--- title: Profiles in Risk BONUS INTERVIEW – Shannon Shallcross of Pinpoint Predictive - InsNerds.com description: Nick Lamparelli interviews Shannon Shallcross, Head of Client Services at Pinpoint Predictive about predictive modeling and its important function in the Florida Insurance marketplace FULL TRANSCRIPT 1 00:00:00,000 --> 00:00:04,030 …Hi We're back. We're at the 2023 Florida 2 00:00:04,030 --> 00:00:09,020 Chamber annual insurance summit in…Disney World. 3 00:00:09,020 --> 00:00:12,565 Orlando, Florida. And --- [Insurance Nerds - Insuring Tomorrow ](https://insnerds.com/insights) # [Profiles in Risk BONUS INTERVIEW – Shannon Shallcross of Pinpoint Predictive - InsNerds.com](https://insnerds.com/insights/profiles-in-risk-bonus-interview-shannon-shallcross-of-pinpoint-predictive) Written by [Nicholas Lamparelli](https://insnerds.com/insights/author/nicholas-lamparelli) | Jan 27, 2024 5:00:00 AM # Profiles in Risk BONUS INTERVIEW – Shannon Shallcross of Pinpoint Predictive by Nicholas Lamparelli Nick Lamparelli interviews [Shannon Shallcross](https://www.linkedin.com/in/shannonshallcross/), Head of Client Services at [Pinpoint Predictive](https://pinpointpredictive-20366300.hs-sites.com/contactus) about predictive modeling and its important function in the Florida Insurance marketplace FULL TRANSCRIPT 1 00:00:00,000 –> 00:00:04,030 …Hi We’re back. We’re at the 2023 Florida 2 00:00:04,030 –> 00:00:09,020 Chamber annual insurance summit in…Disney World. 3 00:00:09,020 –> 00:00:12,565 Orlando, Florida. And who am I speaking with? 4 00:00:12,565 –> 00:00:16,545 I’m Shannon Shallcross I’m the head of client services from Pinpoint Predictive. 5 00:00:16,545 –> 00:00:22,255 And what does Pinpoint Predictive do? So Pinpoint Predictive is a predictive modeling company 6 00:00:22,255 –> 00:00:28,050 We do risk predictions for the insurance industry, and it’s not cat predictions it’s not cat 7 00:00:28,050 –> 00:00:33,930 modeling but what we’re really looking at is the individual behaviors of a person. 8 00:00:33,930 –> 00:00:39,920 Understanding that someone’s behavioral characteristics…has a bearing on if they’re gonna file 9 00:00:39,920 –> 00:00:45,165 a claim…if they’re gonna be involved in negation if they’re gonna cancel early. 10 00:00:45,165 –> 00:00:51,945 And it’s kind of a premise that is similar to the fact that credit is used in insurance so with 11 00:00:51,945 –> 00:00:56,570 credit…the the understanding there is you’re a good 12 00:00:56,570 –> 00:01:01,205 insurance risk if you have a high insurance based credit score. 13 00:01:01,205 –> 00:01:05,805 Similar premise for us that we’re looking at the behaviors of an individual but our process does 14 00:01:05,805 –> 00:01:12,105 not use credit at all Mhmm And our predictions are available at the earliest possible point So 15 00:01:12,105 –> 00:01:18,155 really before someone even fills out an application, You just have their name and address Don’t know anything else about them. 16 00:01:18,155 –> 00:01:21,205 You can pull a prediction to understand their risk propensity. 17 00:01:21,205 –> 00:01:26,090 Okay So don’t use credit score Correct Okay So…that’s unique. 18 00:01:26,090 –> 00:01:32,020 How is that possible? Magic. Imagine we’re we’re in Disney We’re in Disney world It’s very 19 00:01:32,020 –> 00:01:38,935 fitting…No Just kidding so The way that that’s possible is we’re using a different 20 00:01:38,935 –> 00:01:43,825 data source and it’s a data source that usually is not used by the insurance industry. 21 00:01:43,825 –> 00:01:47,775 It’s behavioral like omics data. And when you have enough 22 00:01:47,775 –> 00:01:51,795 of this type of data and I’m talking about trillions of data points Okay. 23 00:01:51,795 –> 00:01:56,310 And we have this on two fifty million US adults. 24 00:01:56,310 –> 00:02:00,700 there are a lot of things that you can do when you would apply deep learning machine learning in 25 00:02:00,700 –> 00:02:05,890 the modeling process And that’s what we do Yeah So that’s amazing So what would be I’m assuming 26 00:02:05,890 –> 00:02:12,572 social media, what what would be…No So no social media And the reason for that 27 00:02:12,572 –> 00:02:17,802 is when you do social media scraping, there is a potential that you’re adding bias into the 28 00:02:17,802 –> 00:02:22,942 modeling process and we have put in some pretty tight controls to make sure that we’re not doing 29 00:02:22,942 –> 00:02:28,162 that especially in light of some of the you know the NAIC had just really to bulletin on 30 00:02:28,162 –> 00:02:33,060 insurance companies’ use of artificial intelligence, we wanna control for bias. 31 00:02:33,060 –> 00:02:38,510 So we’re using data that is behavioral like an my data So it’s essentially what people are doing 32 00:02:38,510 –> 00:02:42,395 on screens what media they’re consuming, what are they buying. 33 00:02:42,395 –> 00:02:48,205 again they’re they’re data points that literally have nothing to do with what we’re predicting. 34 00:02:48,205 –> 00:02:52,865 But when you’re able to develop this behavioral fingerprint of a person Yeah In the model 35 00:02:52,865 –> 00:02:55,905 training process we can find out Okay. 36 00:02:55,905 –> 00:03:01,510 So this behavioral fingerprint looks a lot like someone who ends up becoming a…or it looks a 37 00:03:01,510 –> 00:03:06,770 lot like someone who is gonna file three claims in the first policy period. 38 00:03:06,770 –> 00:03:11,760 But on the opposite side of that coin and this is the part that is really important for carriers 39 00:03:11,760 –> 00:03:17,402 You can also identify who do we know is gonna be profitable at that really early point. 40 00:03:17,402 –> 00:03:21,732 So that’s very helpful to know because then you can understand when exactly you can quote and 41 00:03:21,732 –> 00:03:26,825 bind as quickly as possible Yes so it’s it’s that fast. 42 00:03:26,825 –> 00:03:30,985 So we’re talking potentially seconds to Subs tracking. 43 00:03:30,985 –> 00:03:34,745 Sub seconds to turn information around. Yes Yes. 44 00:03:34,745 –> 00:03:38,670 So we’re essentially…the integration is an API integration. 45 00:03:38,670 –> 00:03:43,930 So wherever a prediction is pulled, name and address, you get a prediction. 46 00:03:43,930 –> 00:03:49,120 Amazing. so why Florida? Why Why Florida Well why are you here? 47 00:03:49,120 –> 00:03:54,370 My question. Because of the magic…Well I I think I already know why Florida because you’re 48 00:03:54,370 –> 00:03:58,810 predicting claims but Yeah Let’s let’s specifically talk about like why Florida? 49 00:03:58,810 –> 00:04:04,095 Yes So a couple things about the Florida market are really important to our clients. 50 00:04:04,095 –> 00:04:08,350 number one, the citizens’ depopulation…has been 51 00:04:08,350 –> 00:04:12,860 something that’s been a really hot topic It’s a great opportunity for carriers here. 52 00:04:12,860 –> 00:04:18,180 But at the same time, the onus is on the carrier to really understand the nature of risk as 53 00:04:18,180 –> 00:04:23,627 they are evaluating…any new book of business to acquire It doesn’t even have to be related to 54 00:04:23,627 –> 00:04:30,357 that particular thing…but we are providing a prospective view of risk It’s not looking back 55 00:04:30,357 –> 00:04:33,397 It’s looking forward, and it works really well. 56 00:04:33,397 –> 00:04:38,217 The other one that we’ve had a lot of success with in Florida is a litigate prediction. 57 00:04:38,217 –> 00:04:41,390 Yeah Because litigation, I I know that reforms 58 00:04:41,390 –> 00:04:45,450 passed…last December, and those have been really great. 59 00:04:45,450 –> 00:04:51,110 But this has just been a constant issue for years and years and years in insurance where, there 60 00:04:51,110 –> 00:04:56,472 are loopholes…in the auto industry and the homeowners industry…And as soon as those 61 00:04:56,472 –> 00:05:00,682 loopholes are found, they turn into enormous problems for carriers. 62 00:05:00,682 –> 00:05:07,242 So it’s really helpful to be able to understand what’s the likelihood that this policy 63 00:05:07,242 –> 00:05:12,435 holder is gonna respond to a doorkeeper…that’s actually something that you can predict Yeah 64 00:05:12,435 –> 00:05:19,020 Yeah And and Steven Weinstein…who’s premier Permuting…reinsurance expert He was interviewed 65 00:05:19,020 –> 00:05:24,640 by AM Best recently and he was going he was talking about how this has been a problem forever 66 00:05:24,640 –> 00:05:29,720 Yes…In Florida’s like first it was, you know mold is gold. 67 00:05:29,720 –> 00:05:35,680 Right And then sync sync whole problems Like, so I was there for that. 68 00:05:35,680 –> 00:05:40,660 And so like that’s really interesting because I think in when I think when most most folks think 69 00:05:40,660 –> 00:05:47,650 of citizens takeout, they think of cat modeling…and trying to optimize the portfolio… 70 00:05:47,650 –> 00:05:53,605 Yes But this is like an additional layer on top around like okay…the problems that we’ve had 71 00:05:53,605 –> 00:06:00,405 in the last few years are not really cat…oriented They’re litigation oriented And you and you 72 00:06:00,405 –> 00:06:06,070 and that’s not gonna probably go away with the reforms They’ll just find some other…entrance 73 00:06:06,070 –> 00:06:11,400 Exactly…It’s about that people part of the risk And especially in Florida that people part of 74 00:06:11,400 –> 00:06:17,750 the risk, can turn into very high dollar claims if you aren’t able to get your answer out of it 75 00:06:17,750 –> 00:06:24,072 Yeah…so personal lines commercial lines, So we’re doing business in both personal lines and 76 00:06:24,072 –> 00:06:28,412 commercial lines personal lines primarily auto and homeowners insurance. 77 00:06:28,412 –> 00:06:35,192 For commercial lines…we do predictions for companies that have less than twenty employees 78 00:06:35,192 –> 00:06:41,792 because we found that using this process there’s a very strong signal with the business owner. 79 00:06:41,792 –> 00:06:44,992 So the behaviors of a business owner actually do play 80 00:06:44,992 –> 00:06:48,835 out in their loss experience and their loss trend. 81 00:06:48,835 –> 00:06:55,382 So commercial lines…we’re operating there as well workers comp Okay So I’ll tag…I’ll tag 82 00:06:55,382 –> 00:07:00,952 Pinpoint and yourself on this How how should people get in touch with Pinpoint or for yourself? 83 00:07:00,952 –> 00:07:06,652 They they can reach out to me happy to talk with them We have an awesome sales team too We have 84 00:07:06,652 –> 00:07:12,522 information on our website as well so really happy to have any discussions We work with carriers 85 00:07:12,522 –> 00:07:18,650 every single day on developing…strategies like this that help them to optimize all of their process. 86 00:07:18,650 –> 00:07:25,650 Okay So we we are in Disney World. So…who is your favorite Disney character…Oh 87 00:07:25,650 –> 00:07:32,650 boy This is not something that I We can include Pixar…You know, this 88 00:07:32,650 –> 00:07:37,810 you’re gonna laugh but I have to say the little mermaid, and this is a throwback to when that 89 00:07:37,810 –> 00:07:42,540 movie first came out, my younger sister it was her favorite movie. 90 00:07:42,540 –> 00:07:46,460 And my fun fact is I know every single line 91 00:07:46,460 –> 00:07:50,600 to that movie, every single song that’s in that movie. 92 00:07:50,600 –> 00:07:57,552 Because she probably watched it at least two hundred times when I was growing up Yeah So old school…here. 93 00:07:57,552 –> 00:08:03,122 So Pinpoint Predictive is using new technology, but it’s being sold by old school. 94 00:08:03,122 –> 00:08:07,003 Yep. Yeah K Shannon thank you Thank you. ### About Nicholas Lamparelli Nick Lamparelli is a 20+ year veteran of the insurance wars. He has a unique vantage point on the insurance industry. From selling home & auto insurance, helping companies with commercial insurance, to being an underwriter with an excess & surplus lines wholesaler to catastrophe modeling Nick has wide experience in the industry. Over past 10 years, Nick has been focused on the insurance analytics of natural catastrophes and big data. Nick serves as our Chief Evangelist. - ##### [ Nicholas Lamparelli](https://insnerds.com/author/nick-lamparelli/) + posts Nick Lamparelli is a 20+ year veteran of the insurance wars. He has a unique vantage point on the insurance industry. From selling home & auto insurance, helping companies with commercial insurance, to being an underwriter with an excess & surplus lines wholesaler to catastrophe modeling Nick has wide experience in the industry. Over past 10 years, Nick has been focused on the insurance analytics of natural catastrophes and big data. 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