9 min read

Profiles in Risk BONUS INTERVIEW – Shannon Shallcross of Pinpoint Predictive

Profiles in Risk BONUS INTERVIEW – Shannon Shallcross of Pinpoint Predictive

Profiles in Risk BONUS INTERVIEW – Shannon Shallcross of Pinpoint Predictive

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 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.

+ 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. Nick serves as our Chief Evangelist.

Profiles in Risk – E491: Playing The Long Game With Technology – a Conversation with Bruce Broussard, Managing Partner of Percipience

4 min read

Profiles in Risk – E491: Playing The Long Game With Technology – a Conversation with Bruce Broussard, Managing Partner of Percipience

Profiles in Risk – E491: Playing The Long Game With Technology – a Conversation with Bruce Broussard, Managing Partner of Percipience by Nicholas...

Read More
Profiles in Risk – E475: The Evolution of Property Data Continues – Vivek Sablani, CEO of Wizard Analytics

2 min read

Profiles in Risk – E475: The Evolution of Property Data Continues – Vivek Sablani, CEO of Wizard Analytics

Profiles in Risk – E475: The Evolution of Property Data Continues – Vivek Sablani, CEO of Wizard Analytics by Nicholas Lamparelli In late 1992,...

Read More
Profiles in Risk: E463 – Employee Benefit Plans on Steroids; Jordan Peace, Co-Founder & CEO of Fringe

2 min read

Profiles in Risk: E463 – Employee Benefit Plans on Steroids; Jordan Peace, Co-Founder & CEO of Fringe

Profiles in Risk: E463 – Employee Benefit Plans on Steroids; Jordan Peace, Co-Founder & CEO of Fringe by Nicholas Lamparelli Let’s think...

Read More