Maezli by SPYRI, Johanna

Premonition The post Toby Unwin – Testimony To Louisiana Department of Insurance Automobile Committee appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition The post Toby Unwin – Presentation To Louisiana Department of Insurance appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition I Think the Fundamental Problem With Law Is That Financial Incentives Are Misaligned The post Lawless Tech Podcast with Toby Unwin appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition Brandon Righi explains more about forthcoming RIMS2018 conference in San Antonio The post Premonition Podcast 9 : Brandon Righi, Program & Content Manager at RIMS appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition A discussion that explores how insurance incumbents, are engaging with their InsurTech future The post Premonition Podcast 8 : Greg Brown, Partner at Oxbow Partners appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition A little about the concept of blockchain, a lot about the impact of legal tech. The post Premonition Podcast 7 : Stephen Turner from Lawyers of Tomorrow appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition How to reinvent law and why, quality and metrics will play a crucial part. The post Premonition Podcast 6 : Professor Gillian Hadfield, Author of ‘Rules for a flat world’ appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition Discussion with Jeff Seder, Entremanure, and Architect of Moneyball for Horse Racing “it’s not how fast the horse goes, it’s how the horse goes fast” For more than three decades, Jeffrey Seder, a Harvard-educated lawyer and self-described “entremanure” has spent millions to research and refine the science of picking a champion racehorse. His remarkable insight has delivered remarkable success not least, helping the owners of “American Pharoah” spot the potential of a future Triple Crown winner in the US.  His findings are challenging pre-conceived ideas of how to find great horses and we’ll give you a clue, it’s no longer just about the breeding.  His concept of Moneyball for Horse Racing chimed with our own Moneyball for Law, so we just had to get him on the Premonition Podcast. Click here for more information about Jeff Seder and his remarkable story or visit his website : http://www.eqb.com In the meantime – enjoy the podcast, the transcript of which is below; Andrew Weaver: Jeff, Welcome to the Premonition Podcast. Thank you for joining us. Jeff Seder: Happy to be here. Thank you for the invitation. AW: Great to speak to you on a number of levels. I was mentioning to Jeff before we recorded that my family actually have a background in horse racing, so on a very personal level, Jeff, fascinated to see what you’re doing or what you’ve achieved with this. And obviously the Moneyball angle from a premonition basis, from a big data basis is really fascinating particularly in a world where you wouldn’t think the data would have made the impact that it has. So just to reel us back a bit. Take us back to the beginning. You’ll love affair with horses began with a date I understand. JS: Yeah, I was in law school and I went on a date. She took me to a rental stable. And we went out on horses and I kinda fell in love with the horses instead of her. And started taking lessons. I said it’d be a lot more fun if I didn’t have to worry about falling off and then I started taking lots of lessons and then I bought a horse and then I rented a farm and away we went. I don’t know if you know enough about American politics but when Richard Nixon was in there and he got thrown out for Watergate. The thing that precipitated it was after Watergate they hired a special prosecutor, Archibald Cox from Harvard and when I was in Harvard Law School he was my advisor right after that. So I had to go to him and tell him what my thesis was. I was in a law and business programme and I hadn’t done anything and it was near the end of the year and I had to go in there and talk to him about my progress, which was nonexistent. So I went in there and I thought he was gonna throw me out and it’s gonna be a disaster. And he asked me, “Where are we?” And I said … And he said, “Well what are you in interested in?” And I look down at the floor and I said, “Horses.” And I thought that would be it and he turned around and he got this huge book. His office was in the stacks, which was like four levels beneath the earth in a dark section of the library where they store all the books and stuff. He turns around and he pulls this huge book from behind him and he drops it on the desk and the dust all goes up and he says, “This is the statute that governs horse racing in the state of Massachusetts. I don’t think anybody at Harvard has ever looked at it. Why don’t you do that?” So I went crazy doing that and I found out that … I went through the finances of the racetrack, which was all completely sleazy and bogus and I just had a field day with it and I ended up getting a “A”. And then I thought I’d really like to do something with horses but I couldn’t see anywhere but I had no background in it. I had no connections. I was starting to be a good rider. I didn’t know anybody really and I thought, “Well, what the hell can I do?” The only place I saw where I could make the kind of money I wanted to make was in horse racing. So I looked at that and I thought, “Wow”. The more I looked at it and through this research I had done on the racetrack it was 300 back in what they were doing and it was 1976. And the Olympics, the East German just burst onto the scene in the Olympics. And they were winning all these medals, this little country. Before that it was all a fight between the Russians and the American for gold and who had the most gold medals and now all of the sudden this little country … And everybody was horrified and the said, “How are they doing it?” And they thought there were mad scientists taking kids out of kindergartens. It turned out a lot of it was steroids but nobody knew that. Well anyway, I was a young lawyer and I was fortunate enough to be asked to be part of a group that was starting to do something in the United States about it in sports medicine research for the United States Olympic Committee. So that was my first gig and so I started working very heavily with the people who were experts in biomechanics and all the different things that were related to exercise, physiology and sport and the more I got into it the more I realised that racing was really in the dark ages. And I said, well, look I have this education and science … I was premed also at Harvard … education and science and statistics and business and here I am in the middle of this huge explosion of trying to be more scientific about sports and I could do this with horses I thought. And then fast forward after 20 years of starving and doing nothing and finally it turned into a juggernaut. It’s become great. 39 great one-winners we bought in the last five or six years. Four Eclipse Awards and world champions that we bought for not a lot of money and all. And just an incredible track record and last year we bought young yearling from some guy, gave us $900,000 and we turned it into $4.5 million and sell them off as two year olds. Talk about affecting an industry and the impact of us. Although we do work with six of the top ten stables in the United States, most people don’t know who we are or when to use us or there’s cheap imitations of what we did. What we’ve taken 35 years and millions of dollars and there’s people out there kinda winging it but a lot of people can tell the difference between us and them. Except that our track record … And the crowning piece of the programme was American Pharaoh. After 37 years he won the Triple Crown and so although I did … The guy that owns him, kinda somehow I got in a fight with him right before he won the Triple Crown. I was banned from his box at the racetrack during that event. So that that kinda put a damper on the whole event. But we had bought him the dame for that as an incredible physical specimen and she only ran a couple of races and he made her a brood mare but she was something really special. And then the sire was part of a breeding programme and he was gonna sell it and we did all the testing that we do. And he didn’t sell it and we thought we were a real part of it. And then when American Pharaoh was young he was put in a yearling auction and we were supposed to evaluate him and once again we said, the New York Times the day before the Belmont Stakes, that Friday before he won the Triple Crown the quote of the day, they have a quote of the day every day in the New York Times and that day it was me. And the quote was, “Sell your house, don’t sell this horse,” which was from that yearling auction. AW: I saw that. JS: And the rest was history. So that was nice. AW: And just a quickie on that a lot of people listening to this will be from the States, will know what the Triple Crown is. But the Triple Crown is essentially the three premiere race, horse races in the States. The Kentucky Derby, Belmont Stakes and the Preakness Stakes? JS: The Belmont Stakes, the Preakness and the Belmont Stakes. AW: Preakness Stakes yeah. JS: And they’re different distances and different services so it’s extremely hard to do it and what happens these days is fresh horses will meet you at each race, that didn’t go in the other races. You’re not required to go in all three. And they’re bunched fairly close together, so even if you’re a super horse, you’re gonna be tired. You’re gonna meet the best that there is in the world going for million dollar purses in each of these races and they’re fresh and you’re not. So it’s really quite and extraordinary achievement these days. And it didn’t happen for 37 years. Secretariat, people may know that name, was one of the premiere triple crown winners. One of the last ones before it stopped. The whole thing stopped. And of course your grade one races. There three year olds too. Speaker 1: But let me just tell you about very briefly, if you wouldn’t mind Jeff, without giving away trade secrets, clearly here. But you told the owner to sell his house instead of selling this horse. What was it about that horse that convinced you? What had you spotted? Jeff Cedar: Before I do that, let me just tell you one of the things I learned from my experience with the Olympic Sports Medicine Committee and working with American Olympic teams. By the way we made a big difference with a number of teams, was that the medical data that existed, the data that existed to understand medical, engineering, physiological … to understand these things was on normal and on sick or injured. They didn’t know about elite athletes. And it turned out that the elite athletes the data we needed was as different from normal as the injured and the sick was from normal. And so we didn’t know, they really didn’t know. And when I created the first accurate heart rate metre that was the size of a pack of cigarettes and you could carry around. And while I was doing that I went to the world’s leading race horse cardiologists and they told me the heart rate would be about 120 beats per minute going up the racetrack because their heart’s so big and this and that and the other. And then the metre kept telling me it was like 220. And I said, “What’s wrong with this?” And finally decided they’re nothing wrong with my … They don’t know what they’re talking about. And it happened again and again and again. And I realised I had to get the data. Not only that but the equipment to get the data didn’t exist. I had to get designed and manufactured for me specifically and this was back 20 something years ago, a chip. Personal computer didn’t exist. I had to have the chip manufactured so I could get that accurate heart rate. Human metres weren’t accurate for a lot of reasons. And so I did that again and again. I saw that a guy named Steele in Australia was doing the measuring the size of the heart, trying to do it from an EKG. But the little machine that he would measure the distance between the peaks on the EKG on the little tape that came out of the cardio machine but the tapes … They were doing it in barns. I’m gonna answer your question. They were doing it in barns and the voltage would vary so the little motor running the tape would go faster or slower and then they were measuring the distance. It was crap data. And he still had some relationships but I said, “This is ridiculous. I need to do it … ” I found out you could take ultrasound and measure the pieces of the heart but you couldn’t do it in a stall because it wasn’t portable. Speaker 1: But just to tell everybody this was a homemade device wasn’t it? This was something you created. Jeff Cedar: So it was 20 something years ago or more. So we went to … We bought an Apple 2C, the first thing and then we went to military contractors outside of Washington D.C. and we got military hardware crap and then we programmed in machine language. Nobody now, it’s like 15 languages later. We programmed it in machine language so it would be fast enough because you have like 10 million things a second in ultrasound. And we build a machine that could go in the stall and you could wheel it in. And we started doing racehorses. And we found out that the transducers were wrong so we had transducer in different frequencies manufactured for us. By the way, that machine is now commercially available. We should have patent the goddamn thing. And then we got so that we could do reproducibly and to see what we could do. And I’m gonna get back to the analytics and the big data and everything. I’ll get back to that. But to fast forward about American Pharaoh. American Pharaoh fit everything we knew. After 35 years of doing that stuff. Millions of dollars. 20,000 horses, follow every split of every race and do all these work ups on them and everything and now we’re into DNA. But anyway the most outstanding thing about it. The other thing we found out about it was the great athletes. They were different and they didn’t have holes in them. So in the great race horses, you didn’t have to have the very best gate or the biggest heart or the most wonderful something. And a lot of people thought they did. They would fall in love with one thing they found and then they would find a horse or a person, an athlete who would have that one thing and then it didn’t work out and they didn’t know why. That doesn’t work. You don’t need the best everything. You just need a good everything and that makes you very rare. So it’s like if you had 20 links hold the Queen Mary and they were titanium and you had one that was made out of paper make. It will break and the ship will float away. Well it’s the same thing with these athletes. So what we’re really looking for when we find all these great one winners when they’re yearlings is, when there’s no hole. Not only was American Pharaoh had no hole. Now he wasn’t the greatest in everything. But some of the variables he had were just off the chart. One of them was his heart. In fact, I know one of the guys that used to work for us and he left and he competes with us, turned him down because he thought his heart was too big. But we were pretty sure that wasn’t the case because everything else fit. Everything fit. And yes, it was extraordinary but that’s what we’re looking for, extraordinary. So why would we get rid of the anomalous. When we see a big anomaly wy would we throw it out? It’s like with a symphony. You can jumble notes together an infinite number of ways and you just get noise. But there’s also a whole lotta ways you can do it that are symphonies and they’re different but it all fits together and that’s what he was. And he had this huge, thick enormous heart pumping a lot of blood. And the quality of the muscle and this and that. The whole things was just extraordinary. And on top of that we couldn’t find anything else, no matter what we … traditional, non-traditional we couldn’t find a hole in this horse. So we said if this horse can’t … Unless something happens to him he’s really gonna be memorable. And he was. Speaker 1: Yeah, amazing story. I just want to move you on to the challenge that you put into the given the traditional, and the word traditional is what chimes with us in legal services, because we are struggling under the weight of tradition and peer review and all that nonsense. And actually data shining the light on performers that we’ve never done before. So you’re challenging or have challenged the traditional view that bloodlines and pedigree are what matter. Jeff Cedar: Absolutely. Speaker 1: That’s what drives the value. That’s what drives the price. That’s what gets sexy headlines. But actually, what I found astonishing when I was reading some of the background research on you guys was the stats on actually how many of those horses win is incredibly low. I mean what are they … one or two percent are gonna win. Jeff Cedar: That’s what I realised. I realised that 99.9% of the people in horse racing were losing money and they would show me the very best pedigree from the very best veterinarians, the very best owners, the very best handlers, all the smartest most experienced people and if they had 10% major horses they thought that was fantastic. And I was coming out of the Olympics, where if we had 90% failure rate we would think we were pretty awful. Right? Speaker 1: Absolutely. Jeff Cedar: They have no idea what they’re doing. Well, that’s not true. What they have is good but it’s not enough. So I said, “I could make a living here. I can make a contribution here.” And the first 20 years were really rough and the reason was not only did we not have the data but we didn’t have the equipment. We ended up making the equipment to get the data. And along the way we invented some stuff. We invented a bone scanner, noninvasive ultrasound bone scanner and we spun it off to Johnson and Johnson because they could diagnose osteopetrosis with it and it won the New Medical Device of the Year in Europe in 1986. And we still get royalties. That was from a little farmhouse in a corn field in Pennsylvania with a bunch of nuts from Harvard that were not doing what their dads told them to do because they liked horses. So, anyway but we accumulated data and we had failure after failure after failure and then I met this lady named Patrice Miller. She was thrown out of a couple of prep schools and a couple of the best colleges in the United States and she was one of the first women jockeys in the United States. The second one, I think. She lied about her age and rode at 15 in a country racetrack. She had done it all. And she got interested in what we were doing and joined us and she’s a genius at what she does and now she’s our partner in it all. And she, at that point, had worked for some of the top trainers in the United States like Rob Whitely. Hall of Famers. So she brought the traditional expertise and the curiosity to improve it and that was our turning point. Because before that we were doing technical things well and basic things badly. And she made the difference and there was a lot of conflict. I remember one time when she was telling me … I was looking at how they were wrapping a leg and she said, “Well, that’s how Frank Whitely does it.” And I said, “Well, does Frank Whitely wear a hat?” She was gonna kill me. I said I wanted to make my own traditions. But anyway that was wrong I needed to pay attention to what Frank Whitely did and build on it and when we started doing that the whole thing started to open. Plus, by that point I had 20 years of data. We had spent millions of dollars. I have worked other jobs I had been successful in major businesses that I didn’t give a shit about because all I wanted was the money to do my horse research. And so that’s where the money came from. And I also brought technology from my businesses. The slow motion photography, I brought that from textiles where you had to have a machine that could watch the loom, the needles all going like mad and it could immediately slow it down and look at it and figure out what to fix. I took that over and I did it on the horses breezing on the racetrack. And I found out that again, like I did in the Olympics that the great horses ran differently than the average horse. And you couldn’t do it with a regular camera. In those day, these were cameras we didn’t have video. It was like $200,000 for shitty video these were special camera, and again military that went 500 pictures a second with film without breaking it. And then special projectors on special computer platforms. It was incredible but we found out there were major things that we could identify that were different in the way that these really good horses ran. And we added that to our stuff. And so we do gate analysis now. And well people say, “You don’t need a camera that goes that fast.” Well then they don’t know what to look for because they haven’t got the data. And the same thing with the heart. I see guys out there and they’re looking at the injection fraction of the heart and I know, because I got mountains of data, that’s meaning … well it has to be normal in the range. But other than that it’s not gonna tell you much. There’s all kinds of crap being sold out there. They have 100 ponies in a lab or they have 170 horses out a some guy’s barn. Something ridiculous. Well we have 50,000 horses over 15 years worked up and followed. And we published a lot of it. We published mountains of data. Because nobody would pay any attention to us and they didn’t believe it and they couldn’t tell the difference between what we were doing and the bullshitters. So we published it. And at the time, I thought, “Well, I’m giving it away but it’s the only way it’ll legitimise it and I’ll be the first one. So I’ll get my share of the market.” As it turned out, although it was read by the scholar, veterinary scholars I got it into the most prestigious scientific journals where it had to be refereed by those guys who were completely prejudiced against me because I didn’t have a veterinarian’s degree. But they realised … I had to jump through so many hoops but it was real. We published all this data and that was a fraction of what we have. But people still don’t … They don’t read it. They don’t use it. They don’t understand. In the hard stuff for example, it turned out we were measuring the hearts and we couldn’t get anything. It really wasn’t any good until we got enough data. Until we had 12,000 horses over three or four years and every split of every race through the end of the three role year kind of thing. And at that point we realised that if we separated the data from when we took the reading … So we only compare horses that are the same age very tightly within a month. Chronological age and the same sex. And the same height and the same weight. We had to have a database big enough so that if it was a 900 lb. 13 month old Philly that was 15 hands that we had enough horses that were graded stakes horses in there that weren’t just on drugs or by accident in one race or something. In order to have hundreds of horses to compare to everything you looked at, you had to have a database of 20,000. So when we got there and we did that statistically on the computers. And then still we didn’t have personal computers. In those days I would run up to a hospital in New York and they had a huge IBM 360 and I knew a doctor there and he would let me put these huge discs on it from midnight until 7 AM. All the stuff. And we found, “Oh my god. Look at this.” Now it’s obvious what we’re looking for. So when people go out and wing it. The other thing is it’s not easy … When we first started measuring hearts for example. And again we went to the experts and they showed us how you do it and the protocol of equine cardiology. And it didn’t work. It wasn’t reproducible. You couldn’t go in a stall and do that with a yearling running around and get the same result twice. And we said, “Well, what’s wrong with this picture?” So we worked and worked and we ended up with a different protocol. We went around the other side of the horse. We changed the frequency of the transducer and we measured a different angle at a different part and we got so that it was reproducible. You could do it tomorrow, next week, next month or whatever. And you couldn’t do it with another technician really. You needed a really trained technician. It was not easy. You could people the data, the instruments, you could give them the violin and the instruction manual but they weren’t going to play a symphony. They needed the experience. Anyway it got so that we could do it reproducibly. And we proved that with a huge study. And then we win after it. And the data just become obvious when we win. Some of these horses are not gonna make it. This is Moneyball in its pure … It’s analytics, it’s big data, it’s biometrics. I didn’t do it by myself. I went and found the leading people in each of the different fields in our country and I worked with them to design our studies and to interpret our studies. So that they would be sound. But every time that I started they all thought they knew the answers and it turned out they didn’t. It’s really different. I don’t know I get so excited and go on and on. Speaker 1: Well it’s brilliant. Jeff Cedar: I know what I was gonna tell you. So one of the horses in sales was offered $2.4 million. It was an unraised two year old Breezing and it was a big good luck and it had a good pedigree and it was a beautiful gate and it was fast and everything else. And it passed the vet and x-rays and everything. And it had a heart that was in the bottom quartile, maybe the bottom 10% of the breed. Small spleen and I thought forget it. Forget it, forget it, forget it. It goes for $2.4 million because they’re two geniuses they don’t need this new shit. Right, they’re not gonna spend $300 to look at a heart. And then first race was a sprint it didn’t do so hot. So they were gonna run it ina second race it was running long in a major racetrack. And I was on the computer looking at it. And it was going off on one to five. Such a heavy favourite. And I said, “He’s gonna get to the head of the stretch and he’s gonna just die. Right cause he can’t … it’s never … That horse is not going over a mile in good company. Forget it.” So I pile on every other horse in the race, right and sure enough he gets to the end of the stretch and he gives it up the second. And I thought, “Well, there it is.” People may not understand with all the rest of it but they understand that, right. You think they get it. Speaker 1: I’ll tell you what I was also thinking when I was looking at this. Jeff Cedar: The people that paid the $2.4 million are some of the most famous accomplished people in horseracing and I won’t name them [inaudible 00:25:09] Speaker 1: And one of these things I was thinking, Jeff, was the data that you got that’s unchallengeable. All these imitators can do what they want to do with it but they haven’t got that. But they also haven’t got you and they haven’t got that instinct. For example, knocking knees and the knocking feet analysis. This morse may look good now but how’s it going to look after 20 races? Jeff Cedar: Forget 20 two or three the guys are knocking their feet. In the gate when we breeze them some of them bang their front hoof into their back hooves and they do it every stride and then they don’t want to go in the starting gate or they don’t like trainer and their feet are … And they don’t know why. And there’s like ten things like that. Some of them when they throw their leg out, their foreleg out at the end of the leg it’s a five pound hoof at the end of a rope. At the end of every time they throw that leg out they snap their ankle and then low and behold they don’t make racing or they have a short career because they crack their specimoids. And I wouldn’t have bought that horse because I bothered to sit there with a camera that costs $20,000. I sit there and they go by and I have screen in front of me and until the next horse goes by I see the horse going by in slow motion and I see them banging their hooves together and I see them snapping their ankles. I see them going so far back in their knees that the whole leg in the shape of a banana. So you have a thousand pound horse going 40 mph and he goes so far back at the knee it’s like up straight forward and your whole leg bends like a banana with 8 tonnes of force on it. And I know that because I had force plates in racetracks that I ran horses that I know how much force there is and the angles of it. If I started telling you all the things that I measured … I measured the weight of the manure that was coming out of horses on the way to the paddock. We cut of legs of every horse that broke down in some of the racetracks and tested them in engineering machinery to find out … We looked at so many things and just piled at that data. And nine out of ten of them were useless. One of them became the bone scanner and we sold it to Johnson and Johnson. I told you about. A lot of it was interesting but it didn’t do anything. That tenth out of ten would be gold. And some of the stuff I don’t talk about it really, really stupidly simple but it’s powerful and lately we’re doing the DNA. And there’s guys out there with the DNA … The big deal is the called it the speed gene. And the main thing they have is a Mycostatin inhibitor marker. And the Mycostatin inhibitor is related to precocious muscle development and there’s diseases associated that makes a whippet look like a bulldog. A baby look like weightlifter. And so the idea was that then when they would be precocious and they would put on muscle faster and be stronger earlier and win earlier races and blah, blah, blah. And they found a relation between that and the big really good racehorses and I thought, “Well, I don’t know if that’s so valuable because I can look at a yearling or I can look at a 2-year and I can tell you whether it’s muscled up without having to get DNA out of it.” And they are different. And secondly I wasn’t a three and four year old anyway so I have time to train it. It doesn’t have to be muscled up as a 2-year old to be a valuable racehorse. And thirdly, the idea of a speed gene is the idea of a health gene. Health is very complex, it’s made of many, many things. The performance of a racehorse is extremely complex phenomena. It’s not anchored to one thing let alone precocious muscle development. Instead of looking for a health gene the look for a disease gene. One related to a specific disease. So what I look for is markers related to specific things that I know relate to the performance of the horse. I go over all my data. And I got all these horses to do. So I started going back and I found all these horse and I said, “Okay, I know these horse have these biometric traits and I want to see if I can find markers for it.” And so I found several markers and they have nothing to do with what the big company in horse research DNA is doing. Nothing to do with that. And I’m not advertising and I’m not selling it right now. But it’s completely different but it’s a very, very specific thing. It’s pretty good. Speaker 1: Well, that’s fascinating- Jeff Cedar: But what is it based on? Data. You have to be willing to spend the time and the money … you have to be imaginative. You have to get the right people so you’re asking the right questions looking for the right kinds of data. And then you have to realise that you’re probably you’re gonna find something you had no idea was important. You have to be open. Speaker 1: In a strange way it’s not rocket science is it. If you collect that amount of data you can have a massive impact on an industry. So tell me this, you mentioned in your discussions with the vet industry. The prejudices against you. Prejudices against you I should say. How is been more generally in the horseracing environment. What are they thinking of what you’ve done and how you’ve changed the way that you spot- Jeff Cedar: And now the sale companies in the United States all provide these video of the breezes. Although they don’t give you a video that’s good enough quality to do what I do. Speaker 1: And Jeff, just a second breezing is what? Jeff Cedar: They work out the horse at a racing speed with a jockey on his back in front of the crowd. A few days before the auction. So you can look at that and they provide a regular video of that so you can look at it and slow it down. But the video is such that if you really try to slow it down it doesn’t take enough pictures per second so when you stop it the legs are all fuzzy. So the things you want to look at you can’t. But you can old do major pattern recognition from speed of the horse. So of course what do they do. They all go by how fast the horse went. And a lot of horses go fast in ways that are dangerous or that are use too much energy so they will work out at a phenomenally fast eight of a mile but the way they did it makes sure that they can’t go a mile. So I wouldn’t want them. But they sell for the most money because they went the fastest workout time. [crosstalk 00:31:14] But I interrupted you. Speaker 1: No, not at all. I was interested in disruption. They talk about- Jeff Cedar: Oh, the disruption. What do the vets think? I think they’re still sceptical. I don’t know why. We’ve published and published and published and we got our results. You can verify results. Everybody lies so they show you their big horse or whatever. We’re doing it every year. A couple of years ago we had … They’re 30,000 foals born a year in the United States. 20 of them could qualify for the Kentucky Derby, which is the hold grail. So that’s 20 out of 30,000. Five, 20% of the entire field was horses that I had picked and bought for people. Now there’s a lot of bullshitters out there who say, “We picked that horse.” But they pick a hundred horses at the auction and then one of them does well they picked it. I’m talking pick it and buy it, right. Not out of a hundred where an idiot could go and if I’m allowed to pick a hundred I could get a good horse, right? Out of a major sales. But anyway, so we had five at the Kentucky Derby at low odds, right? And then we had two or three a year every year. And nobody noticed so I published an ad that said, “Statistics 101, my father said three flukes is a trend and I listed whether the horse would qualify for the Kentucky Derby as three-year olds. Like five years in a row. It had zero impact on our business. And it’s just impenetrable. So I’ve decided, it used to make me crazy, now I think, “Thank you god. I can buy the horses I want off them because the others aren’t doing it.” There’s a couple of other guys who are out there that are doing it and copying what I do and most of them without the data. But sometimes they fall on the same horse we fall on. The other thing is we can’t get the horse we want because if it has everything that traditional people want and it has the other stuff than I’m gonna be competing against Michael Tabor when I’m bidding and I can’t afford to. Not because he knows what I know but because it has all the traditional stuff. He doesn’t know it has the other stuff too. So, that’s a problem but those guys are never gonna hire us. And then there’s bullshitters out there who claim they do the same thing we do because they use big words. I can’t believe people hire them but they do. Speaker 1: Well, it’s a brilliant story. Jeff, I’m gonna wrap it up there. There’s a lovely saying, I’m not actually sure if it was from you or your colleague Patrice but, “It’s not how fast the horse goes, it’s how the horse goes fast.” Is that yours or Patrice? Yeah, it’s a brilliant, brilliant- Jeff Cedar: Yeah, that’s me. We don’t look at how fast they go. We look at how they go fast. Speaker 1: Well on that fantastic note. We’re gonna put some links at the bottom of this podcast for people who actually want to find out more about the Jeff Cedar EQB. Jeff thank you for joining me and thank you so much for all the amazing information and background about the disruption you’re bringing to horseracing. Jeff Cedar: Well, thank you and I hope I can get to be more disruptive. Speaker 1: Well good luck to you. Thanks again Jeff. The post In Discussion With …. Jeff Seder, Architect of Moneyball for Horse Racing appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition Legal AI, What to Expect in 2018 and the Reasons Why We Do, What We Do Richard Tromans is founder of Tromans Consulting, advising lawyers on strategy and innovation, including the adoption of legal AI and other ’New Wave’ technology. He’s spent over 18 years working in the legal sector, focused on the UK and global legal markets. Richard is also founder of the global legal AI and new technology site, Artificial Lawyer, recently recognized as a top 50 information site in the world on Artificial Intelligence. I ask Richard to look into the future and more specifically, what to expect in the year ahead, but I started by asking him to embellish a topic he raised in his excellent blog www.artificiallawyer.com and that’s, alongside getting excited about the who and the what in legal tech, do we spend enough time considering the why? Alongside his blog, you can contact Richard here: www.tromansconsulting.com Transcript of the podcast; Andrew Weaver:  Richard Tromans, welcome. It’s Monday, the 29th of January. I’m referring to that day because I’m going to refer directly to an article on your blog today. Richard Tromans: Hello. How are you doing? AW: I’m very well. Thank you again for joining us. As I’ve mentioned in the intro, we did try this once before, but the gremlins affected the sound quality, so thank you very much for joining us. I know you’re absolutely flat out, for reasons we might go into briefly later on. Whilst we were going to have a very headline conversation, Richard, about what we can expect in 2018 within the world of legal AI and automation, et cetera, I’m actually going to pick up on an article that I saw on your blog today, which was related to a question not often asked in the sexy world of legal tech and that’s why? Why are people doing all this stuff? We know the who. We know the what. We get a feeling of where it’s all going, but flesh out the article that you wrote today, Richard, about that particular issue. RT: In a nutshell, why does legal tech automation, artificial intelligence, and so forth, why does it matter? Why are we so concerned about it? I guess different people have different reasons. Some people are perhaps worried about their jobs. Some people are worried about the pocketability of their law firm. Others are worried about the incessant headlines about AI and having to deal with reality and trying to make even incremental change inside a law firm or an in-house legal team. Those are all totally valid. I think there’s a bigger why, a bigger question. That is, what is the end goal in all of this? To me, the end goal is justice, because that is what the legal market or legal sector, lawyers, courts, et cetera, all exist for. People want justice. People want justice personally. They want justice for all the businesses that they own and invest in or work for or are employed by. This is the fundamental issue. People who are outside of the legal world do not think in terms of law and lawyers and courts. They think about things in terms of fairness, what is right and wrong. They say, “I have been cheated. I want justice,” or, “I believe that I’m owed something,” or “I have an inalienable right and I’m not receiving that right.” They want justice. We’ve been working with lawyers for many centuries, millennia, and that’s been good up to a point. Now there’s this new wave of technology and that’s tremendously exciting, but fundamentally, none of it really matters unless it is delivering justice on a societal level, because that’s what we’re all here for. Isn’t it? If we’re excited about legal technology because it makes a couple of people rich or it shaves off 1% from the bills of in-house legal departments but are spending hundreds of millions of dollars, I don’t see the point. We might as well pack up and go home. That’s just not a good enough reason to get out of bed in the morning. There has to be a bigger reason. There has to be a better why. To me, that better why is access to justice. I don’t just mean in terms of refugees seeking asylum. I mean right across the piece from the poorest person on earth to the richest corporations on the planet. There are access to justice issues all the way up. For example, imagine if you’re a shareholder, imagine if it’s just simply you have an insurance policy or a pension, some of that money is going into corporates who are then not really doing very much to manage how their internal lawyers are spending that money. I said it on record many times and to other people in conversation, I do believe that the in-house legal teams, excluding the new Legal Operations executives who are doing fantastic work, like [CLOC 00:05:25]. The traditional internal legal departments, I believe, are doing a terrible job of spending money on external legal services. They have the buying power and they have access to all kinds of things internally. They are not really putting pressure in the right way. They’re not analysing their law firms. They’re not analysing the illegal spend. They’re just not doing what they could do. There’s a whole raft of tools which are coming out. I can understand why they can’t keep up, most of … even legal technologists can’t keep up with daily announcements. I mean, today, there’s been two announcements of two new applications. Tomorrow there’ll be two more. How do you keep up? Totally understand, but something needs to change and these … it all comes together. It all comes together, because it offers a chance to improve the legal system for the good of all. AW: Well, I’m going to pick up on two things then, Richard. One is very close to my heart and it’s one of the reasons I got involved in legal tech, but there’s a statement within your blog today about approximately 70% of SMEs, and I know this to be true through research I’ve done separately, don’t use lawyers. They’ll sweep things under the carpet. They will do everything they can to avoid using a lawyer. Lots of reasons, cost being an obvious one, but also fear of where cases go. It’s such a kind of time warp that people go in. There’s so much asymmetric information in using a lawyer and using legal services where you just don’t know where it’s going to go. Fear and cost and all of those kind of things put people off. I’m slightly concerned at the moment about whether any of these people on the ground level are actually getting the impact; getting any real impact on all this legal tech stuff. Is the trickle down happening? If you think it is, where? And if you think it isn’t, how can we accelerate getting to the people who perhaps need it the most? RT: I think it’s patchy, but I do see room for some hope. I mean there are some great things going on in the US for example. There’s a lot of effort being made there to support legal bots that will provide some, at least, basic support for people who would qualify for Legal Aid. There’s some great work going on there. You got people at Joshua [Browder 00:07:47]. I mean, he, if anything, he has really made people aware of what you can do. You’re not solving complete legal problems, but solving elements of legal problems and saying, “Come on guys. You do not have to be so inefficient. There are ways through this.” The simple answer is, is that the average man or woman on the street, the legal world remains incredibly complicated. Understandably, the legal tech companies like AI companies that are coming onto the market now, they have to make a living. They have to go where they’re going to get money; where they’ve got a compelling business case, so they focus on things like M and A due diligence, which is not much interest to the average person. That’s where, initially, they will get some income. That’s where they’ll get started. If you were in an expert system company, again, where is the money to be generated? In the commercial legal space. It’s probably true to say it’s not just in the commercial legal space of lawyers, but that in itself is split, because you’ve got the commercial legal space of lawyers in private practise and then you’ve got the commercial lawyers who are working inside corporates. Most of the AI companies that I’ve seen at any rate, are working primarily in conjunction with law firms, they’re not working with the corporates. Most of them do some work with the corporates, but then that raises another question is, why? Because most of the AI companies that I talk to, if you said, “Hey, would you like to go work with BP or Barclays or Morgan Stanley,” they’d say yeah, they’d bite your arm off, but they’re not all being asked. Why is that? Probably because the top table has got other things to worry about and the in-house legal team doesn’t want to rock the boat; likes things as they are. AW: Likes things to remain inefficient, as you’ve highlighted before. RT: They don’t wake up in the morning and rub their hands with glee and say, “Yippee! How can I make this more wasteful, more time consuming, more painful for the shareholders of this company that employs me?” Obviously, no one does that. You’d have to be a maniac to think that, but unconsciously and implicitly, that’s what we’re allowing to happen. It’s like an aristocracy. The aristocracy didn’t wake up in the morning and go, “How can I terrorise the peasants this morning? How can I exploit the current socioeconomic status quo for my own benefit?” It just was as it was. The sun came up in the morning, they were sitting in their palaces, out there were the peasants, and they were pretty happy with the way things were looking. Of course, then people got wise to it and chopped their heads off. You know. I think that it’s a slow, slow revolution. That may be an oxymoron, I’m not sure if you can have a slow revolution. Maybe a slow revolution is an evolution. Things are changing and things have to change, because fundamentally, why is anyone doing any of this? Even something as what now is actually relatively mainstream, using natural image processing to conduct an M&A due diligence exercise, because you have a ton of documents. The amount of documents grows every year relatively. It makes sense to use this. Why does it make sense to use this? It only makes sense to use this because open-ended, high cost hourly rates do to that work don’t make any sense. Again, but why? Because it all sounds kind of natural when we’re talking about it and the average person on the street will say, yeah, of course, it’s obvious, isn’t it? Until very recently, until 18 months ago, it wasn’t an issue really. It was just something the general council kind of go, “Hmm, I wish there was a better way of doing this, but there isn’t, so it doesn’t matter. Let’s carry on.” I think this is the fundamental thing. I think we’re only, only really just starting to grasp it really; these bigger issues. What is it all about? Why are we doing this? Also, we get distracted, and also rightly distracted. There’s a tonne of people who work inside law firms, and there’s a few who are very vocal and, quite rightly so, who get incredibly angry and annoyed about all the talk about all these AI systems and all these whatever it is, because they just look at their law firm and they think, Jesus, can we just get on with this? Can we actually just make some real changes internally? Can we improve the processes in a really substantive way? You know, some of them are making great inroads internally. That draws the debate in that direction and then you’ll talk to some senior equity partners and they’ll be just like, “Well, I’m getting out of this. I’m going to be retired soon. I don’t particularly want to pay much attention.” You have this mass of different issues. Then you’ve got the whole ethics and AI for good crew, who, again, all totally valid, but it’s all just drawing us away from the central issues, which is why on earth are we doing all of this? What is the societal benefit? AW: I completely agree. I think we all get a bit lost in that when we’re thinking of all these new wonderful products to be putting on the market. My particular passion is increased transparency. Getting those people on the lower level of the consumer line, I guess, the SMEs, the individual purchasers of products, and allowing them to get the benefits of some of this technology that’s coming down. Then, as you say, of course, the AI companies are going to want to work with the people that pay the bills, but hopefully, there’s an acceleration towards the man or the woman on the street or the smaller business. Richard, I’m very aware that you are engaging with the legal tech event in New York, so I don’t want to take up too much more of your time, but can I just move it on a little bit then to this more generally in 2018. We’ve bedded down the new year. We’ve all got ourselves over the Australian flu, or I have, anyway. What do you think is going to happen this year, talking of the next stages of the evolution of AI and automation? RT: Yeah, well I think it’s multiple things. One, we’re going to see more collaborations as we’ve seen today, for example Kira and NetDocuments. NetDocuments is a DMS. Kira is a AI doc review system working together. IBM Watson working together with Thomson Reuters to create Q and A tool for data privacy. A kind of expert system, research system using machine learning, but a combination. I think that is the way to go. I don’t think any single individual tech company has got all the answers. I think we’re going to have to see a lot more cooperation and coordination and different types of AI systems, different types of process automation system put together. I think another thing, as well, and you have to agree with them that there has to be a greater focus on process, on awareness of legal data, of understanding how the bits connect and actually trying to get real outcomes. I think there is a genuine risk that people do a pilot with a AI system and then it just sits there. I get the same experience, I’ve noted the same experience as a strategy consultant, which is my day job, as you know, at Tromansconsulting.com where I’m advising law firms on a variety of issues. In the past, not while working at Tromans Consulting, obviously, but in the past at previous consultancies I’ve worked at, there were cases where a strategy document would be drawn up, everyone would vote it in, and almost nothing would be done, because the reality between buying in … some consultancy provides you with a beautiful strategy document, and actually executing it in a way that changes your behaviour as an organisation, are two radically different things. That really is to some degree where we are now with legal AI technology. Now, I’m not saying every firm is like that. I don’t believe that for a minute. I’ve met many firms who are doing fantastic work, who are using AI systems in multiple practise areas; they’re using expert systems, they’re using different types of document automation. They’ve got internal process people, innovation people, who are doing all kinds of great work. Joining it all up, bringing in the lawyers, even senior partner level, getting everyone integrated into this and driving it forward. There are some great examples like that. There’s also a whole bunch of firms who have just done pilots and there’s no real innovation going on inside. The business change team internally, they’re probably tinkering with a bit of document automation and it’s kind of not really having a big impact. That needs to be addressed. I really hope that 2018 we’ll see that change. Hope that the good news stories, the good examples will act as a benchmark for everyone else to follow. AW: Just a quick last question, actually because I saw it on your Twitter feed earlier. The legal tech incubators that have emerged over the last year or two. Are you seeing some great potential coming out of those? RT: Yeah, yeah. I think initially, I, and everybody else we were a little bit, and like the firms who actually hosted them, were a little bit unsure what would happen, but I think the proof is in the pudding. I’ve done two extensive interview pieces; one with Allen & Overy and one with Mishcon de Reya who did two very successful incubators. It’s been tremendous. It really has been tremendous. It’s a win-win, because the startups get to learn what the lawyers really want and what their clients wants, and the lawyers really start to think more laterally and pragmatically about what is there. They stop, I think, hopefully, stop seeing tech as like this thing that you buy, that is delivered to you in a box that you plug in, maybe it’s useful, maybe it’s not and seeing it as just something that you can integrate into whatever you’re working. It becomes more organic. I think it has been tremendously good. It’s interesting to see that Mishcon has now brought a couple of clients into the mix who will both be sponsoring part of the incubator and also getting involved with a bit of mentoring, which is tremendously good for everyone. Again, because then, the lawyers, the client, and the tech companies all start to see that their interests are aligned and can work more efficiently together. I think that’s a great move, because I think, going back to my previous point, the greatest risk to all of this, this new wave of legal techs, I call it, the greatest risk is that people just see it as a bunch of stuff in a catalogue that they buy. They buy it in, you know, you probably, you remember the … [inaudible 00:18:52] hold you up, but in the ’80s you used to get these catalogues pushed through the door full of fitness machines and stuff that could make waffles or bread or all this kind of stuff. These really cool things. You know, sometimes you buy them … I guess it’s been replaced by The Shopping Channel now on TV. You buy these things and you use them once. They’re kind of okay, but you couldn’t really see the case for using them on a regular basis. Then it would go back into the kitchen cupboard and it will stay there forever and then eventually it will get thrown out and taken to a charity shop. I think that is the biggest risk. That is very, very, very biggest risk. Certainly, some of the AI companies I’ve got to know, they got a fantastic track record. Nearly every, if not all of their clients are using it in a regular way. There are other that are not, I think that is the thing; real use. Real use is only going to happen when all the different parts of the firm and the clients are aligned. You can have an innovation team that’s brilliant, but if the lawyers aren’t buying it, it makes not difference. Vice versa, you can have a bunch of lawyers who are really are up for it, but if its support mechanism, its IT people, its innovation people, its process management people have no real desire to help, it’s going to die. AW: Yeah, well that’s why the most interesting feedback there, certainly with the Mishcon’s one is the engagement with all … quite a wide variety of stakeholders, not least the lawyers themselves. That’s really pleasing to hear isn’t it? RT: Absolutely, because at the end of the day, I mean, again, it goes back to my very, very original point, justice. How do we get justice? We get justice by engaging with lawyers. How do lawyers succeed? Lawyers succeed by engaging with everything at their means possible to delivery that service to clients. If the lawyers disengage from the potential benefits of technology, then, nothing’s really ever going to change. It’s just going to be noise and headlines and a few pilots here and there and some very frustrated innovation people jumping up and down at the back of the law firm. We- AW: Those innovation people have been jumping up and down frustratedly for many years I suspect, but perhaps there’s a sea change going on in terms of engagement. RT: I think there is. I think there is. It’s like all parts of the legal market, it’s highly unconsolidated and it’s highly nonuniform. You can walk down a major street in the city full of different law firms and the experience of legal technology in each of those different law firms, it would be radically different. Radically, radically different and the attitudes and the types of technology they’re using will all be very, very, very, very, very different. They’ll all be using document management systems, billing systems, all of the sort of day-to-day mundane stuff, which doesn’t really have any strategic impact. When we look at the more interesting stuff, the technology that actually performs work, AI systems, automation and so forth, then we are in a different scenario. I think perhaps the simplest way of putting it is despite all the headlines, despite us actually having ridden at the height curve, and actually now coming down the other side of it already, in terms of AI and technology, it’s very, very early days. It really is. I mean, for me, I always use the analogy of the personal computer. For me it’s like 1977, 1978 max. I think that’s as far as we got. We’re at the personal computer stage of when people were still screwing circuits onto blocks of wood. AW: Yeah. Richard, you keep referring to these decades that I’m sure the millennials have no relationship with at all, which I think is giving away your age and mine, because I remember the ’70s and ’80s in very much the same way. Richard, I’m going to thank you very much indeed. I’m conscious of the time we’ve taken up from your day. I know you’re engaging with New York and the legal tech event over there. Artificial Lawyer blog is the blog, frankly, that I go to first for any information and news about this kind of stuff. The amount of content you put onto that site is phenomenal, Richard. I don’t know where you find the time, to be honest. It’s kind of relentless. RT: I usually get up at about 5:30. AW: Do you? RT: That’s one way of doing it. AW: Well, it’s admirable. It’s an impressive blog. Anybody who wants any more information, sign up to Artificial Lawyer blog. I’m also slightly mesmerised by the video on your Twitter feed, which is showing me a combination of beef and lettuce, Richard. I would prefer to have had a visual on the avocado on toast. RT: Yeah, I will try rectify that and get some avocado on toast images up as well. AW: Well, if you want to know what that’s all about, it’s about collaboration and how different things go together, and two plus two equals five. Richard, thank you so much for your time. RT: My pleasure. Take care.   The post In Discussion With …. Richard Tromans, The Artificial Lawyer appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition Premonition podcast talks to Dickie Whitaker, an expert on risk and innovation within the (re)insurance market. Dickie Whitaker has 30 year’s experience in the Re(In)surance business and for the last 20 years has specialized in risk and innovation and linking academia, government, and finance.  He has worked and talked around the world on topics relating to insurance innovation.  He is currently the CEO of Oasis Loss Modelling Framework based in London and we caught up with him on the Premonition Podcast to talk about the impact of big data and innovation on the insurance industry. The conversation in full; Andrew Weaver: Hello Dickie Whitaker, welcome to the Premonition Podcast. Dickie Whitaker: Well good morning, and glad to be here. AW: We’re looking at the impact of big data and Insure tech on the market. Tell me this, insurance has been transacted in the same way for a very long time, and the insurance industry and innovation have not always been natural bedfellows. I sense it’s changing, quite dramatically, but is that a fair assessment of the past? DW: In essence, it is, yes. I mean there are pockets of times in history when, at least parts of the world you know, have shown quite a bit of innovation. But as a general rule, at least going back through recent history, the answer to that is it’s been a traditional business and one that’s heavily regulated and that regulation tends to have a drag on innovation. But I think that’s changing and what we’ve been seeing in the last few years is a drive for innovation that’s coming from some fundamental attacks on the ultimate business models of insurance. It’s coming from a prevalence of what these days we call Insure Tech, which is sort of our version of Fintech.  What that’s doing is it’s making people think that we’ve got to think differently about big data and machine learning and the sort of AI. The sort of things we’re seeing in other sectors are beginning to worry people in the insurance industry, and they think the answer is innovation and they’re trying to work out how to do it. So it’s exciting times for those reasons. AW: I’m very interested from the premonition perspective on where you see big data playing a role? DW: It’s yet uncertain how it’s going to completely play out, but there are some things we can see.  You’ve got things like telematics as the obvious example. Where being able to put a bit of kit in the car and being able to monitor on a regular basis the driving habits of, at least initially young people. What that’s doing is providing an enormous amount of data from which insurers can say, “What are we going to do with this? How are we going to use it? What information does it give us on risk?” And that’s very much changing the sort of way people are looking at things. I think though that’s just the tip of the iceberg. I know another organization, for example, that’s looking at training and behavior and how behavior is changing what’s going on. And all of those things are coming together on a number of different new platforms. I can think of two new data management platforms that are offering services around how can we begin to bring all this data together and make some sense of it. There is a lag though, I think the insurance industry is still working out how to consume this information, how to mix this information, how to bring it into the sort of key functions of sort of risk selection and pricing. But it’s happening, and it’s happening fast. My guess is the pace will accelerate. AW: I want to come back at the end of this to your views on where the major changes will come. But one thought I’ve always had is that insurance has traditionally been focused on how we lived, rather than how we live. Do you think that big data and risk assessment can bring it into the present? DW: Absolutely. Not only does it need to happen, but it’s beginning to happen too. So you’re absolutely right. Historically insurance risk analysis, and for that matter, customer analysis has always been looking in the rearview mirror. You take historical activity, historical losses, historical customer statistics. Now not only can we say, “Well, what do people want now, today? And what is their performance today? And what is their risk profile today?”, but we can actually look into the future as well. So there are considerable debates going on, the obvious one perhaps is climate change, where we’re beginning to use some of the data that’s emerging, to look in the future. So it’s the people, their trends, and activities, that’s where we’re going to see the biggest change coming forward. AW: The insurers or the Insure tech companies that are coming into the market, will become ever more niche, using data to drill down into the habits of particular niche areas within insurance. DW: I think that’s true.   I’m not a big fan of huge companies coming in and saying, “We’re going to be all things to all men.” I never really believe that, but it had some logic when you had an IBM type of footprint to survive. Today I think the opposite is true, so you need to be light, you need to be focused, you need to be agile. Those are the characteristics that make sense and those are the ones that allow innovation to thrive. We’ve got a little bit of a bottle jam, because I think whilst that is true on the one hand, we still need better technology systems to be able to distribute this and I think that’s probably one of the biggest areas of weakness, where we’re not seeing enough platforms allowing interoperability of data and tools into the insurance industry. So I think that’s our biggest gap right now. AW: With the insular world that we live here at Premonition with legal data, and as we begin to penetrate the insurance market … now that legal data is available in the way that it is with the likes of Premonition, how do you think this can help the industry? DW: When I first saw that data set I was just sort of amazed that somebody had been able to get all that data together.  Pretty quickly after that, I started thinking about the opportunities that it could help create. I would say there are two things. The first one is, one of the bigger challenges that we face in the insurance industry, is trend identification. So it’s where’s the next big thing is going to happen?  One of the obvious abilities that you can do with your data is able to say, “Well, actually there looks like there’s increasing litigation in this area, this type of business. Or alternatively, perhaps, we’re seeing awards change dramatically in this State, or in this part of the UK, or in this profession.” That type of trend analysis is really the bread and butter of the insurance industry. That’s essentially how you understand, how you select risks, how you price risks, how you develop the right amount of sovereignty capital. So that to me is, that’s the golden egg that I am sure people are going to take hold of. AW: How do you see the before and after in terms of the market and big data? But you know, the way that people dealt with risk assessment before something like Premonition came along and after, it should dramatically change the way they assess the risk. DW: Some of this data historically has been available but in many cases, it’s been available only to the largest companies who’ve got the longest track record in the business. So actually it’s sort of perpetuating some of the big beasts in the marketplace and what it really means is that light, new, agile companies can come along and go, “I don’t have to have 20 years track record of underwriting in the UK, or Australia, or the US. I can get that information from this database.” And as you say, there’s an additional element, which I think frankly is in the infancy of utilization by the insurance industry at least, of saying, “We are more likely to be able to direct …” It’s almost like a sort of loss adjuster function. If a house sort of burns down or has got a problem, one can start saying, “Well, actually don’t you think, if you’re going to get this judge or this expert witness …” That we’re increasingly able to say what the prognosis will be of the size of the award or success of the award, and therefore take action before that happens to reduce costs or to make a better decision as a result. And that’s never been able to be done before. AW: I’m going to close this podcast fairly shortly but just give me a view on a couple of final questions. Is open data a key element in success and fairness in the market? DW: Open data to me is extremely important for a couple of reasons. The sort of foundation for a company that I started, is all about the openness of data. Because I it does a couple of things. One, you can take a fairly moral and ethical view here and just say that actually society needs to have access to the information, that’s what’s is efficient for society, and that’s what allows people to build products and get transparency and get fairness and equity. So for all those reasons, it makes sense. But I think it’s this angle on innovation that’s really interesting. I’m fascinated by the ability of people all around the world to take data and say, “I’ve got a new use for it. I’ve got a new application. I’ve got a more efficient way of doing this.” I think we just need that. It’s both exciting, but you know it’s important. And productivity is increasingly being looked at around the world. This is the way we’re gonna sort of deal with it, and we just need to have openness around the world, all types of data. AW: Final question.  There’s much talk about the convergence of incumbents and Insure tech and where that might or might not lead. Have you got a view of when the major changes will happen in the next five, 10 years? DW: If you look at typical cases around the world where you get some sort of innovation driver against a well-established industry, you tend to get a sort of common pattern. The common pattern is the slow-moving companies just can’t move quick enough, and therefore some of them fall by the wayside. Others can move quick enough and actually do what’s needed to be done.  Then you get new players that were never there before, that come onto the scene, that end up by driving their sort of business models in that space. So I think we’ll inevitably see some of all of the above in a mix that will be slightly unique because every market is unique. But we’ll see significant disruption, significant new players. We’ll evolve, and we’ll all be better off. The post Premonition Podcast 3 : Dickie Whitaker, Oasis Loss Modelling Framework appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.

Premonition Interview with Ty Sagalow about Lemonade, Premonition & The Future Of Lawyer Selection We were delighted to be joined at the Premonition Podcast by Ty Sagalow, Founder & CIO at The Insurance Innovation Group, Founding Member of Lemonade and Senior Advisor and CIO with us here at Premonition. Show Podcast Transcription (2,478 Words)   Andrew Weaver:             Ty, welcome, thank you for joining us on this podcast. Ty Sagalow:                   Oh, thank you for asking me, Andrew. Andrew Weaver:             It’s a great pleasure. I’m intrigued and interested. You’re described as an insurance veteran. But I think your work in insurtech means it’s more appropriate to call you a spring chicken. Ty Sagalow:                   I think that’s right. I mean, I don’t think there are any insurance veterans when it comes to insurtech. It’s really been around as an industry only since 2015, so that’s only two years ago as of today. But in terms of the traditional insurance industry, I’ve been around for 35 years and the insurance industry has been around for several hundred years, so perhaps it would be incorrect to call any living human being an insurance veteran. Andrew Weaver:             You worked for significant insurance incumbents before you joined startups with Lemonade, and we’ll come on perhaps to Premonition as well later. What made you decide, what was the decision process for you to leave that traditional model behind and go with startups? Ty Sagalow:                   I built my entire career, and it was about 33 years in the traditional insurance industry 25 a day IG. My entire career was built really about innovation. I saw that there was a need to be creative in the insurance industry. Since there aren’t too many folks in the insurance industry that have that as a goal, it was a nice foundation for a career, because you always try to choose something that very few other people are doing. I was very happy doing that, and did some very, very cool things like create YQK insurance and cyber insurance, you know. At the time I thought that was about as cool as the insurance industry can be. But eventually I realised that the traditional insurance industry in itself can really only execute incremental innovation. But at the end of the day, disruptive innovation. This is not just true of insurance. It’s true of all industries. But true disruptive innovation has to come from outside the insurance industry. So at 33 years I went out on my own, created my own consulting company really in search for disruptive innovation. I had no idea whether I would find it, and if I did find it I had no idea where it might end up. Andrew Weaver:             As part of my research for this podcast, I heard a story you told that related to the Urban Dictionary and the perception of the insurance market. It’s interesting how people perceive the insurance market, and perhaps that’s a fundamental part of the problems it’s encountering at the moment. Ty Sagalow:                   Yeah, it is. I eventually realised that what was bugging me about the insurance industry, the traditional insurance industry was how much it’s hated. You don’t want to be in an industry that everybody hates you. For those in the audience that don’t know what the Urban Dictionary is, it is the essence of the wisdom of the crowd, all right? So folks will submit into the Urban Dictionary what they believe the definition of an industry or a definition of a thing is, and then the ones that have the most votes of a similar definition wins, and that becomes the definition. So the Urban Dictionary asked people, “How do you define the insurance industry? What does the insurance industry mean to you?” The answer that came back was, “The insurance industry is an industry that makes promises to pay in the future, which promises are never fulfilled.” You know, having spent my entire life in this industry it’s not very pleasant. Eventually working with some really, really, really smarter people, we figured out that the reason for that was that there’s something fundamentally broken about the current modern insurance model, whereby there is a conflict of interest inherent in the model between the claims department of an insurance company and the policyholders, where for every dollar that is not paid in a claim, that is another dollar of profit for the insurance company. So the insurance company and their client are in direct conflict. It’s hard to think of another industry that has survived where the industry and their client are inherently in conflict based upon the business model of how that industry works. Andrew Weaver:             So here you are with an industry that’s fundamentally flawed in many ways. You’ve had a 33 year career. What took you to Lemonade? How did you find that project? Ty Sagalow:                   Well, actually, Lemonade found me, which was really cool. So I was, you know, you can imagine, I’m a consultant, I’m in my office, I get phone calls on a regular basis from entrepreneurs that want to do something fascinating and they need an insurance product but the insurance product that they need doesn’t exist. Eventually they come to me and I create something new. So it was on a day like any other where I got a phone call from an entrepreneur. He had a nice British accent like you, so a little bit different than most folks that would call me in New York, and he wanted to meet me to talk to me about this new idea. So I met him, of course, and he only knew three things at the time, he wanted to create a company called Lemonade, it would have something to do with the insurance industry, and it would some way be able to give back to the community. His name is Daniel Schreiber, one of the most brilliant people I know, a great executive, and eventually learned something about insurance, but that first meeting he knew nothing about insurance. So he and his co-founder Shai Wininger, who’s the CEO of Fiverr and probably one of the world’s greatest technologists and software coders, we started with the name on the wall, Lemonade, and then after three days we brainstormed what type of insurance company it could be. They were kind enough to ask me afterwards to stick around and be the chief insurance officer of Lemonade and the CEO of Lemonade Insurance Company. It was a tremendous adventure. Lemonade quickly become the leader in the insurtech space. But again, if the space is only two years old, you can do that sort of thing. Andrew Weaver:             Well, phenomenal growth. So your time with Lemonade ended in July of this year? Ty Sagalow:                   Correct. So in July 1st of this year I transitioned off of Lemonade, having done the job I was hired by Daniel and Shai [inaudible 00:09:02]. With underwriting claims, finance, we had our domestic licence in New York, licenced in over 22 States, representing 60% of the company. They asked me to stay on the board, so I’m still on the board. But then I went back to consulting to look for my next adventure. Andrew Weaver:             Which landed where? Premonition I think is the name of the company? Ty Sagalow:                   Yeah, Premonition is the name of the company. I am so excited about my affiliation with Premonition. When I went out after Lemonade and I was sort of looking for the next Lemonade, and what I mean by that is I was looking for the next company that would severely disrupt the insurance industry. You know, the co-founders of Premonition found me in much of the way that Daniel and Shai found me. They began talking to me about an area of insurance, which had long bothered me, which was the lack of scientific method in managing and choosing outside counsel. The company had been around for four years, but they had not thought of it as a potential solution to insurance. Then after discussions with me we all concluded that there was a lot there and they asked me to stick around as an advisor and as the chief insurance officer to help talk to the insurance industry about the Premonition business model and how it could be helpful to them. Andrew Weaver:             One observation I made in terms of the connection between Lemonade and Premonition is that they’re both working with how artificial intelligence and transparency are transforming the space. Did you see that when you landed on Premonition? Did you see the connection? Ty Sagalow:                   No, actually, not on a conscious level. Andrew, you’re 100% right. I mean, up until five minutes ago I had not thought about it. But you’re absolutely right. I think one of the drivers toward Premonition is the fact that it shares a couple of fundamental characteristics with Lemonade. It is a company based upon artificial intelligence and algorithms and it also is fundamentally based on transparency. So as Lemonade actually has transparent chronicles that tell insureds everything that’s going on within the company and of course within their insurance, in many ways Premonition takes you to a step further and shines light on an area where there was no light, which is, how do you manage your outside counsel, how do you manage even panel counsel in many ways? What are the KPIs? What is the data behind those KPIs? And how can you lower your, not just expense ratio, but more importantly loss ratio, in addition to what I realised later was a tremendous help that the Premonition business model, database and algorithm can do for the underwriting community. Andrew Weaver:             For the one or two listeners that don’t know what Premonition do or are, they have the world’s largest litigation database. 40,000 cases a day are scraped it’s an extraordinary machine. From that they’re able to extract very detailed analysis about the performance of judges and lawyers and courts, and the three of them combined. Ty, who in the insurance industry do you think will find the premonition database and win rates useful? Ty Sagalow:                   Well, your summary of Premonition is good. So it has two fundamental value propositions. One, it has the largest litigation database, 87% of all cases in the United States and in many other countries as well are in this database, which includes the judge, the plaintiff’s attorney, the defendant’s attorney, the venue, the duration of the case, where it is, and the type of case. Simply put, it has more data than all of the competitors combined, really phenomenal, and again, shows that true disruption can only occur from the outside. So the second value proposition is the artificial intelligence. It is the algorithm that takes this massive database and calculates win rates, which attorneys against which attorneys in which courthouses against which judges, in which types of cases are more likely to win. The easiest way of thinking about it is money ball, right? So it is the money ball selection of outside counsel on these two value propositions. The most obvious place in the insurance industry is of course the claims department, right? So the claims department chooses the outside counsel, the claims department puts together a panel for their liability claims. The claims department manages those outside counsel. The claims department decides when to settle, decides when to appeal. So obviously knowing which attorneys do well against which judges in which cases will have a dramatic impact on your ability to manage those cases. Knowing simply the duration of the cases will be able to help you pick which lawyers you want and drive down your expense costs. So the first and obvious part is claims. But the more we started talking to carriers, the more we realised that the underwriting department can find this database plus algorithm to be as useful. A couple of obvious examples, right? So if you’re a lawyer’s professional liability underwriter, you underwrite the errors and omissions of attorneys. This type of database would be immediately helpful to you in knowing which attorneys are, all else being equal, good risks or bad risks. Simply knowing all the types of cases your insured has been involved in is critical. Because as any lawyer’s professional liability underwriter will tell you, lawyers get sued when they go out of their comfort zone. Taking cases outside of your expertise will not necessarily be disclosed in your insurance application. So you need access to a third party in order to get that information. But it’s not just lawyers professional liability or attorney ENO underwriters, small company DNO underwriters, directors and officers liability insurance for small businesses is based upon the founders. So if the founders are being sued a lot, if they’re even being sued for things having nothing to do with their capacity as a director or officer of the company, if they’re being sued for divorce, for credit problems, if they’re involved in a criminal action, a DWI, all those things are relevant to the underwriting of small business directors and officers liability insurance, and yet DNO underwriters today have no access to that information. So the bottom line is, the more we talk to insurance carriers, the more various parts of those carriers we find the Premonition business model and the Premonition database and algorithm could be useful. Andrew Weaver:             Let me wrap this up with a final question that I want you to look into that crystal ball of yours and tell me where you see insurtech taking us in the next 5 to 10 years. Ty Sagalow:                   You know, my daughter asked me recently, “Daddy, what do they call Chinese food in China?” And I said, “Food.” I think the same thing’s going to happen with insurtech. In the years to come, insurtech will disappear. It will just be insurance. What today we think about as a technologically enhanced insurance industry will simply the insurance industry. I do believe that we will need to effectively deal with the inherent conflict of interest in the traditional insurance industry that is just not a way that an industry can thrive and survive. I believe wisdom of the crowd is going to replace professional centres of knowledge. Then I think the selection of outside counsel and the management of outside counsel in the very, very short years to come will look nothing like it’s had in the past. No longer will I simply pick as my outside counsel the guy that I’ve known the longest and I go drinks with and I have lunch with, and then I drive down his rates by asking for volume discounts. There will come a time very, very soon, in my belief, that Premonition and tools like Premonition will be the way, the only way in which outside counsel is chosen and outside counsel is managed by the insurance industry. Andrew Weaver:             I knew I was going to enjoy this conversation. I was very excited meeting you. You’ve absolutely delivered. Thank you so much for your input and your insight. Ty Sagalow:                   Well, thank you very much, Andrew. I very enjoyed our conversation. Thank you so much for all your questions. I look forward to doing this again with you sometime in the future. The post Podcast With Ty Sagalow, CIO at Premonition and Employee No. 1 at Lemonade Inc. appeared first on Premonition. Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. Follow us at https://www.linkedin.com/company/premonition-analytics for insider legal news and visit us at Premonition.