AI Opportunity in Insurance, from Process Automation to Decision Support - with Gary Hagmueller
This week's episode is focused squarely on insurance. There's a lot to keep track of in the space from claims to underwriting to back in process automation to customer service every six months the landscape of AI vendors and known use cases in the enterprise companies changing in altering in part of our work involves staying on top of that that means speaking to heads of AI and innovation leaders at companies. You might know like Geico allstate or Axa. Some the biggest insurance players in the world as well as staying on top of the start up ecosystem this week we speak with one of the players in that. Startup ecosystem. Gary Moeller is the CEO and president of Clara Analytics Clara analytics based in the bay area. And they are focused squarely on insurance artificial intelligence applications Gary previously was the chief operating officer at a house. D One of the rare companies in Silicon Valley to raise hundred million dollars plus for an artificial intelligence company and he was before that the CFO at Zoro which is an incredibly successful subscription management payments. Firm out again in the bay area so gary has got a pretty storied past in the startup world. Clarice raised about twelve million in there. Certainly on the way up insurances ripe for disruption and there's plenty to cover so gary gives us his perspective on where is making its way into insurance where he thinks it's going to make the biggest impact in the relative near term without further ado. We're going to hop right. It says Gary Hag with Clara analytics here on the business podcast so Gary. I wanted to start us off with just your idea today as to where is making a difference in insurance what what functions. It's being adopted into where the traction is today. If we look at a in the Insurance Enterprise Great Question. Damn yeah so so. There's definitely a whole bunch of different places where we're starting to see a proliferate. I will say it's probably very early days really for a big time. So we're you know. Obviously we are very focused on the claims operation space and so we're seeing a variety of different places where this is getting applied. It's getting applied. At least we're we're we're seeing it generally in two flavors things that can kind of be automated away. You know think simple tasks that it you know today. You got a human doing that. Maybe doesn't need to be done and in the second place where we're seeing it. Generally is is occurring in places where there are very complex in weak signals. That have a pretty large bearing on the outcome of whatever the person is working on or whatever the group is working on in is really being used as an augmentation of human capability but so think about the ability to kind of see around the corner and figure out where the things that that could affect what they're working on positively or negatively are In giving them action in on so like nausea as he said. Our focus is on claims ops but yet we have it a guy on my board works in in underwriting and we seen a bunch of different places where this is starting to apply even in the actuarial space. But it's been you know it's really feels like there's a. There's a groundswell of interest activity coming. I like your break out here when you when you look at a impact and insurance. Maybe we could do this with any sector. But you're talking about two categories. One is what can be automated away. I like the term. A lot of vendors are afraid to use that phrase even because it it comes across. You know immoral. You're one of those automation. People stealing job I. I hear a lot of vendors been far too tender with being able to say that phrase Second informing decisions. So it's sounds like a short breaking things up into we look at insurance. What might be an example of each just to give people a Nice Representative Lens into space some automated stuff and then some some decision informing. So I'm GonNa give you some thoughts on both of those but I do WanNa touch on the point on the automation automation away. I feel like that's a topic. That comes up a lot in this whole a discussion on. I don't think it's as sinister as which you portrayed it as I think it's really a situation where there's a lot of tasks that are being done today that I guarantee you that people do not. I don't WanNa do. And it's part of their regular job and so if you free them from doing tasks that they don't WanNa do in focus them in on the things that they would rather be doing that. They are probably better at doing right. That actually ends up making everybody better off instead of giving you kind of an example right. There's a lot of places where you have things blow through processing right where you can get a claim you can analyze. The machine can analyze the claim. The machine can make a determination that like this routine claimed. Let's just go ahead and eight. You know issue payment or issue settlement or whatever On this particular thing so that's maybe an example of things that the kind of flow through the machine can take care of An ambitious close out without necessarily having to kick it up to somebody who is just going to you know. Look at it. Roll their eyes. It's just another one of these ones again. You know do a couple of things. Close it out and move on right. So that's kind of an example of the automation flow the other side of it. So think of it as kind of decision support or or kind of enhancements human enhancement is what I would basically think about it. Were that's where you know. This is this by the way is common across all areas of machine learning. Were what you're doing. If you're tapping into an appropriately. Large amount of data. You're going to begin to pick up weak signals right in in things that are actually deterministic. That most humans aren't going to be able to go off and do right in in. That's for two reasons number one. They may have been doing this job for ten fifteen whatever years to have a certain way of doing the job and they're just never going to look at those other sources of data right. The secondary part is that some of those sources flow in places that people generally don't even look at so if you've figured out how to tap into all these different data sources and you can then get a much more complete picture like in the case of the sorts of things that we do. We can do a much more complete sense of what's going on with an individual claiming give evidence on like exactly how to attack this problem. Right now to mitigate loss or wind up doing something that's going to wind up making the claiming happier subtle faster. That sort of thing in. Maybe there's an interesting sort of exercise that we could do so I guess one quick thing I did I certainly wouldn't call automation sinister per se. I think there are to be some cases where someone gets freed up to do something more cognitively interesting and it's a thank goodness kind of experience. There will be other times where you know. Abacha folks in India Needham Filing TPS reports anymore. You know what I mean. They'll find somebody else to work with point blank period. There's going to be that and I think everybody needs to be pretty honest about it but there will be plenty of of experiences where we'll be able to move people up in work more interesting things when you look in a business and obviously insurance is your space and you aim to sort of help. Maybe business leaders think through where I can find a fit. You look at a business and say oh here's some identifiable for our potentially automative bucket and then here's some ones that we can also identify for the decision bucket. How do we put on a pair of goggles to to see those opportunities an insurance? What might be helpful.