ways to do things, that didn't exist just a short time ago. >> Well, coding is fundamental. I think that coding is going to be fundamental, but you can learn new programming languages if you learn at least the fundamentals of coding. What's really, really important is to be able to pivot your skills sets in such a way that you are keeping up with it. It's never, ever too late. Once you have a knowledge of a particular language, or a knowledge of a particular algorithm, or a way something works, you're going to be able to learn anything. My message was it's never too late. You can start to learn now. >> Right. >> So that's really important. >> And then the other piece on the T skills, again, the IOT's is a giant bundle that we could jump into for a long time. But, you know, as the machines start to take more and more of the low level work, and increasingly the mid level, and the higher level, it is incumbent on a person to really start to bring some context, bring some relative scale, bring, you know, a lot softer skills to help influence that activity in the correct way. >> Interdisciplinary skills are the ask for the 21st century. So for example, I was just at the school of, I was actually on a strategic advisory board for the School of Computer Science, a particular university here in the United States, and one of the asks was not only have the skill set of computer science, but oh, by the way, go take an improvisational class at their school of fine arts. So to have the ability to communicate, because communication skills are the number one skills that companies and enterprises are looking for. So interdisciplinary skills, big currency for the 21st century. >> Well that's interesting, 'cause I wonder how aggressively that communications message is weaved into, kind of, your classic STEM conversation. >> They are, well, they are very much weaved into the classic STEM conversation, and I would say it's STEAM, because you have to put A for art there. >> Well, there you go. (laughing) Fixed. >> So, to the classic conversation, you can be a savant in a particular science, but if you don't have the ability, and this is with enterprises essentially, to communicate and to be able to work in teams, it's going to be a dead end for you to come into the enterprise. So it's really, really important to have those skill sets. >> Yeah, so I want to shift gears a little bit. >> Sure. >> 'Cause not only do you have your day job at Cisco-- >> Yeah. >> But you're involved in a lot of, kind of, advocacy. >> Yes. >> So tell the audience some of the work that you're doing there. >> Yes, I mean, so one of the areas that I really care about is advocating for women, and women creating technology, women who were actually in technologies, so there is also the UN component of that. I think that's very, very important, tech policy component for it. The UN women's organization received the lowest budget of all of the UN, so getting more, remember the context, 51% of the worlds population are women, and so we have to go up, and down, and across the pyramids. And so we need that, that's the level of advocacy that I'm involved in, not only from a company and an industry perspective, but also from a UN related perspective, and a standard setting perspective. Because it is about about the power of the and, and our ultimate goal is to achieve gender neutraility, I think, at the end of the day. I recall one thing is that there are 17 UN sustainable goals that were contented and approved, really, by the United Nations this past September. Number one is ending poverty, number five is achieving gender equality. >> It's just those are such big problems, just, you know, you look at hunger. >> Yes. >> And it just seems this continual battle to try to make improvement, make improvement, make improvement, and yet we're continued to be surrounded, probably within blocks of where we're sitting now, with people that are not getting enough to eat. So how does education compare to that, or how tightly are they intertwined? And then, within education, is STEAM a leading edge? Is STEAM, you know, kind of a way to break through, and get more education? How does STEAM fit within the education broader? >> Oh, well, it's, (chuckling) it's all intertwined. >> I told you we weren't going to have enough time. (laughing) >> Yeah, so, it's all, it's really all intertwined at the end of the day. It's what is taught at what age group, it depends on whether you're in a developing country or a developed country. So we're, you know, in the United States advocating, and most of other countries advocating that technology STEAM be really taught at a very early age, you know, primary school. If you get skill sets really broadened and developed at and early age, you also develop the capacity to actually be able to work, or to be able to create, and to be able to add to your household. And if you're in a village, to be able to do some very creative things, too, because of what you're dealing with. So think about connecting here's the bigger problem that we, as an industry, want to solve. That is connecting one to two billion people on the internet in the next several years, and they're not going to be in North America, and they're not going to be in Europe. They're going to be in Africa. They're going to be in other countries of the world, and so we need to think creatively, working with people on the ground, learning from them, and not being techno, what was told to me, not to be techno colonialist at the same time. Because there's some very interesting solutions that are coming out of the countries that we could actually tap into. >> Right, and just to wrap, not that you don't have enough to do in your day job, (chuckling) or your global advocacy, but you're also a very prolific writer. >> Yes, I'm a, well, a prolific writer, and I'm so proud to have coauthored three books this year. one that is already out, is Disrupting Unemployment. The other two will be out in June, which is Inner Cloud Interoperability with our three other coauthors. And the third book, which I'm almost most proud of, is The Internet of Women Accelerating Cultural Change, and that will be out on June 30th of this year. >> You're a busy lady. >> Busy. (chuckling) >> Alright, well, Monique, thanks for taking a few minutes-- >> Thank you. >> Out of your busy day. You probably could've written another couple chapters-- (chuckling) >> In the 20 minutes that we've had together. I really appreciate the time. I look forward to really kind of looking for where your guys imprint starts coming out of the Cisco machine on the back and with the products. So thank you very much-- >> Thank you. >> For all your work. >> Well, it's a pleasure to be here. >> Absolutely. Jeff Frick, here at the Girls in Tech Catalyst Conference in Phoenix, Arizona. Thanks for watching. (funky electronic music)

>>Good afternoon, I'm wanting or evening depending >>On where you are and welcome to this breakout session around insurance, improve underwriting with better insights. >>So first and >>Foremost, let's summarize very quickly, um, who we're with and what we're talking about today. My name is Mooney castling, and I'm the managing director at Cloudera for the insurance vertical. And we have a sizeable presence in insurance. We have been working with insurance companies for a long time now, over 10 years, which in terms of insurances, maybe not that long, but for technology, it really is. And we're working with, as you can see some of the largest companies in the world and in the continents of the world. However, we also do a significant amount of work with smaller insurance companies, especially around specialty exposures and the regionals, the mutuals in property, casualty, general insurance, life, annuity, and health. So we have a vast experience of working with insurers. And, um, we'd like to talk a little bit today about what we're seeing recently in the underwriting space and what we can do to support the insurance industry >>In there. So >>Recently what we have been seeing, and it's actually accelerated as a result of their recent pandemic that we all have been going through. We see that insurers are putting even more emphasis on accounting for every individual customer's risks, lotta via commercial, a client or a personal person, personal insurance risk in a dynamic and a bespoke way. And what I mean with that is in a dynamic way, it means that risks and risk assessments change very regularly, right? Companies go into different business situations. People behave differently. Risks are changing all the time and they're changing per person. They're not changing the narrow generically my risk at a certain point of time in travel, for example, it might be very different than any of your risks, right? So what technology has started to enable is underwrite and assess those risks at those very specific individual levels. And you can see that insurers are investing in depth capability. The value of, um, artificial intelligence and underwriting is growing dramatically. As you see from some of those quotes here and also risks that were historically very difficult to assess such as networks, uh, vendor is global supply chains, um, works workers' compensation that has a lot of moving parts to it all the time and anything that deals with rapidly changing risks, exposures and people, and businesses have been supported more and more by technology such as ours to help account for that. >>And this is a bit a difficult slide. So bear with me for a second here. What this slide shows specifically for underwriting is how data-driven insights help manage underwriting. And what you see on the left side of this slide is the progress insurers make in analytical capabilities. And quite often the first steps are around reporting and that tends to be run from a data warehouse, operational data store, Starsky, Matt, um, data, uh, models. And then, and reporting really is, uh, quite often as a BI function, of course, a business intelligence function. And it really, you know, at a regular basis informs the company of what has been taken place now in the second phase, the middle dark, the middle color blue. The next step that is shore stage is to get into descriptive analytics. And what descriptive analytics really do is they try to describe what we're learning in reporting. >>So we're seeing certain events and sorts and findings and sorts of numbers and certain trends happening in reporting. And in the descriptive phase, we describe what this means and you know why this is happening. And then ultimately, and this is the holy grill, the end goal we like to get through predictive analytics. So we like to try to predict what is going to happen, uh, which risk is a good one to underwrite, you know, Watts next policy, a customer might need or wants water claims as we discuss it. And not a session today, uh, might become fatherless or a which one we can move straight through because they're not supposed to be any issues with it, both on the underwriting and the claims side. So that's where every insurer is shooting for right now. But most of them are not there yet. Totally. Right. So on the right side of this slide specifically for underwriting, we would, we like to show what types of data generally are being used in use cases around underwriting, in the different faces of maturity and analytics that I just described. >>So you will see that on the reporting side, in the beginning, we start with braids, information, quotes, information, submission information, bounding information. Um, then if you go to the descriptive phase, we start to add risk engineering information, risk reports, um, schedules of assets on the commercial side, because some are profiles, uh, as the descriptions move into some sort of an unstructured data environments, um, notes, diaries, claims notes, underwriting notes, risk engineering notes, transcripts of customer service calls, and then totally to the outer side of this baseball field looking slide, right? You will see the relatively new data sources that can add tremendous value. Um, but I'm not Whitely integrated yet. So I will walk through some use cases around specifically. So think about sensors, wearables, you know, sense of some people's bodies, sensors, moving assets for transportation, drone images for underwriting. It's not necessary anymore to send, uh, an inspection person and inspector or a risk risk inspector or engineer to every building. You know, insurers now fly drones over it, to look at the roofs, et cetera, photos. You know, we see it a lot in claims first notice of loss, but we also see it for underwriting purposes that policies all done out at pretty much say sent me pictures of your five most valuable assets in your home and we'll price your home and all its contents for you. So we start seeing more and more movements towards those, as I mentioned earlier, dynamic and bespoke types of underwriting. >>So this is how Cloudera supports those initiatives. So on the left side, you see data coming into your insurance company. There are all sorts of different states, Dara. Some of them aren't managed and controlled by you. Some audits you get from third parties and we'll talk about Della medics in a little bit. It's one of the use cases, the move into the data life cycle, the data journey. So the data is coming into your organization. You collected, you store it, you make it ready for utilization. You plop it, eat it in an operational environment for processing what in an analytical environment for analysis. And then you close on the loop and adjusted from the beginning if necessary, no specifically for insurance, which is if not the most regulated industry in the world it's coming awfully close. And it will come in as a, as a very admirable second or third. >>Um, it's critically important that that data is controlled and managed in the correct way on all the different regulations that, that we are subject to. So we do that in the cloud era share data experiment experience, which is where we make sure that the data is accessed by the right people. And that we always can track who did watch to any point in time to that data. Um, and that's all part of the Cloudera data platform. Now that whole environment that we run on premise as well as in the cloud or in multiple clouds or in hybrid, most insurers run hybrid models, which are part of that data on premise and part of the data and use cases and workloads in the cloud. We support enterprise use cases around on the writing in risk selection, individualized pricing, digital submissions, quote processing, the whole quote, quote bound process, digitally fraud and compliance evaluations and network analysis around, um, service providers. So I want to walk you through some of the use cases that we've seen in action recently that showcases how this >>Work in real life. First one >>Is to seize that group plus Cloudera, um, uh, full disclosure is obviously for the people that know a Dutch health insurer. I did not pick the one because I happen to be Dutch is just happens to be a fantastic use case and what they were struggling with as many, many insurance companies is that they had a legacy infrastructure that made it very difficult to combine data sets and get a full view of the customer and its needs. Um, as any ensure customer demands and needs are rapidly changing competition is changing. So C-SAT decided that they needed to do something about it. And they built a data platform on Cloudera that helps them do a couple of things. It helps them support customers better or proactively. So they got really good in pinging customers on what potential steps they need to take to improve on their health in a preventative way. >>But also they sped up rapidly their, uh, approvals of medical procedures, et cetera. And so that was the original intent, right? It's like serve the customers better or retain the customers, make sure what they have the right access to the right services when they need us in a proactive way. As a side effect of this, um, data platform. They also got much better in, um, preventing and predicting fraud and abuse, which is, um, the topic of the other session we're running today. So it really was a good success and they're very happy with it. And they're actually starting to see a significant uptick in their customer service, KPIs >>And results. >>The other one that I wanted to quickly mention is Octo as most of you know, Optune is a very, very large telemedics provider, telematics data provider globally speaking with Cloudera for quite some time, this one I want to showcase because it showcases what we can do with data in mass amounts. So for Octo, we, um, analyze on Cloudera 5 million connected cars, ongoing with 11 billion data points and really want to doing as the creating the algorithms and the models and insurers use to, um, to, um, run, um, tell them insurance telematics programs, right to pay as you drive B when you drive the, how you drive. And this whole telemedics part of insurance is actually growing very fast too in, in, still in solidified proof of concept mini projects, kind of initiatives. But, um, what we're succeeding is that companies are starting to offer more and more services around it. >>So they become preventative and predictive too. So now you got to the program staff being me as a dry for seeing Monique you're hopping in the car for two hours. Now, maybe it's time to take a break. Um, we see that there's a Starbucks coming up on the right or any coffee shop. That's part of a bigger chain. Uh, we know because you have that app on your phone, that you are a Starbucks user. So if you stop there, we'll give you a 50 discount on your regular coffee. So we start seeing these types of programs coming through to, again, keep people safe and keep cars safe, but primarily of course the people in it, and those are the types of use cases that we start seeing in that telematic space. >>This looks more complicated than it is. So bear with me for a second. This is a commercial example because we see a data work. A lot of data were going on in commercial insurance. It's not Leah personal insurance thing. Commercial is near and dear to my heart. It's where I started. I actually, for a long time, worked in global energy insurance. So what this one wheelie explains is how we can use sensors on people's outfits and people's clothes to manage risks and underwrite risks better. So there are programs now for manufacturing companies and for oil and gas, where the people that work in those places are having sensors as part of their work outfits. And it does a couple of things. It helps in workers' comp underwriting and claims because you can actually see where people are moving, what they are doing, how long they're working. >>Some of them even tracks some very basic health-related information like blood pressure and heartbeat and stuff like that, temperature. Um, so those are all good things. The other thing that had to us, it helps, um, it helps collect data on the specific risks and exposures. Again, we're getting more and more to individual underwriting or individual risk underwriting, who insurance companies that, that ensure these, these, um, commercial commercial enterprises. So they started giving discounts if the workers were sensors and ultimately if there is an unfortunate event and it like a big accident or big loss, it helps, uh, first responders very quickly identify where those workers are and, and, and if, and how they're moving, which is all very important to figure out who to help first in case something bad happens. Right? So these are the type of data that quite often got implements in one specific use case, and then get broadly move to other use cases or deployed into other use cases to help price risks better, better, and keep, you know, risks, better control, manage, and provide preventative care. Right? >>So these were some of the use cases that we run in the underwriting space that are very excited to talk about. So as a next step, what we would like you to do is considered opportunities in your own companies to advance whisk assessment specific to your individual customer's need. And again, customers can be people they can be enterprises to can be other, any, any insurable entity, right? The police physical dera.com solutions insurance, where you will find all our documentation assets and thought leadership around the topic. And if you ever want to chat about this, you know, please give me a call or schedule a meeting with us. I get very passionate about this topic. I'll gladly talk to you forever. If you happen to be based in the us and you ever need somebody to filibuster on insurance, please give me a call. I'll easily fit 24 hours on this one. Um, so please schedule a call with me. I promise to keep it short. So thank you very much for joining this session. And as the last thing I would like to remind all of you read our blogs, read our tweets. We'd our thought leadership around insurance. And as we all know, insurance is sexy.

(upbeat music) >> Good morning or good afternoon, or good evening depending on where you are, and welcome to this session: Reduce claims fraud with data. Very excited to have you all here. My name is Monique Hesseling and I'm Cloudera's managing director for the insurance vertical. First and foremost, we want to let you know that we know insurance. We have done it for a long time collectively, personally, I've done it for over 30 years. And, you know, as a proof of that, we want to let you know that we insure, we insure as well as we do data management work for the top global companies in the world, in north America, over property casualty, general insurance, health, and life and annuities. But besides that, we also take care of the data needs for some smaller insurance companies and specialty companies. So if you're not one of the huge glomar, conglomerates in the world, you are still perfectly fine with us. So why are we having this topic today? Really digital claims and digital claims management is accelerating. And that's based on a couple of things. First and foremost, customers are asking for it. Customers are used to doing their work more digitally over the last decennium or two. And secondly, with the last year or almost two, by now with the changes that we made in our work processes and the society at large around Covid, both regulators, as well as companies, have enabled digital processing and a digital journey to a degree that they've never done before. Now that had some really good impacts for claims handling. It did meant that customers were more satisfied. They felt they have more control over their processes in the claims, in the claims experience. It also reduced in a lot of cases, both in commercial lines, as well as in personal lines, the time periods that it took to settle on a claim. However, it, the more digital you go, it, it opened up more access points for fraudulence activities. So unfortunately we saw indicators of fraud, and fraud attempts, you know, creeping up over the last time period. So we thought it was a good moment to look at, you know, some use cases and some approaches insurers can take to manage that even better than they already are. And this is how we plan to do that. And this is how we see this in action. On the left side, you see progress of data analytics and data utilization around in this case, we're talking about claims fraud, but it's a generic picture. And really what it means is most companies that start with data efforts pretty much started around data warehousing and preliminary analytics and all around BI and reporting, which pretty much is understanding what we know, right? The data that we already have utilizing that to understand better what we know already. Now, when we move to the middle blue color, we get into different types of analytics. We get into exploratory data science, we get to predictions and we start getting in the space of describing what we can learn from what we know, but also start moving slowly into predicting. So first of all, learn and gather insights of what we already know, and then start augmenting with that with other data sets and other findings, so that we can start predicting for the future, what might happen. And that's the point where we get to AI, artificial intelligence and machine learning, which will help us predict which of our situations and claims are most likely to have a potential fraud or abuse scenario attached to it. So that's the path that insurers and other companies take in their data management and analytics environments. Now, if you look at the right side of this slide, you see data complexity per use cases in this case in fraud. So the bubbles represent the types of data that are being used, or the specific faces that we discussed on the left side. So for reporting, we used a DBA data policy verification, claims, files, staff data, that it tends to be heavily structured and already within the company itself. And when you go to the middle to the more descriptive basis, you start getting into unstructured data, you see a lot of unstructured text there, and we do a use case around that later. And this really enables us to better understand what the scenarios are that we're looking at and where the risks are around, in our example today, fraud, abuse and issues of resources. And then the more you go to the upper right corner, you see the outside of the baseball field, people refer to it, you see new unstructured data sources that are being used. You tend to see the more complex use cases. And we're looking at picture analysis, we're looking at voice analysis there. We're looking at geolocation. That's quite often the first one we look at. So this slide actually shows you the progress and the path in complexity and in utilization of data and analytical tool sets to manage data fraud, fraud use cases optimally. Now how we do that and how we look at that at Cloudera is actually not as complicated as this slide might want to, to, to give you an impression. So let's start at the left side, at the left side, you see the enterprise data, which is data that you as an organization have, or that you have access to. It doesn't have to be internal data, but quite often it is. Now that data goes into a data journey, right? It gets collected first. It gets manipulated and engineered so that people can do something with it. It gets stored something, you know, people need to have access to it. And then they get into analytical capabilities for insight gathering and utilization. Now, especially for insurance companies that all needs to be underpinned by a very, very strong security and governance environment. Because if not the most regulated industry in the world, insurance is awfully close. And if it's not the most regulated one, it's a close second. So it's critically important that insurers know where the data is, who has access to it, for what reason, what is being used for, so terms like lineage, transparency are crucial, crucially important for insurance. And we manage that in the shared data experience that goes over the whole Cloudera platform and every application, or tool, or experience, you use within Cloudera. And on the right side, you see the use cases that tend to be deployed around claims and claims fraud, claims fraud management. So over the last year or so, we've seen a lot of use cases around upcoding, people get one treatment or one fix on a car, but it gets coded as a more expensive one. That's a fraud scenario, right? We see also the more classical fraud things and we see anti-money laundering. So those are the types of use cases on the right side that we are supporting on the platform around claims fraud. And this is an example of how that actually looks like. Now, this is a one that it's actually a live one of a company that had claims that dealt with health situations and pain killers. So that obviously is relevant for health insurers, but you also see it in, in auto claims and car claims, right? You know, accidents. There are a lot of different claims scenarios, that have health risks associated with it. And what we did in this one is, we joined tables in a complex schema. So you have to look at the claimant, the physician, the hospital, all the providers that are involved, procedures that are being deployed medically, medicines has been utilized to uncover the full picture. Now that is a hard effort in itself, just for one claim at one scenario. But if you want to see if people are abusing, for example, painkillers in this scenario, you need to do that over every instant that this member, this claimant has, you know, with different doctors, with different hospitals, with different pharmacies or whatever, That classically it's a very complicated and complex the and costly data operations. So nowadays that tends to be done by graph databasing, right? So you put fraud rings within a graph database and walk the graph. And if you look at it here in that, you can see that in this case, that is a member that was shopping around for painkillers and went to different systems, and different providers to get multiple of the same big LR stat. You know, obviously we don't know what he or she did with it, but that's not the intent of the system. And that was actually a fraud and abuse case. So I want to share some customer success stories and recent AML and fraud use cases. And we have a couple of them and I'm not going to go in an awful lot of detail about them because we have some time to spend on one of them immediately after this. But one of them, for example, is voice analytics, which is a really interesting one. And on the baseball slide that I showed you earlier, that would be a right upper corner one. And what happened there is that an insurance company utilized the, the divorce records. They got from the customer service people, to try to predict which one were potentially fraudulence. And they did it in two ways. They look at actually the contents of what was being said. So they looked at certain words that were being used, certain trigger words, but they also were looking at tone of voice, pitch of voice, speed of talking. So they try to see trends there, and hear trends that would, that would ping them for a potential bad situation. Now, good and bad news of this proof of concept was, it's, we learned that it's very difficult, just because every human is different to get an indicator for bad behavior out of the pitch or the tone or the voice, you know, or those types of nonverbal communication in voice. But we did learn that it was easier to, to predict if a specific conversation needed to be transferred to somebody else based on emotion. You know, obviously as we all understand, life and health situations tend to come with emotions. Also, people either got very sad or they got very angry or, so the proof of concept didn't really get us to affirm understanding of potential fraudulence situation, but it did get us to a much better understanding of workflow around claims escalation in customer service, to route people, to the right person, depending on, you know, what they need, in that specific time. Another really interesting one, was around social media, geo open source, all sorts of data that we put together. And we linked to the second one that I listed on the slide here that was an on-prem deployment. And that was actually an analysis that regulators were asking for in a couple of countries for anti-money laundering scams, because there were some plots out there that networks of criminals would all buy low value policies, surrender them a couple of years later. And in that way, got criminal money into the regular amount of monetary system, whitewashed the money and this needed some very specific and very, very complex link analysis because there were fairly large networks of criminals that all needed to be tied together with the actions, with their policies to figure out where potential pin points were. And that also obviously included ecosystems, such as lawyers, administrative offices, all the other things. Now, but most, you know, exciting, I think that we see happening at the moment and we, we, you know, our partner, of analytics just went live with this with a large insurer, is that by looking at different types, that insurers already have, unstructured data, their claims notes, reports, claims filings, statements, voice records, augmented with information that they have access to, but that's not theirs. So it's just geo information obituary, social media, deployed on the cloud, and we can analyze claims much more effectively and efficiently, for fraud and litigation than ever before. And the first results over the last year or two, showcasing a significant decrease, significant decrease in claims expenses and, and an increase at the right moment of what a right amount in claims payments, which is obviously a good thing for insurers. Right? So having said all of that, I really would like to give Sri Ramaswamy, the CEO of Infinilytics, the opportunity to walk you through this use case, and actually show you how this looks like in real life. So Sri, here you go. >> So insurers often ask us this question, can AI help insurance companies, lower loss expenses, litigation, and help manage reserves better? We all know that insurance industry is majority, majority of it is unstructured data. Can AI analyze all of this historically, and look for patterns and trends to help workflows and improve process efficiencies. This is exactly why we brought together industry experts at Infinilytics to create the industry's very first pre-trained and pre-built insights engine called Charlee. Charlee basically summarizes all of the data, structured and unstructured. And when I say unstructured, I go back to what Monique, basically traded, you know, it is including documents, reports, third party, it reports and investigation, interviews, statements, claim notes included as well, and any third party enrichment that we can legally get our hands on, anything that helps the adjudicate, the claims better. That is all something that we can include as part of the analysis. And what Charlee does is takes all of this data and very neatly summarizes all of this, after the analysis into insights within a dashboard. Our proprietary natural language processing semantic models adds the explanation to our predictions and insights, which is the key element that makes all of our insights action. So let's just get into understanding what these steps are and how Charlie can help, you know, with the insights from the historical patterns in this case. So when the claim comes in, it comes with a lot of unstructured data and documents that the, the claims operations team have to utilize to adjudicate, to understand and adjudicate the claim in an efficient manner. You are looking at a lot of documents, correspondences reports, third party reports, and also statements that are recorded within the claim notes. What Charlee basically does is crunches all, all of this data, removes the noise from that and brings together five key elements, locations, texts, sentiments, entities, and timelines. In the next step. In the next step, we are basically utilizing Charlee's built-in proprietary natural language processing models to semantically understand and interpret all of that information and bring together those key elements into curated insights. And the way we do that is by building knowledge, graphs, and ontologies and dictionaries that can help understand the domain language and convert them into insights and predictions that we can display on the dashboard. And if you look at what is being presented in the dashboard, these are KPIs and metrics that are very interesting for a management staff or even the operations. So the management team can basically look at the dashboard and start with the summarized data and start to then dig deeper into each of the problematic areas and look at patterns at that point. And these patterns that we learn, from not only from what the system can provide, but also from the historic data, can help understand and uncover some of these patterns in the newer claims that are coming in. So important to learn from the historic learnings and apply those learnings in the new claims that are coming in. Let's just take a very quick example of what this is going to look like for a claims manager. So here the claims manager discovers from the summarized information that there are some problems in the claims that basically have an attorney involved. They have not even gone into litigation and they still are, you know, experiencing a very large average amount of claim loss when they compare to the benchmark. So this is where the manager wants to dig deeper and understand the patterns behind it from the historic data. And this has to look at the wealth of information that is sitting in the unstructured data. So Charlee basically pulls together all these topics, and summarizes these topics that are very specific to certain losses combined with entities and timelines and sentiments, and very quickly be able to show to the manager where the problematic areas are and what are those patterns leading to high, severe claims, whether it's litigation or whether it's just high, severe indemnity payments. And this is where the managers can adjust their workflows, based on what we can predict using those patterns that we have learned and predict the new claims. The operations team can also leverage Charlee's deep level insights, claim level insights, in the form of red flags, alerts and recommendations. They can also be trained using these recommendations, and the operations team can mitigate the claims much more effectively and proactively, using these kind of deep level insights that need to look at unstructured data. So at the, at the end, I would like to say that it is possible for us to achieve financial benefits, leveraging artificial intelligence platforms like Charlee and help the insurers learn from their historic data and being able to apply that to the new claims, to work, to adjust their workflows efficiently. >> Thank you very much Sri. That was very enlightening as always. And it's great to see that actually, some of the technology that we all work so hard on together, comes to fruition in, in cost savings and efficiencies and, and help insurers manage potential bad situations, such as claims fraud better, right? So to close this session out as a next step, we would really urge you to assess your available data sources and advanced or predictive fraud prevention capabilities, aligned with your digital initiatives to digital initiatives that we all embarked on, over the last year are creating a lot of new data that we can use to learn more. So that's a great thing. If you need to learn more, want to learn more about Cloudera and our insurance work and our insurance efforts call me, I'm very excited to talk about this forever. So if you want to give me a call or find a place to meet, when that's possible again, and schedule a meeting with us. And again, we love insurance. We'll gladly talk to you until SDC and parts of the United States, the cows come home about it. And we're done. I want to thank you all for attending this session, and hanging in there with us for about half an hour. And I hope you have a wonderful rest of the day.

>>Good morning or good afternoon or good evening, depending on where you are and welcome to this session, reduce claims, fraud, we're data, very excited to have you all here. My name is Winnie castling and I'm Cloudera as managing director for the insurance vertical. First and foremost, we want to let you know that we know insurance. We have done it for a long time. Collectively, personally, I've done it for over 30 years. And, you know, as a proof of that, we want to let you know that we insure, we insure as well as we do data management work for the top global companies in the world, in north America, over property casualty, general insurance health, and, um, life and annuities. But besides that, we also take care of the data needs for some smaller insurance companies and specialty companies. So if you're not one of the huge Glomar conglomerates in the world, you are still perfectly fine with us. >>So >>Why are we having this topic today? Really digital claims and digital claims management is accelerating. And that's based on a couple of things. First and foremost, customers are asking for it. Customers are used to doing their work more digitally over the last descending year or two. And secondly, with the last year or almost two, by now with the changes that we made in our work processes and in society at large around cuvettes, uh, both regulators, as well as companies have enabled digital processing and the digital journey to a degree that they've never done before. Now that had some really good impacts for claims handling. It did meant that customers were more satisfied. They felt they have more control over their processes in the cloud and the claims experience. It also reduced in a lot of cases, both in commercial lines, as well as in personal lines, the, um, the, the time periods that it took to settle on a claim. However, um, the more digital you go, it, it opened up more access points for fraud, illicit activities. So unfortunately we saw indicators of fraud and fraud attempts, you know, creeping up over the last time period. So we thought it was a good moment to look at, you know, some use cases and some approaches insurers can take to manage that even better than they already >>Are. >>And this is how we plan to do that. And this is how we see this in action. On the left side, you see progress of data analytics and data utilization, um, around, in this case, we're talking about claims fraud, but it's a generic picture. And really what it means is most companies that start with data affords pretty much start around data warehousing and we eliminate analytics and all around BI and reporting, which pretty much is understanding what we know, right? The data that we already have utilizing data to understand better what we know already. Now, when we move to the middle blue collar, we get into different types of analytics. We get into exploratory data science, we get to predictions and we start getting in the space of describing what we can learn from what we know, but also start moving slowly into predicting. So first of all, learn and gather insights of what we already know, and then start augmenting with that with other data sets and other findings, so that we can start predicting for the future, what might happen. >>And that's the point where we get to AI, artificial intelligence and machine learning, which will help us predict which of our situations and claims are most likely to have a potential fraud or abuse scenario attached to it. So that's the path that insurers and other companies take in their data management and analytics environments. Now, if you look at the right side of this light, you see data complexity per use cases in this case in fraud. So the bubbles represent the types of data that are being used, or the specific faces that we discussed on the left side. So for reporting, we used a TPA data, policy verification, um, claims file staff data, that it tends to be heavily structured and already within the company itself. And when you go to the middle to the more descriptive basis, you start getting into unstructured data, you see a lot of instructor texts there, and we do a use case around that later. >>And this really enables us to better understand what the scenarios are that we're looking at and where the risks are around. In our example today, fraud, abuse and issues of resources. And then the more you go to the upper right corner, you see the outside of the baseball field, people refer to it, you see new unstructured data sources that are being used. You tend to see the more complex use cases. And we're looking at picture analysis, we're looking at voice analysis there. We're looking at geolocation. That's quite often the first one we look at. So this slide actually shows you the progress and the path in complexity and in utilization of data and analytical tool sets to manage data fraud, fraud, use cases, optimally. >>Now how we do that and how we look at at a Cloudera is actually not as complicated as, as this slight might want to, um, to, to give you an impression. So let's start at the left side at the left side, you see the enterprise data, which is data that you as an organization have, or that you have access to. It doesn't have to be internal data, but quite often it is now that data goes into a data journey, right? It gets collected first. It gets manipulated and engineered so that people can do something with it. It gets stored something, you know, people need to have access to it. And then they get into analytical capabilities who are inside gathering and utilization. Now, especially for insurance companies that all needs to be underpinned by a very, very strong security and governance, uh, environment. Because if not the most regulated industry in the world, insurance is awfully close. >>And if it's not the most regulated one, it's a close second. So it's critically important that insurers know, um, where the data is, who has access to it for Rodriguez, uh, what is being used for so terms like lineage, transparency are crucial, crucially important for insurance. And we manage that in the shared data experience. So it goes over the whole Cloudera platform and every application or tool or experience you use would include Dao. And on the right side, you see the use cases that tend to be deployed around claims and claims fraud, claims, fraud management. So over the last year or so, we've seen a lot of use cases around upcoding people get one treatment or one fix on a car, but it gets coded as a more expensive one. That's a fraud scenario, right? We see also the more classical fraud things and we see anti money laundering. So those are the types of use cases on the right side that we are supporting, um, on the platform, uh, around, um, claims fraud. >>And this is an example of how that actually looks like now, this is a one that it's actually a live one of, uh, a company that had, um, claims that dealt with health situations and being killers. So that obviously is relevant for health insurers, but you also see it in, um, in auto claims and counterclaims, right, you know, accidents. There are a lot of different claims scenarios that have health risks associated with it. And what we did in this one is we joined tables in a complex schema. So we have to look at the claimant, the physician, the hospital, all the providers that are involved procedures that are being deployed. Medically medicines has been utilized to uncover the full picture. Now that is a hard effort in itself, just for one claim and one scenario. But if you want to see if people are abusing, for example, painkillers in this scenario, you need to do that over every instant that is member. >>This claimant has, you know, with different doctors, with different hospitals, with different pharmacies or whatever that classically it's a very complicated and complex, um, the and costly data operation. So nowadays that tends to be done by graph databases, right? So you put fraud rings within a graph database and walk the graph. And if you look at it here in batch, you can see that in this case, that is a member that was shopping around for being killers and went through different systems and different providers to get, um, multiple of the same big LR stat. You know, obviously we don't know what he or she did with it, but that's not the intent of the system. And that was actually a fraud and abuse case. >>So I want to share some customer success stories and recent, uh, AML and fraud use cases. And we have a couple of them and I'm not going to go in an awful lot of detail, um, about them because we have some time to spend on one of them immediately after this. But one of them for example, is voice analytics, which is a really interesting one. And on the baseball slide that I showed you earlier, that would be a right upper corner one. And what happened there is that an insurance company utilized the, uh, the voice records they got from the customer service people to try to predict which one were potentially fraud list. And they did it in two ways. They look at actually the contents of what was being said. So they looked at certain words that were being used certain trigger words, but they also were looking at tone of voice pitch of voice, uh, speed of talking. >>So they try to see trends there and hear trends that would, um, that would bring them for a potential bad situation. Now good and bad news of this proof of concept was it's. We learned that it's very difficult just because every human is different to get an indicator for bad behavior out of the pitch or the tone or the voice, you know, or those types of nonverbal communication in voice. But we did learn that it was easier to, to predict if a specific conversation needed to be transferred to somebody else based on emotion. You know, obviously as we all understand life and health situations tend to come with emotions, or so people either got very sad or they got very angry or so the proof of concept didn't really get us to a firm understanding of potential driverless situation, but it did get us to a much better understanding of workflow around, um, claims escalation, um, in customer service to route people, to the right person, depending on what they need. >>And that specific time, another really interesting one was around social media, geo open source, all sorts of data that we put together. And we linked to the second one that I listed on slide here that was an on-prem deployment. And that was actually an analysis that regulators were asking for in a couple of countries, uh, for anti money laundering scams, because there were some plots out there that networks of criminals would all buy the low value policies, surrendered them a couple of years later. And in that way, God criminal money into the regular amount of monetary system whitewashed the money and this needed some very specific and very, very complex link analysis because there were fairly large networks of criminals that all needed to be tied together, um, with the actions, with the policies to figure out where potential pain points were. And that also obviously included ecosystems, such as lawyers, administrative offices, all the other things, no, but most, you know, exciting. >>I think that we see happening at the moment and we, we, you know, our partner, if analytics just went live with this with a large insurer, is that by looking at different types that insurers already have, um, unstructured data, um, um, their claims nodes, um, repour its claims, filings, um, statements, voice records, augmented with information that they have access to, but that's not their ours such as geo information obituary, social media Boyd on the cloud. And we can analyze claims much more effectively and efficiently for fraud and litigation and alpha before. And the first results over the last year or two showcasing a significant degree is significant degrees in claims expenses and, um, and an increase at the right moment of what a right amount in claims payments, which is obviously a good thing for insurers. Right? So having said all of that, I really would like to give Sri Ramaswami, the CEO of infinite Lytics, the opportunity to walk you through this use case and actually show you how this looks like in real life. So Sheree, here >>You go. So >>Insurers often ask us this question, can AI help insurance companies, lower loss expenses, litigation, and help manage reserves better? We all know that insurance industry is majority. Majority of it is unstructured data. Can AI analyze all of this historically and look for patterns and trends to help workflows and improve process efficiencies. This is exactly why we brought together industry experts at infill lyrics to create the industries where very first pre-trained and prebuilt insights engine called Charlie, Charlie basically summarizes all of the data structured and unstructured. And when I say unstructured, I go back to what money basically traded. You know, it is including documents, reports, third-party, um, it reports and investigation, uh, interviews, statements, claim notes included as well at any third party enrichment that we can legally get our hands on anything that helps the adjudicate, the claims better. That is all something that we can include as part of the analysis. And what Charlie does is takes all of this data and very neatly summarizes all of this. After the analysis into insights within our dashboard, our proprietary naturally language processing semantic models adds the explanation to our predictions and insights, which is the key element that makes all of our insights >>Actually. So >>Let's just get into, um, standing what these steps are and how Charlie can help, um, you know, with the insights from the historical patterns in this case. So when the claim comes in, it comes with a lot of unstructured data and documents that the, uh, the claims operations team have to utilize to adjudicate, to understand and adjudicate the claim in an efficient manner. You are looking at a lot of documents, correspondences reports, third party reports, and also statements that are recorded within the claim notes. What Charlie basically does is crunches all, all of this data removes the noise from that and brings together five key elements, locations, texts, sentiments, entities, and timelines in the next step. >>In the next step, we are basically utilizing Charlie's built-in proprietary, natural language processing models to semantically understand and interpret all of that information and bring together those key elements into curated insights. And the way we do that is by building knowledge, graphs, and ontologies and dictionaries that can help understand the domain language and convert them into insights and predictions that we can display on the dash. Cool. And if you look at what has been presented in the dashboard, these are KPIs and metrics that are very interesting for a management staff or even the operations. So the management team can basically look at the dashboard and start with the summarized data and start to then dig deeper into each of the problematic areas and look at patterns at that point. And these patterns that we learn from not only from what the system can provide, but also from the historic data can help understand and uncover some of these patterns in the newer claims that are coming in so important to learn from the historic learnings and apply those learnings in the new claims that are coming in. >>Let's just take a very quick example of what this is going to look like a claims manager. So here the claims manager discovers from the summarized information that there are some problems in the claims that basically have an attorney involved. They have not even gone into litigation and they still are, you know, I'm experiencing a very large, um, average amount of claim loss when they compare to the benchmark. So this is where the manager wants to dig deeper and understand the patterns behind it from the historic data. And this has to look at the wealth of information that is sitting in the unstructured data. So Charlie basically pulls together all these topics and summarizes these topics that are very specific to certain losses combined with entities and timelines and sentiments, and very quickly be able to show to the manager where the problematic areas are and what are those patterns leading to high, severe claims, whether it's litigation or whether it's just high, severe indemnity payments. >>And this is where the managers can adjust their workflows based on what we can predict using those patterns that we have learned and predict the new claims, the operations team can also leverage Charlie's deep level insights, claim level insights, uh, in the form of red flags, alerts and recommendations. They can also be trained using these recommendations and the operations team can mitigate the claims much more effectively and proactively using these kind of deep level insights that need to look at unstructured data. So at the, at the end, I would like to say that it is possible for us to achieve financial benefits, leveraging artificial intelligence platforms like Charlie and help the insurers learn from their historic data and being able to apply that to the new claims, to work, to adjust their workflows efficiently. >>Thank you very much for you. That was very enlightening as always. And it's great to see that actually, some of the technology that we all work so hard on together, uh, comes to fruition in, in cost savings and efficiencies and, and help insurers manage potential bad situations, such as claims fraud batter, right? So to close this session out as a next step, we would really urge you to a Sasha available data sources and advanced or predictive fraud prevention capabilities aligned with your digital initiatives to digital initiatives that we all embarked on over the last year are creating a lot of new data that we can use to learn more. So that's a great thing. If you need to learn more at one to learn more about Cloudera and our insurance work and our insurance efforts, um, you to call me, uh, I'm very excited to talk about this forever. So if you want to give me a call or find a place to meet when that's possible again, and schedule a meeting with us, and again, we love insurance. We'll gladly talk to anyone until they say in parts of the United States, the cows come home about it. And we're dad. I want to thank you all for attending this session and hanging in there with us for about half an hour. And I hope you have a wonderful rest of the day. >>Good afternoon, I'm wanting or evening depending on where you are and welcome to this breakout session around insurance, improve underwriting with better insights. >>So first and >>Foremost, let's summarize very quickly, um, who we're with and what we're talking about today. My name is goonie castling, and I'm the managing director at Cloudera for the insurance vertical. And we have a sizeable presence in insurance. We have been working with insurance companies for a long time now, over 10 years, which in terms of insurance, it's maybe not that long, but for technology, it really is. And we're working with, as you can see some of the largest companies in the world and in the continents of the world. However, we also do a significant amount of work with smaller insurance companies, especially around specialty exposures and the regionals, the mutuals in property, casualty, general insurance, life, annuity, and health. So we have a vast experience of working with insurers. And, um, we'd like to talk a little bit today about what we're seeing recently in the underwriting space and what we can do to support the insurance industry in there. >>So >>Recently what we have been seeing, and it's actually accelerated as a result of the recent pandemic that we all have been going through. We see that insurers are putting even more emphasis on accounting for every individual customers with lotta be a commercial clients or a personal person, personal insurance risk in a dynamic and a B spoke way. And what I mean with that is in a dynamic, it means that risks and risk assessments change very regularly, right? Companies go into different business situations. People behave differently. Risks are changing all the time and the changing per person they're not changing the narrow generically my risk at a certain point of time in travel, for example, it might be very different than any of your risks, right? So what technology has started to enable is underwrite and assess those risks at those very specific individual levels. And you can see that insurers are investing in that capability. The value of, um, artificial intelligence and underwriting is growing dramatically. As you see from some of those quotes here and also risks that were historically very difficult to assess such as networks, uh, vendors, global supply chains, um, works workers' compensation that has a lot of moving parts to it all the time and anything that deals with rapidly changing risks, exposures and people, and businesses have been supported more and more by technology such as ours to help, uh, gone for that. >>And this is a bit of a difficult slide. So bear with me for a second here. What this slide shows specifically for underwriting is how data-driven insights help manage underwriting. And what you see on the left side of this slide is the progress in make in analytical capabilities. And quite often the first steps are around reporting and that tends to be run from a data warehouse, operational data store, Starsky, Matt, um, data, uh, models and reporting really is, uh, quite often as a BI function, of course, a business intelligence function. And it really, you know, at a regular basis informs the company of what has been taken place now in the second phase, the middle dark, the middle color blue. The next step that is shore stage is to get into descriptive analytics. And what descriptive analytics really do is they try to describe what we're learning in reporting. >>So we're seeing sorts and events and sorts and findings and sorts of numbers and certain trends happening in reporting. And in the descriptive phase, we describe what this means and you know why this is happening. And then ultimately, and this is the holy grill, the end goal we like to get through predictive analytics. So we like to try to predict what is going to happen, uh, which risk is a good one to underwrite, you know, watch next policy, a customer might need or wants water claims as we discuss it. And not a session today, uh, might become fraud or lists or a which one we can move straight through because they're not supposed to be any issues with it, both on the underwriting and the claims side. So that's where every insurer is shooting for right now. But most of them are not there yet. >>Totally. Right. So on the right side of this slide specifically for underwriting, we would, we like to show what types of data generally are being used in use cases around underwriting, in the different faces of maturity and analytics that I just described. So you will see that on the reporting side, in the beginning, we start with rates, information, quotes, information, submission information, bounding information. Um, then if you go to the descriptive phase, we start to add risk engineering information, risk reports, um, schedules of assets on the commercial side, because some are profiles, uh, as a descriptions, move into some sort of an unstructured data environment, um, notes, diaries, claims notes, underwriting notes, risk engineering notes, transcripts of customer service calls, and then totally to the other side of this baseball field looking slide, right? You will see the relatively new data sources that can add tremendous value. >>Um, but I'm not Whitely integrated yet. So I will walk through some use cases around these specifically. So think about sensors, wearables, you know, sensors on people's bodies, sensors, moving assets for transportation, drone images for underwriting. It's not necessary anymore to send, uh, an inspection person and inspector or risk, risk inspector or engineer to every building, you know, be insurers now, fly drones over it, to look at the roofs, et cetera, photos. You know, we see it a lot in claims first notice of loss, but we also see it for underwriting purposes that policies out there. Now that pretty much say sent me pictures of your five most valuable assets in your home and we'll price your home and all its contents for you. So we start seeing more and more movements towards those, as I mentioned earlier, dynamic and bespoke types of underwriting. >>So this is how Cloudera supports those initiatives. So on the left side, you see data coming into your insurance company. There are all sorts of different data. There are, some of them are managed and controlled by you. Some orders you get from third parties, and we'll talk about Della medics in a little bit. It's one of the use cases. They move into the data life cycle, the data journey. So the data is coming into your organization. You collected, you store it, you make it ready for utilization. You plop it either in an operational environment for processing or in an analytical environment for analysis. And then you close on the loop and adjusted from the beginning if necessary, no specifically for insurance, which is if not the most regulated industry in the world it's coming awfully close, and it will come in as a, a very admirable second or third. >>Um, it's critically important that that data is controlled and managed in the correct way on the old, the different regulations that, that we are subject to. So we do that in the cloud era Sharon's data experiment experience, which is where we make sure that the data is accessed by the right people. And that we always can track who did watch to any point in time to that data. Um, and that's all part of the Cloudera data platform. Now that whole environment that we run on premise as well as in the cloud or in multiple clouds or in hybrids, most insurers run hybrid models, which are part of the data on premise and part of the data and use cases and workloads in the clouds. We support enterprise use cases around on the writing in risk selection, individualized pricing, digital submissions, quote processing, the whole quote, quote bound process, digitally fraud and compliance evaluations and network analysis around, um, service providers. So I want to walk you to some of the use cases that we've seen in action recently that showcases how this work in real life. >>First one >>Is to seize that group plus Cloudera, um, uh, full disclosure. This is obviously for the people that know a Dutch health insurer. I did not pick the one because I happen to be dodged is just happens to be a fantastic use case and what they were struggling with as many, many insurance companies is that they had a legacy infrastructure that made it very difficult to combine data sets and get a full view of the customer and its needs. Um, as any insurer, customer demands and needs are rapidly changing competition is changing. So C-SAT decided that they needed to do something about it. And they built a data platform on Cloudera that helps them do a couple of things. It helps them support customers better or proactively. So they got really good in pinging customers on what potential steps they need to take to improve on their health in a preventative way. >>But also they sped up rapidly their, uh, approvals of medical procedures, et cetera. And so that was the original intent, right? It's like serve the customers better or retain the customers, make sure what they have the right access to the right services when they need it in a proactive way. As a side effect of this, um, data platform. They also got much better in, um, preventing and predicting fraud and abuse, which is, um, the topic of the other session we're running today. So it really was a good success and they're very happy with it. And they're actually starting to see a significant uptick in their customer service, KPIs and results. The other one that I wanted to quickly mention is Octo. As most of you know, Optune is a very, very large telemedics provider, telematics data provider globally. It's been with Cloudera for quite some time. >>This one I want to showcase because it showcases what we can do with data in mass amounts. So for Octo, we, um, analyze on Cloudera 5 million connected cars, ongoing with 11 billion data points. And really what they're doing is the creating the algorithms and the models and insurers use to, um, to, um, run, um, tell them insurance, telematics programs made to pay as you drive pay when you drive, pay, how you drive. And this whole telemedics part of insurance is actually growing very fast too, in, in, still in sort of a proof of concept mini projects, kind of initiatives. But, um, what we're succeeding is that companies are starting to offer more and more services around it. So they become preventative and predictive too. So now you got to the program staff being me as a driver saying, Monique, you're hopping in the car for two hours. >>Now, maybe it's time you take a break. Um, we see that there's a Starbucks coming up on the ride or any coffee shop. That's part of a bigger chain. Uh, we know because you have that app on your phone, that you are a Starbucks user. So if you stop there, we'll give you a 50 cents discount on your regular coffee. So we start seeing these types of programs coming through to, again, keep people safe and keep cars safe, but primarily of course the people in it, and those are the types of use cases that we start seeing in that telematic space. >>This looks more complicated than it is. So bear with me for a second. This is a commercial example because we see a data work. A lot of data were going on in commercial insurance. It's not Leah personal insurance thing. Commercial is near and dear to my heart. That's where I started. I actually, for a long time, worked in global energy insurance. So what this one wheelie explains is how we can use sensors on people's outfits and people's clothes to manage risks and underwrite risks better. So there are programs now for manufacturing companies and for oil and gas, where the people that work in those places are having sensors as part of their work outfits. And it does a couple of things. It helps in workers' comp underwriting and claims because you can actually see where people are moving, what they are doing, how long they're working. >>Some of them even tracks some very basic health-related information like blood pressure and heartbeat and stuff like that, temperature. Um, so those are all good things. The other thing that had to us, it helps, um, it helps collect data on the specific risks and exposures. Again, we're getting more and more to individual underwriting or individual risk underwriting, who insurance companies that, that ensure these, these, um, commercial, commercial, um, enterprises. So they started giving discounts if the workers were sensors and ultimately if there is an unfortunate event and it like a big accident or big loss, it helps, uh, first responders very quickly identify where those workers are. And, and, and if, and how they're moving, which is all very important to figure out who to help first in case something bad happens. Right? So these are the type of data that quite often got implements in one specific use case, and then get broadly moved to other use cases or deployed into other use cases to help price risks, betters better, and keep, you know, risks, better control, manage, and provide preventative care. Right? >>So these were some of the use cases that we run in the underwriting space that are very excited to talk about. So as a next step, what we would like you to do is considered opportunities in your own companies to advance risk assessment specific to your individual customer's need. And again, customers can be people they can be enterprises to can be other any, any insurable entity, right? The please physical dera.com solutions insurance, where you will find all our documentation assets and thought leadership around the topic. And if you ever want to chat about this, please give me a call or schedule a meeting with us. I get very passionate about this topic. I'll gladly talk to you forever. If you happen to be based in the us and you ever need somebody to filibuster on insurance, please give me a call. I'll easily fit 24 hours on this one. Um, so please schedule a call with me. I promise to keep it short. So thank you very much for joining this session. And as a last thing, I would like to remind all of you read our blogs, read our tweets. We'd our thought leadership around insurance. And as we all know, insurance is sexy.