March 6, 2023 • 41 mins
In this episode, ADJUSTED welcomes Sam Neer, Group Product Manager with Berkley Alternative Market Technology (BAMTECH). Sam discusses how technology and claims work together, and the ever changing landscape that is technology.
Season 5 is brought to you by Berkley Industrial Comp. This episode is hosted by Greg Hamlin and guest co-host Matt Yehling, Directory of Claims at Midwest Employers Casualty.
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Visit the Berkley Industrial Comp blog for more!
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Greg Hamlin (00:12):
Hello everybody and welcome to adjusted. I'm your
host Greg Hamlin coming at youfrom beautiful Birmingham,
Alabama and Berkeley industrialComp and with me is my co host
for the day, Matt Yehling.
Matthew Yehling (00:24):
Hello, everyone. This is Matthew
Yehling coming from St. Louis,Missouri along the banks of the
Mighty Mississippi.
Greg Hamlin (00:31):
So glad to have Matt with us today. We actually
have a special guest with us.
That is with Berkley. Actuallyit goes by the acronym BAM TECH
but Berkley alternative marketstechnology, our special guest
for the day is Sam Neer who isour group Product Manager at
Berkley alternative marketstech. So Sam, could you say
hello to everybody?
Sam Neer (00:53):
Hey, everybody, very excited to be here, Greg and
Matt. And again, I'm based outof Raleigh, North Carolina,
where it's a little bit warmer,not as warm as Birmingham,
Alabama right now, but it'sstill there. And I will say no
one wants to say Berkeleyalternative markets technology
over and over. So we might justcall it BAM tech just for all of
our sanity if that's okay.
Greg Hamlin (01:12):
I think that's wise. I think that's why it's so
the topic today, this issomething we've never really
tackled in the nearly threeyears that I've been doing
adjusted is claims andtechnology, and the role that it
plays in helping us do our jobs.
So I wanted to bring Sam intoday to talk to us a little bit
about that since he is part ofthe Berkley company that manages
(01:32):
our technology to help us besuccessful. Before we dive deep
into the topic. Sam, I wanted toask you a little bit about
yourself. So I'm sure that youknew you were going to be in
workers compensation, right?
Sam Neer (01:48):
Greg knew it is again, coming up as a small boy, I
would say to my dad with a glintin my eye one day dad, I come to
work and workers compensationand a quippy line I'll have is
either the way I ended up in theindustry is either I found my
way into making great technologyproducts with great
organizations and reallyeffecting change, or I follow
the goal of becoming the moststable, uninteresting version of
(02:10):
myself one of the two. Andagain, maybe, maybe it's a
little bit of column D.
Greg Hamlin (02:16):
there's a lot of stability in worker's
compensation insurance. Sothat's the one positive right.
Sam Neer (02:21):
That's it's something to be accounted for. So with the
32nd background, again, I'vebeen working in digital
technology for the last 12 yearsand product management, started
off in PR and analytics thenmoved to telehealth right at the
height of the pandemic would notrecommend trying to jump into
telehealth at that time, andhave only been in sort of the
insurance industry for you know,for a couple years now. But at
the same time, it really just abaptism by fire. But I've
(02:43):
learned a lot. And I thinkinsurance is a great area,
insurer tech, that's having alot of new frontier, a lot of
legacy systems upgrading. Soit's a great time to be alive.
And again, my specialization isclaims I'm beginning to dabble a
bit more on the policy andunderwriting side. But the
trends are out there that arehappening impacting all of us,
and really excited to talk thosethrough. That's awesome.
Greg Hamlin (03:04):
Well, I know the headlines right now, when you
hear about technology, you hearabout the mass layoffs of
companies like Twitter, andFacebook and everybody else,
Google. So there really issomething to what you know, as
you were joking, there's somecivility that comes with
insurance and worker'scompensation, it might not be as
glamorous. That's definitely notthe Silicon Valley dream, right.
(03:26):
But there's definitelyopportunities for stability.
Sam Neer (03:28):
And glamour is in the eye of the beholder, right. And
I will say that the impact andthe scale of which we're being
able to operate here at Berkleyis enormous, right? And so
that's what I do enjoy that. Andyou getting to build technology
that powers really a lot of whathappens in the workplace is
really awesome. So I'm thrilled.
Okay, I tell my wife, I'm thecoolest guy ever. And she
doesn't believe me yet, butshe'll get there soon.
Matthew Yehling (03:50):
So when we talk about technology and claims, you
know, claims is been around fora while, what do we mean? What's
the role of technology inclaims?
Unknown (04:01):
I think that's a great question. Again, I'm gonna start
off with the buzzword, becauseeveryone needs a cool buzzword
to begin, but I'm gonna say thetechnology and claims the word
of the day is augmentation.
Right? So with this, as youknow, the claim experience is a
very human one, right? You're inthe moment you're in the
accident, how are you basicallygetting through that process?
And that's why at least how Iview this is how can technology
(04:22):
assist in that process toaugment the adjuster and the
humans who are very muchinteracting with that claimant
at that moment, and thatexperience, and also with the
policyholders, and also withagents again, there's a whole
spectrum right here. But withthat, as some people just tried
to say, can we automate it? Canyou just talk to a computer? Can
you never interact? And yes,there's going to be the small
paper cuts or the reportaltogether, there's going to be
(04:43):
some subset that can beautomated. But the way that I've
been viewing against leaves mytime here at Berkley is how can
we use the best of technology toaugment the best of humans to
really attack these problems? Sothat's where I believe that most
places will say just throw Ourcomputer or machine learning
model at it. But in reality, howcan we leverage technology to
(05:03):
leverage humans is the way thatleast I've been trying to
approach it.
Matthew Yehling (05:08):
Can you give an example? You know, a recent
example maybe of how you'reusing technology to augment
something in the claimsindustry?
Unknown (05:17):
Way too many Matt and again, this is what keeps me on
my toes, right. So with that,for instance, sometimes again,
we will use machine learning orwhat some people call AI. So ML
is the buzzword machinelearning, which is sometimes
people call it AI. It's allthese different cool terms,
right, just saying, hey, the,you know, Skynet is doing
something in the background. Sofrom there, we at least some of
(05:40):
our Berkley time will say, hey,when a new claim comes in, based
off the first report of injury,how do we get some of these
early details to identifywhether it's going to be a large
scale claim, or just, you know,just the paper cut, right. And
again, when it's the smallerones, we're saying, hey,
adjuster doesn't need to do 17of the normal steps, again, all
the regulatory and must haverequirements we're always doing
(06:00):
to meet the laws of the land,but some of the stuff of the
process all the follow ups, wecan reduce some of that, on that
when the machine learning or AIor Skynet identifies and you
don't need to do everything onthis one, right, and we don't
get it 100%, but directly thatsave some time. So just do the
human steps, the augmentationwhere they need the plug in and
reduce some of that early. Sothat's a good example where
(06:23):
we're saying, hey, technologywill identify some things that
reduce some of those steps, butwe still have the human doing
the must have steps in thatprocess.
Greg Hamlin (06:31):
I think it's really key one of the things you're
highlighting there, and thatwe're not in a state where we're
looking for technology toreplace human beings. But if we
could redeploy some of thoseefforts, so we have time to do
the things humans are reallygood at that's, that's one of
the things that can make usspecial if we allow computers to
help us and technology to helpus improve the processes. Sam,
(06:53):
what are some of themisconceptions? You see, when
you start to hear about howtechnology can improve claim
processes? You're dealing with alot of claims, people who
probably don't all have highlevels of technology background?
And so what are some of thosemisconceptions you sometimes
hear where there's some, youknow, maybe they don't align
with what's actually whatactually can be done? Well,
yeah,
Sam Neer (07:13):
I think that's a great question, Greg. And, but it will
say it's, there's two ends ofthat spectrum of those
misconceptions, right. One isthat technology, hey, anything
that I don't like doing rightnow, technology can solve it can
solve world hunger, just with asingle flitch. Sam, here's the
idea. Just go make it happen.
wave your magic technology wand.
And you know, it's just like, myparents, when I go home for
(07:33):
Christmas think that i isbecause I work in technology can
solve all of their routerproblems and printer problems.
Some people think that, hey,technology can solve anything
you want. They're just like theprinters, the TV's broken, can
you fix the Roku? Right? It'slike, I can't do everything,
folks. Okay, so that's one endof the spectrum. And the other
one is that misconceptions isthat technology can't do some of
(07:56):
the cool things that humanshistorically could do. Right?
And some of those, which whenyou actually talk about it makes
sense. Like, hey, we wanttechnology to catch mistakes or
errors, right? Oh, yeah, ofcourse, I would like that. Or
could it reduce mundane,repeatable tasks, so you're
doing like think work, and notjust, you know, just click work,
right. But the two ends of thosespectrums are always fun, and
(08:18):
especially insurance, some tendsto gravitate towards, oh,
technology isn't gonna be ableto do anything. So that's the
fun conversation. And I'llactually flip the question back
to you. How have you all seen?
Like, what are some of themisconceptions you found? We're
working in this industry longerthan I have that, you know, that
come up a lot as well? Is thereany different ones? Besides from
those two extremes?
Greg Hamlin (08:38):
All right. Well, I was just gonna say the one I've
seen the most, in my experiencehas been that often technology
can do things, but what's notbeing calculated as the amount
of time and effort and resourcesthat would have to be deployed
to get there. And so often, Ifeel like adjusters managers,
(08:58):
supervisors have the want list,and the want list might save
them five minutes, but it mighttake 10 years to accomplish it
today. So then theprioritization and becomes the
issue. It's not always that itcan't happen. It probably could.
But is it really worth havingthat happen for what it's going
to cost?
Matthew Yehling (09:19):
Yeah, my example would be the first
notice of loss and automation ofthat, and triggering, you know,
maybe keywords, you know, giventhe example of highlighting, you
know, more serious claim, so youtrigger in on words like
amputation, traumatic braininjury, and those go to a more
experienced high level claimsperson versus you have something
(09:42):
where it's like contusionlaceration, and those things get
assigned to a newer person orsomebody, maybe that's less
experienced. I mean, I've seensome examples of that through
automation and augmentation, Iguess. You know, the word we're
used And so that would be anexample I've seen of where it's
(10:02):
been applied in, in the industryversus having it go basically,
across the spectrum. A new claimcomes in and always goes to
Sandra, because Sandra reviewsevery new claim, it's like,
well, you know, if we canidentify, you know, things, and
you might, you know, identify80% of the stuff, and then
Sandra just gets 20%. And thenthat time that Sandra is freed
(10:25):
up, is now able to go in and dosome other tasks versus all
she's doing is reviewing newlosses coming in. That's a
recent example I've seen.
Sam Neer (10:35):
I love that example of that. Because I will say it's
like, it's a combination ofrouting rules saying, hey, and
we picking out words, but thisis where technology like we say
can assist is sometimes when youhave to write the exact if then
rules and then you forgot aderivation, right? Some people
call things very similarly,there's also this idea in
technology called stemming, or,you know, he's like do plurals
or ing or things like that,right. And that's where you can
(10:56):
say, hey, technology, withnatural language processing,
called NLP, come up with allvariations of this type of word,
or even recommend words. Andthat's what it's getting crazy
right now. It's like, Yeah, I'lljust recommend these 20 other
words, should these route tothis type of adjuster who's
going to handle this type ofcatastrophic claim, for
instance, or this cat claim? Sothat's where it can use the
combination of an if then ruleset this kind of basic, but it's
(11:18):
good and technology, but thencan technology, figure out a few
more examples around that, tohelp escalate that? I love that
example. And again, it's onewe're even looking Yeah, we
utilize here to some degree, andhow can we expand and enhance
that as well?
Matthew Yehling (11:32):
And it's small things, and then you can
continue to build on them too,right? Yeah, it's nice to have
the home run. But every time youknow, it's a lot of times it's a
little singles, and how do yougain little efficiencies, the
1%? Better improvement, and thenthat becomes exponential
improvement over time, right? Sonow has, you know, 80% of her
time freed up to do moremeaningful other tasks? You
(11:54):
know, we're not trying toeliminate her role. It's we're
trying to elevate her role intolike, okay, let's, you know,
you're you're better utilized inthis capacity. versus, you know,
breeding. Yeah, I mean, that'sanother example. I mean, the
difficulty I work with a lot ofattorneys and nurses, you know,
the difficulty here, I get alittle pushback from them, as
I'm in the show me state. I'm inSt. Louis, I'm in Missouri. They
(12:16):
want to see everything. Sothat's some of the difficulties
I experienced on the kind of thereverse. I'm like, no, let me
review the document, give me thedocument. And
Sam Neer (12:25):
one of the fun things about the changes in technology
is sometimes under the hood, itis not easy to decouple with
machine learning and AI andinsert buzzwords, and you know,
joke Skynet from the Terminatorback in the day, right? But to
that end is, it can't alwaysshow you that way. So that's
where sometimes again, the ideaof a buzzword back again,
augmentation where you could saythe computer, or ml or AI
(12:47):
recommended this, do you want totake action on it? So it's not
basically making the choice allthe way, but it's allowing those
to say, hey, oh, I want todouble look at the document the
Show Me side, I want to Okay,let me just double check to make
sure the computer got it right.
And if it does that 100 out of100 times, or 99 out of 100.
Maybe you start trusting it. Sothat's the idea is you don't
like you said we all want tousing a bad sports analogy. We
(13:08):
all like seeing the long pastbombed Randy Moss at the end,
you know, with a touchdown, Idon't know why I'm going to the
Vikings in the 90s. But, but inreality, the teams that just
chunk it forward to like fiveyards at a time, five yards at a
time, the small incrementalgains can eventually really get
you there. And it's not asglamorous as the star play. But
that's really what's gonna movethe needle in the long run. So
okay, so
Greg Hamlin (13:31):
one of the questions I had Sam is what is
technology good at in the claimspace?
Unknown (13:36):
Yeah.
Sam Neer (13:37):
And that's where I think, you know, the unglamorous
sides of technology is thepiping right? Technology is a
good thoroughfare of informationconnecting different systems,
facilitating seamless and easycommunication interactions, a
couple of ideas that come tomind that, especially in the
claim space, especially as we asadjusters need to be more easily
contactable or basicallyreaching out to claimants,
(13:58):
things like digital payments,don't go send my old school
check to my grandma, like mygrandma did. Can I get something
in? You know, Venmo? Right.
We've heard that or texting,right? Like which kid under the
age of 25 wants to have a phonecall they dread it that's like,
is that a punishment? Right? AmI being grounded? Because I have
to answer the phone. Soeverything from the piping to
also thinking about where canidentify patterns, right?
(14:19):
technology could say I'm seeinga lot of this problems. Let me
just take this as a problem.
What should we do with it? So itcan't always say the what to do
with it. That's the milliondollar question. For instance,
one of our partners at a similarcompany Midwesterners employers
casualties have this idea ofidentifying problematic claim
notes, hey, we've seen a bunchof these claim notes. This one
(14:40):
is flagged for attention. Oranother operating unit at
Berkeley is also saying we'veseen claims like this, you might
want to go check those out. Sothese are the ways that
technology can find thosepatterns to then surface it and
then at that point, the adjusteror the human can take it there.
And so again, the reticence isSometimes that technology is not
(15:01):
as good as humans andeverything, it's like, well,
that's majority clue we trulyhave a brain, we have the
ability to think. But it canalso look at 10 million claim
notes, and come up with somegroupings of problems and say,
Hey, I've seen similar ones thata human would never be able to
do. And so and then datavalidation, and anything that we
said earlier, it's not thinktime, it's just checking a box.
(15:22):
Because computers, they do makeerrors. Trust me, I've got a
backlog full of bugs to try totackle. But at the same time,
it's much more consistent inhumans at that part, but trying
to ask your computer or eventhis newfangled chat GPT, I
tried to you know, that's thebuzzword nowadays, I tried to
ask it to write a haiku aboutclaims adjustment, and it failed
miserably. So anything oncerespective, but it did actually
(15:45):
have a good song two claims.
This is one aside about claims,I asked it to do song about
claims adjustments to the tuneof Baby Got Back. And it did
says I like small claims, and Icannot lie and it actually went
there. So it started. It'sgetting closer. But the huge
nuance, you know, is not there'swhere it needs to be.
Matthew Yehling (16:02):
Great points.
Yeah. So when you're workingwith obviously, individual
adjusters, you're working withinjured employees, you're
working with differentcomponents? What are some of the
challenges and managing thoseexpectations of the claim staff?
When it comes to technology?
What comes to, you know,enhancements and things like,
(16:22):
you know, Greg alluded toearlier, like, and you alluded
to when you're like, fix all mystuff, like what? What are some
of the, those expectations likehow do you how do you normalize
those?
Sam Neer (16:34):
That's great. And that is like, it's the idea that I
want it all. And I want it now,right? Like that's the the
problem that we're dealing withright here is like, there's so
much that we want to fix. Greg,you made a great point earlier
saying, Hey, we don't alwaysnecessarily know the size, this
will save me five minutes, butit's going to take Sam six
months to build. Right. So thislike some of the misconceptions
(16:54):
that I wanted, when thinkingabout this question, it's a
really good question. Is thatone you hear about it elsewhere?
Well, we can do it right away.
Hey, I've heard about my friendwho works at similar insurance
company, they have this coolbutton that you know, brews and
coffee and does XYZ for my claimprocess. Why can't I do that?
Right? So misconception ispeople don't forget out all the
piping all the underlyingarchitecture that goes up to
make that right. Another is likethe response times, like we
(17:16):
think that everything in everysystem is supposed to be
blistering fast, right, whichshould be like my iPhone starts
up in a second, I can't wait asecond, give something pauses,
even briefly, we're like what'shappening. So the challenge is,
again, these are misconceptionsthat are out there. So the
challenge is trying to not justaddress them, but then overcome
them. And that's where again,product management comes in my
(17:38):
entire profession is to say is,hey, we understand that your
iPhone can boot up in a second.
And we understand that yourfriend can do XYZ at this other
carrier, here's how we'redifferent. Let's work within our
sandbox, and let's createamazing things within there. So
it's really us trying toovercome those by doing the
quick explanation, because itcan't just like not, we can't do
it, then you're just feel likeeither you're being talked down
(18:00):
to. But at the same time, ourrole is in product management,
and even within technology is toexplain what we can do. And then
we partner with again, GreatPlains groups who are coming up
with amazing ideas to figure outwhat we can do. I'll actually
turn this question back to youtoo, and saying, you've seen a
lot more of these mismanagedexpectations over time. And I
know, Greg, you said earlier asa mismanaged expectation of
(18:22):
sizing, what do you think ontechnology, technology teams can
do better to help in thatconversation? Like what is we as
technologists, what do we needto learn, when actually engaging
in that back and forth?
Greg Hamlin (18:34):
Great question, I think, on are end, sometimes we
don't understand how hardcertain things are not maybe how
difficult but how many hourswould be needed to accomplish
something. And so that wish listwe've talked about can get
really long, because we can allthink of things that would make
things easier. Well, if I justclicked here, then I wouldn't
(18:55):
have to do these five otherthings. So I think sometimes
understanding also what otherthings are being worked on that
are competing for that sametime. So we could spend five
weeks adding a new button to thescreen. Or we could do this
other thing that would allow usto automate small claims. And so
(19:16):
understanding the impact, Ithink, on both sides, sometimes
maybe the technology side mightnot understand the lift that
would come from something. Andthen that's our job to make sure
we're speaking up to explainthat. And then on the other end,
I think understanding how longit takes so that we're
prioritizing the right things. Idon't know what your thoughts
are, Matt?
Matthew Yehling (19:37):
Yeah, something similar. I think spending enough
time with the people doing thejob is a lot of times the
difficulty and making sure likewhen I say I want this, like
that five button example or likeI have to click on these eight
things to have this happen.
Instead of these eight things,like would it make sense to
click on it once and then itjumps to eight. What is that? Up
(19:57):
to 234567 doing? Are they doinganything like? Just, you know,
sometimes maybe it's the, youknow, the Data Architect, or
maybe it's the person that'sactually, you know, the business
analyst. Do they understand? Andthen sometimes the claims people
really don't understand well,the reasons you have to click on
(20:17):
steps three, seven, are thislike that triggers this piping
like that, like Sam was saying,like, I think there's definitely
roles on both sides. Like, thereason the phrase that everybody
hates is like, Well, the reasonwe do it that way, is the reason
why it's done that way. Right?
(20:39):
Yeah, yeah. Because an x rayover come over and hey, can you
just cut out this process?
Sam Neer (20:46):
Love those phone calls, we love this one goes.
And I'll actually add somethingout there. I think it's great to
say as we as technologists needto get better at actually going
and sitting down next to theperson saying, walk me through
instead of just saying, I wantXYZ at the eighth step, like
skipping steps two throughseven. Let me see why you don't
need those. And then again,that's where the dialogue can
occur, right? Hey, step numberthree. And I need to give a
(21:08):
shout out to my amazing team whothinks through all these things.
So again, we have a team calledclaim to fame where we really
focus on claims shout out tothat group, what they'll say is,
Hey, you forgot about regulatoryreporting here, or you forgot
about NCCI will have thesedifferent plugins of different
steps, we may need it. But thatdoesn't mean we can't, we may
need steps now. One and two, butwe may not need 345 and six,
right? So I think it's a goodback and forth to be able to
(21:30):
explain but too many times, wejust hear I want to go jump from
one to eight, we write it off astechnologists can't do it. And
then that dies, and you losethat potential savings over
time. So rolling up the sleevesand getting in the trenches
important, it just just takestime. Right.
Matthew Yehling (21:45):
So how would you tell Greg and I are both in
claims management. And we manageyou know, the claims adjusters
and claims analyst andexaminer's etc. So, you know, we
have in our prioritizations thatare important to us, you guys
obviously have prioritizationsthat are important to it and to
technology. So what's the bestway to manage that those
(22:09):
prioritizations in the limitedresources? Because, like the
example Greg gave earlier, howdo we manage something, when
there's only a limited resource,you know, IT resources and
technology resources?
Unfortunately, we've mentioned,you know, Google earlier, like,
we don't have Google money,we're not gonna like, you know,
how do we manage thoseprioritizations?
Sam Neer (22:31):
Oh, this, these are the questions that make us happy
that we're asking thesequestions, right? Well, the
there's a simple answer,actually, man, Greg, I will tell
you the way we prioritize hisliquor and guessing I wish it
was that easy, or just adartboard. It's like, close your
eyes. And that's no, Icompletely joke. But I think a
better way of like, for lack ofa letter buzz phrase, it's
basically like, It's the voiceof the tactical combined with
(22:52):
the insight of the strategic,right. So with that, as we need
to do, anytime you ignore us, orthe people who are living this
day in and day out, we talkedabout that dialogue earlier,
then you're not going to get thethings that will actually impact
people in the day to day. On theflip side, for all you're
focusing on some details in theweeds, you will miss the bigger
strategic opportunities outthere. So the first thing we
need to do in order toprioritize the chaos, that is a
(23:12):
huge backlog and seems like 15,new things have popped up in the
last, you know, 30 minutes,since we've been talking is
combining those two, get thosetwo inputs. And then after that,
you need to basically thennarrow it down quickly, because
there's always way too much thatyou want to do. Just like my
wife has 15,000 home projectsshe wants me to do. We can't
talk about 15,000 projects, weneed to talk about five, right?
(23:34):
So first is get the inputs, doyour homework, again, with
targeted homework, you can'tboil the ocean, narrow it down,
and then pick a direction andgo. I think, you know, it's sort
of the idea that a lot of placeshave analysis paralysis. But
again, there's a show calledPrison branco TV years ago,
where the guy Wentworth Miller,who played one of the characters
there had said, There's only forfour roles you need to remember,
(23:56):
make a plan, execute the plan,expect the plan to go off the
rails, throw away the plan. Soeven with that things will go
wrong at times. It's not anypoint. It's not perfect, but you
need to make it and you need tostart executing, right? So take
those inputs, Nora down and thengo. But in reality, if you're
not thinking about thestrategic, then you're missing
(24:16):
out. And Greg, I'll actually askyou as basically, as a, you
know, a claims leader, what'simportant when we on the
technology side should bethinking about the strategic
should we be coming to you withthe ideas, should you become the
US or is there some better forumfor us to make sure we're not
missing that voice of thestrategic?
Greg Hamlin (24:33):
Yeah, I felt like that conversation has to go both
ways. But I do think you'reright. It's really important.
You don't want to have yourtechnology department, so
focused on just fixing thebrakes, that they're never ever
thinking about the strategy, andthe bigger things that could
really move the needle becauseif all we're doing is keeping
the car running, that's great,but we haven't actually come up
(24:55):
with new innovative ways to dothings better and our
competition is going to belooking for those way is to do a
better, we're all trying. So ifall our attention is just trying
to make sure that the car runs,that's great, but we're gonna
miss out on some reallyimportant things. So I think
talking back and forth andcreating that dialogue of what's
possible versus what can bedreamed up is, to me some of the
(25:18):
first steps to really coming upwith that. That's what I would
say. Anyway, Matt, thoughts onyour end?
Matthew Yehling (25:23):
Yeah, like, Sam put it. I'm a big games clear
fan, where you just have tostart something. Stop, you know,
ultimately, like, you're alwaysgreat to have the plan. But
sometimes, you have to start andsomething. Yeah, keep worrying
about that being perfect. When,because then you'll you'll never
(25:44):
get anything started. And you'regonna never never finish
anything. If it's always got tobe, you know, 100% accurate.
There's going to be littleglitches, like you said, there's
going to be little bugs here.
Having people understand likethat, you know, we're going to
do that. And then Greg hit onit, too. Like, we can't create
something that is also so bad,though, that it's great. I see
(26:04):
people and technology, peoplewant to work on new things,
right? Unfortunately, likeclaims, a lot of times it's like
we're on legacy. There's a lotof legacy stuff. There's years
and years, there's there's legalcomponents in this component
and, you know, reporting upwardcomponents. So there's all that
that needs to be factored in.
(26:26):
And sometimes that, you know,that minutia that's not
glamorous, and nobody wants towork on that stuff. You know,
it's all the fun, new thingsthat everyone wants to work on.
Right? Yes, very much. So.
Greg Hamlin (26:39):
I've heard it said that objects in motion tend to
stay in motion. So I think thereis something to getting started
and just getting moving. Yeah,Sam, I know, on our end, our
claims people, we're pretty goodat understanding a lot of
medical terminology. Now, we'realso pretty good at learning a
lot of legal because we spend alot of time in both of those
worlds. But when we start to getinto the technical side, I think
(27:01):
maybe I'm the only one but I dothink sometimes we struggle a
little bit. So what are some ofthe challenges? Or what do you
do from your perspective, tobreak down some of these
concepts and help the folks inclaims understand what it is
you're trying to accomplish?
Sam Neer (27:18):
Yeah, I think that's a great question, Greg. Because
just as a technologist cominginto insurance, it's sometimes
your kids, it's actually goingboth ways, because insurance is
not the easiest and moststraightforward at times, right?
And I had alphabet soup for myfirst like, when I first joined,
I was like, Oh my gosh, let'splay this game. Which acronym is
this? Is this the insurancecompanies acronym? Berkeley
acronym? Is this like stateacronyms is? There's two
(27:38):
acronyms for the same thing. Howdo I survive it? Right. But I
think that idea is like,hopefully things like this
podcast where you're explainingthings like in metaphors or
analogies. This is like XYZ oreven my bad analogy of my wife
giving me the honey do list,right? How do we break this down
into a shared understanding thatwe all have a checklist as well,
but try to talk a similarlanguage. And that's what
(27:59):
product management does usuallytrying to take stakeholders, and
then developers. So first ofall, talk things something out.
And if it makes no sense, try itagain. And then say, explain it
like you're explaining it to afive year old, and everyone
seems to kind of like roll theireyes like no, okay, I can try to
explain that. But in reality,amazing if you to simplify it,
and they get to a way that youunderstand it. Another thing
(28:20):
that we do in our team a lot isbasically a diagram, diagram
diagram. Big Box is like you'regoing to a whiteboard. And
again, we use a digital toolcalled Miro, there's other
versions out there. But how dowe just like there's a box here,
and it goes to this box? And letme get this process to this box.
Right. So that's how sometimeswe explain the intricacies of
API's and batch jobs and all youknow, buzzword soup, when you
(28:44):
hear again, I'm sure when peoplehear our technologists talk to
like, their eyes are rolling,what are they talking about? And
so this is why at least what oneof our goals is to under explain
what we're doing and why is Isay, let's make it stupid, come
up with bullet points, not longparagraphs and explain to it
like in a common language. Andthen if there's follow up
questions and Nepalese like,well, let's, I actually care
(29:06):
about there's that one person inthe room who's like, I want to
ask the detailed question to bethe teacher's pet. Right? At
that point, then you say, Let'ssidebar this, and let's go to
the huge deep dive sames ideas,I think on both ends. Also, when
adjusters like you said earliermatters. Like, they may just
say, I just wanted to do this,but need to break it down that
way. I think both sides need tosay, explain it simply, and then
(29:28):
have the follow up questionsafter that.
Greg Hamlin (29:30):
That's really easy to forget all of the things we
just assume we know. And I couldsay, well, we denied the TTD
because of the IMEI but we needto keep in mind CMS and somebody
who's outside this world if youjust say somebody in claims
understands every word I justsaid. Yes. And I do think it
goes both ways.
Sam Neer (29:52):
I love that Greg and I think especially because like
again, a lot of developerssometimes they're not even
having that interaction. Soagain, if you ever get a
developer on a call Have, youwill rattle that off? And
they'll just be completely likethey're like, I understand none
of it. So I'm not even engaged.
Right. So I think the goal is issaying just like, you know, I've
seen great on this podcast,people will explain what, you
know, what are these terms? Letme explain the context, even if
it's baseline, or, you know,most insurance adjusters or
(30:14):
people will know, how do wehere's it, we all understand
what we're talking about. Yes,yes, yes or no? Okay, let's
explain. 30 seconds. There wego. Now, let's move on. And I
actually finally understoodlike, it took me a while but
actually understood exactly whatyou said. So that's where us as
technologists we're gettingthere's
Matthew Yehling (30:33):
so I when I was in college, I worked in a
restaurant. And you know, mostpeople paying credit now, right
credit card. So periodically,the credit card system would go
down. We still had the whole,the old. You know, Swiper makes?
Yeah, yeah. Sam's probably tooyoung to even have seen any of
(30:54):
those. But Greg and I aren't,you know, but no one knew how to
use it. And then there'soccasionally credit cards don't
even have those raised ridgesanymore. So then you have to
write it down. And people arelike, What do you mean? Anyway?
What happens when whentechnology goes down, and it's
all hands on deck for you guys?
Sam Neer (31:11):
You just You just panic? That's what you do. You
just can't? Just like I don'tknow, it's like, I never want to
claim home. Exactly. Just likeas a shutter down for the day. I
guess no one else is doing anymore work. Right? Like those
are? Those are the good old daysis like snow days or stuff. It
looks like it's gonna stay Iguess they just shut it down.
Right? But unfortunately, wewere not in middle school
anymore, right? And I'll have toask at the end bad how you
(31:32):
survive those fun, probably theteachings and people like, you
know, customers, I'm sure lovethat and love taking time.
Right? What I will say is likeanything like any types, you've
got an emergency on claims,whether it's technology, people
forget that the techniques thatyou use, whether and you know
either on the claim side, whenthere's a fire, whether it's at
home, whether it's via whetheranything is you just want shut
out the noise and focus, right?
The problem is with technologyis sometimes it's very hard to
(31:53):
diagnose the problem. But you'llhear Hey, the system's slow or
something like that, orsomething's not working, right.
And then of course, everyonetrying to be helpful is like,
Oh, and this other thing isn'tworking and this thing, and then
we have to like, okay, there's alot of noise. How do we focus
it? You start decoupling thesethings, breaking it down here?
Oh, yeah, that thing has beenbroken for the last two years.
Okay. Thank you for that help.
(32:15):
It's not helping right now andthe panic or the fire. But I
think communication is also key.
Because when something breaks,just like you had, there's, you
know, the difference is if youput up a sign and saying, Hey,
our credit card machine isbroken, it's going to take us a
little longer to processpayments, people like oh, okay,
they know, they understand atleast I'm going into it right
you see those sometimes i Bigfast food addict. When I go in,
(32:36):
someone had put the sign upsaying we're understaffed today,
please be patient. I'm like, Oh,okay. This is why the lines
taking so long. So communicationis is also a way during those
panic moments, to try tocommunicate it there, right. And
finally, send consistentmessaging. And then, at the end,
give a wrap up or summary, wehad technology called a buzzword
retrospective where we get in aroom and again, most people have
(32:59):
retrospectives. But we get in aroom and say, Why did everything
go so badly? Right? And it'snever a fun conversation. You
don't want to be having these.
But you really need to have thetactical discipline say, Here's
everything that went wrong,summarize it, and then send it
out to your stakeholders foraccountability. I've had this in
Greg a couple of his emails lastyear. And they're not fun to
(33:20):
descend. But I'm hoping that itgives the visibility saying,
Hey, we're taking ownership forwhat did break. And here's what
we're doing to solve it in thefuture. Right. Greg, when you
get those emails for me, are youyou're not happy to see them.
And I hoping you're at leastliking that we're taking
ownership that we brokesomething, right,
Greg Hamlin (33:37):
absolutely. And I think some of the things, you
know, we don't have these typesof problems very often. But when
they have happened, what Ireally appreciate one is just
the regular communication,knowing, you know, sometimes
every half hour hour, this iswhere we're at this is what
we're working on, this is whatwe're doing to fix it, updates
are going to follow. And then atthe end, talking about this is
what we found out, this is whatwe learned, this is how it
(33:59):
happened. And this is what we'regoing to do so it doesn't happen
again. I think that's a big partof it. And luckily, we have a
company culture where we canaccept ownership. And there's
not a lot of finger pointingthat allows us to get better. So
kudos to Berkeley for developingthat. But that's a big part of
the success.
Sam Neer (34:15):
This is what I love working about this organization.
And why I think we are theleader in like our respective
insurance areas is the fact thatwe've got a great product. And
again, like I said, it doesn'thappen often. But no company is
perfect. It's how the companyresponds, and we take ownership.
And then we say how do we notmake sure this doesn't happen?
Again, a lot of places that haveworked even passcode has tried
to sweep it under the rugsaying, hey, there was no
problem or let's just cross ourfingers and toes and hope it
(34:38):
doesn't happen again. Weactually take the methodical
time to really not just figureout what went wrong but enhance
so it doesn't happen in thefuture. And that's why we're
always pushing forward which isappreciated.
Matthew Yehling (34:46):
So that's awesome. I mean, how many
problems mobile down tocommunication, right? When it's
like a like you gave the examplelike put up the sign tell
people, you know, we're movingslower today because we're
understaffed or whatever. Weknow this is down, hey, we know,
emails responding slowly, orwe're not able to get outgoing
things or in going things orwhatever. So just that
(35:08):
communication, I think it'scritical. And hopefully, this
never happens at our industry.
But that's why I had to give therestaurant analogy, right. But
yeah, when those problems godown, you know, we have the
after action review, or whateveryou wanna call and, and, yep,
and we are able to able toimprove the process. Right? Very
much. So.
Greg Hamlin (35:29):
So Sam, this is the exciting part, I think, is where
do you see technology going toimprove claims outcomes in the
future? So we've talked aboutsome of the things we're doing
now. But where do you think thisgoes in the next five years?
Yeah,
Sam Neer (35:42):
I think that's a really great question, Greg. And
that's what again, exciting wedon't always just want to be
fixing the the bad things. Wealso want to be improving and
really enhancing and making morefun for everyone involved. The
technologists, the adjusters,policyholders, there's a lot
that technology can do to reallyenhance. And so for instance,
the research firm McKinseyrecently had a report and again,
they call it what it was calledhuman in the loop. We've talked
(36:03):
a little bit about this earlier,where technology can take some
of the mundane heavy lifting,but then keep the human apprised
of the steps, they need to stopor double click or click in or
as you said earlier, find thedocuments read the document to
show me they can the human isleast tangential to that
process, right. So they don'thave to do everything, but
they're in the loop. Another oneis, again, the buzzword that was
(36:24):
really big years ago, but youstill hear it a lot is cloud
computing. And most people don'teven know what that means, or
like, how does that impact me.
But the better way to thinkabout as the daft punk song from
years ago, Harder, Better,Faster, Stronger. So cloud
computing can make systemsfaster, more reliable, have
better, you know, uptime,better, you know, just overall
better experience. And so that'sanother shift to saying, hey,
(36:44):
let's get away from the thingthat's been in the closet, the
server in the closet for thelast 50 years, don't unplug, you
see the signs, like don't everapply this. More like we don't
have to worry about someonetripping and just like, oh,
gosh, the system's down, right.
Another one is the idea ofbuying versus building. Some
larger organizations like ourshistorically have been like, we
(37:04):
got to build everythingourselves. We got to do
everything ourselves and manageeverything ourselves. But really
the ones that the great thingabout Berkeley, while we're
leading sort of this insuranceindustry is we're thinking about
what are we great at, and let'sexecute on that. And that's
augment with other organizationsthat do their best that and then
we make an ecosystem the best,right? We, at Berkeley are
plugging the piping, that wedon't have to build everything
(37:25):
from scratch, sort of like,again, you're making your
recipe, you don't have to do itthe way grandma did, where you
make every single ingredient.
From there, I can get the bestingredient from here, the best
ingredient here best and greenfrom here, and still make that
delicious dessert. And I'mmaking myself hungry at this
point. And then the last one, aswe talked about machine learning
and AI, you're getting servedwith the idea of human in the
loop. But where can we leveragethose technology areas to really
(37:48):
solve problems that we didn'teven think about? Right? Like
one of the exciting things thatwe in the technology are citing,
go to the basic question, thejob to be done of what are we
trying to do here? And where cantechnology assist? So we're able
to ask bigger and biggerquestions, it seems like every
year, but then again, we hadenough to go back to the drawing
(38:08):
board to actually do it is ityou know, regulations. But it's
exciting to having to be aninsurance tech. So like I, you
know, gets me up in the morning.
Greg Hamlin (38:17):
I agree. And when I think back on when I started my
career, you know, maybe four orfive years in some of the data
analytics was starting to, youknow, really start to get into
insurance. And everything wasabout filling out a field,
right? There's a new box everyfive minutes. Here's a new box.
Here's a new box, right? Becausewe have to capture the data. And
so as a frontline adjuster, itwas a nightmare. Because I was
(38:39):
like, well, now I've got to makesure I find the 900 boxes, and
all the data goes in the box.
But I look at where we are nowin some of the advances in
analytics and being able to justcognitively not only read notes,
but start to make inferencesabout what those things mean.
That will allow us to do thingsthat instead of having to fill
900 boxes out, we can actuallyget to that same place without
(39:02):
having to do that. Which isexciting to me to think about,
like where that goes next. Idon't even know what that looks
like, you know, 10 years fromnow. But yeah, it's very
interesting to see very much,Sam. Overall, I've really
enjoyed having you on here. Ican't end the episode without
joking. You know, our audiencewill never hear it. But in the
middle of this technologyepisode, my computer had a blue
(39:23):
screen of death and we had tostop the entire podcast because
everything locked up and shutdown. So even in those moments,
the stuff can happen. But theshow must go on. So we show must
go on. That's right. We wereluckily we have a great editor
that will make the sound amazingat the end. But I wanted to end
this season by asking each ofour guests to tell us something
(39:45):
that they're grateful for. I'mreally a big proponent of
putting good vibes in theuniverse. I feel like there's
plenty of people doing theopposite. So my, my small
contribution to the world is tomake sure I put something good
and every time I could think ofit So, for you today, Sam,
what's something you're gratefulfor?
Sam Neer (40:03):
I love this question, by the way. And again, listen to
other amazing adjusted podcasts,go shout out, please go back to
the library with there's a tonof great episodes on Spotify,
your streaming, platform ofchoice, great content there. But
I have one professional and onepersonal. So the professional
thing I'm grateful for is anamazing claims product and
analysts and dev team. So again,I get to come on this podcast
(40:24):
and tell everyone about it. Butwe've got a lot of great people
who have been working fordecades, who really get all the
credit for supporting Berkeleyindustrial comp. And, again, our
alternative markets are bamtechIt's too much of a mouthful,
even for me to say it on apersonal level. I'll call it the
five F's, right? So my faith, myfamily, football, film, and
finally fast food. Okay, sothose five really get me up in
(40:47):
the morning and make me excitedto so I'm grateful for all of
those things. And, again,grateful for this podcast and
the best cohost group productmanager could ask for. So Vicki
offer a great conversation.
Greg Hamlin (41:00):
Thanks, Sam. Really appreciate having you as a guest
today. And with that, we willwrap up this episode, and hope
you'll follow us in futureepisodes releasing every two
weeks on Monday. You can alsocatch our blog on the off weeks
that is written by our wonderfulNatalie Dangles. So again,
remind everybody to do rightthink differently and don't
forget to care. And that's itguys
Hello everybody and welcome to adjusted. I'm your
host Greg Hamlin coming at youfrom beautiful Birmingham,
Alabama and Berkeley industrialComp and with me is my co host
for the day, Matt Yehling.
Matthew Yehling (00:24):
Hello, everyone. This is Matthew
Yehling coming from St. Louis,Missouri along the banks of the
Mighty Mississippi.
Greg Hamlin (00:31):
So glad to have Matt with us today. We actually
have a special guest with us.
That is with Berkley. Actuallyit goes by the acronym BAM TECH
but Berkley alternative marketstechnology, our special guest
for the day is Sam Neer who isour group Product Manager at
Berkley alternative marketstech. So Sam, could you say
hello to everybody?
Sam Neer (00:53):
Hey, everybody, very excited to be here, Greg and
Matt. And again, I'm based outof Raleigh, North Carolina,
where it's a little bit warmer,not as warm as Birmingham,
Alabama right now, but it'sstill there. And I will say no
one wants to say Berkeleyalternative markets technology
over and over. So we might justcall it BAM tech just for all of
our sanity if that's okay.
Greg Hamlin (01:12):
I think that's wise. I think that's why it's so
the topic today, this issomething we've never really
tackled in the nearly threeyears that I've been doing
adjusted is claims andtechnology, and the role that it
plays in helping us do our jobs.
So I wanted to bring Sam intoday to talk to us a little bit
about that since he is part ofthe Berkley company that manages
(01:32):
our technology to help us besuccessful. Before we dive deep
into the topic. Sam, I wanted toask you a little bit about
yourself. So I'm sure that youknew you were going to be in
workers compensation, right?
Sam Neer (01:48):
Greg knew it is again, coming up as a small boy, I
would say to my dad with a glintin my eye one day dad, I come to
work and workers compensationand a quippy line I'll have is
either the way I ended up in theindustry is either I found my
way into making great technologyproducts with great
organizations and reallyeffecting change, or I follow
the goal of becoming the moststable, uninteresting version of
(02:10):
myself one of the two. Andagain, maybe, maybe it's a
little bit of column D.
Greg Hamlin (02:16):
there's a lot of stability in worker's
compensation insurance. Sothat's the one positive right.
Sam Neer (02:21):
That's it's something to be accounted for. So with the
32nd background, again, I'vebeen working in digital
technology for the last 12 yearsand product management, started
off in PR and analytics thenmoved to telehealth right at the
height of the pandemic would notrecommend trying to jump into
telehealth at that time, andhave only been in sort of the
insurance industry for you know,for a couple years now. But at
the same time, it really just abaptism by fire. But I've
(02:43):
learned a lot. And I thinkinsurance is a great area,
insurer tech, that's having alot of new frontier, a lot of
legacy systems upgrading. Soit's a great time to be alive.
And again, my specialization isclaims I'm beginning to dabble a
bit more on the policy andunderwriting side. But the
trends are out there that arehappening impacting all of us,
and really excited to talk thosethrough. That's awesome.
Greg Hamlin (03:04):
Well, I know the headlines right now, when you
hear about technology, you hearabout the mass layoffs of
companies like Twitter, andFacebook and everybody else,
Google. So there really issomething to what you know, as
you were joking, there's somecivility that comes with
insurance and worker'scompensation, it might not be as
glamorous. That's definitely notthe Silicon Valley dream, right.
(03:26):
But there's definitelyopportunities for stability.
Sam Neer (03:28):
And glamour is in the eye of the beholder, right. And
I will say that the impact andthe scale of which we're being
able to operate here at Berkleyis enormous, right? And so
that's what I do enjoy that. Andyou getting to build technology
that powers really a lot of whathappens in the workplace is
really awesome. So I'm thrilled.
Okay, I tell my wife, I'm thecoolest guy ever. And she
doesn't believe me yet, butshe'll get there soon.
Matthew Yehling (03:50):
So when we talk about technology and claims, you
know, claims is been around fora while, what do we mean? What's
the role of technology inclaims?
Unknown (04:01):
I think that's a great question. Again, I'm gonna start
off with the buzzword, becauseeveryone needs a cool buzzword
to begin, but I'm gonna say thetechnology and claims the word
of the day is augmentation.
Right? So with this, as youknow, the claim experience is a
very human one, right? You're inthe moment you're in the
accident, how are you basicallygetting through that process?
And that's why at least how Iview this is how can technology
(04:22):
assist in that process toaugment the adjuster and the
humans who are very muchinteracting with that claimant
at that moment, and thatexperience, and also with the
policyholders, and also withagents again, there's a whole
spectrum right here. But withthat, as some people just tried
to say, can we automate it? Canyou just talk to a computer? Can
you never interact? And yes,there's going to be the small
paper cuts or the reportaltogether, there's going to be
(04:43):
some subset that can beautomated. But the way that I've
been viewing against leaves mytime here at Berkley is how can
we use the best of technology toaugment the best of humans to
really attack these problems? Sothat's where I believe that most
places will say just throw Ourcomputer or machine learning
model at it. But in reality, howcan we leverage technology to
(05:03):
leverage humans is the way thatleast I've been trying to
approach it.
Matthew Yehling (05:08):
Can you give an example? You know, a recent
example maybe of how you'reusing technology to augment
something in the claimsindustry?
Unknown (05:17):
Way too many Matt and again, this is what keeps me on
my toes, right. So with that,for instance, sometimes again,
we will use machine learning orwhat some people call AI. So ML
is the buzzword machinelearning, which is sometimes
people call it AI. It's allthese different cool terms,
right, just saying, hey, the,you know, Skynet is doing
something in the background. Sofrom there, we at least some of
(05:40):
our Berkley time will say, hey,when a new claim comes in, based
off the first report of injury,how do we get some of these
early details to identifywhether it's going to be a large
scale claim, or just, you know,just the paper cut, right. And
again, when it's the smallerones, we're saying, hey,
adjuster doesn't need to do 17of the normal steps, again, all
the regulatory and must haverequirements we're always doing
(06:00):
to meet the laws of the land,but some of the stuff of the
process all the follow ups, wecan reduce some of that, on that
when the machine learning or AIor Skynet identifies and you
don't need to do everything onthis one, right, and we don't
get it 100%, but directly thatsave some time. So just do the
human steps, the augmentationwhere they need the plug in and
reduce some of that early. Sothat's a good example where
(06:23):
we're saying, hey, technologywill identify some things that
reduce some of those steps, butwe still have the human doing
the must have steps in thatprocess.
Greg Hamlin (06:31):
I think it's really key one of the things you're
highlighting there, and thatwe're not in a state where we're
looking for technology toreplace human beings. But if we
could redeploy some of thoseefforts, so we have time to do
the things humans are reallygood at that's, that's one of
the things that can make usspecial if we allow computers to
help us and technology to helpus improve the processes. Sam,
(06:53):
what are some of themisconceptions? You see, when
you start to hear about howtechnology can improve claim
processes? You're dealing with alot of claims, people who
probably don't all have highlevels of technology background?
And so what are some of thosemisconceptions you sometimes
hear where there's some, youknow, maybe they don't align
with what's actually whatactually can be done? Well,
yeah,
Sam Neer (07:13):
I think that's a great question, Greg. And, but it will
say it's, there's two ends ofthat spectrum of those
misconceptions, right. One isthat technology, hey, anything
that I don't like doing rightnow, technology can solve it can
solve world hunger, just with asingle flitch. Sam, here's the
idea. Just go make it happen.
wave your magic technology wand.
And you know, it's just like, myparents, when I go home for
(07:33):
Christmas think that i isbecause I work in technology can
solve all of their routerproblems and printer problems.
Some people think that, hey,technology can solve anything
you want. They're just like theprinters, the TV's broken, can
you fix the Roku? Right? It'slike, I can't do everything,
folks. Okay, so that's one endof the spectrum. And the other
one is that misconceptions isthat technology can't do some of
(07:56):
the cool things that humanshistorically could do. Right?
And some of those, which whenyou actually talk about it makes
sense. Like, hey, we wanttechnology to catch mistakes or
errors, right? Oh, yeah, ofcourse, I would like that. Or
could it reduce mundane,repeatable tasks, so you're
doing like think work, and notjust, you know, just click work,
right. But the two ends of thosespectrums are always fun, and
(08:18):
especially insurance, some tendsto gravitate towards, oh,
technology isn't gonna be ableto do anything. So that's the
fun conversation. And I'llactually flip the question back
to you. How have you all seen?
Like, what are some of themisconceptions you found? We're
working in this industry longerthan I have that, you know, that
come up a lot as well? Is thereany different ones? Besides from
those two extremes?
Greg Hamlin (08:38):
All right. Well, I was just gonna say the one I've
seen the most, in my experiencehas been that often technology
can do things, but what's notbeing calculated as the amount
of time and effort and resourcesthat would have to be deployed
to get there. And so often, Ifeel like adjusters managers,
(08:58):
supervisors have the want list,and the want list might save
them five minutes, but it mighttake 10 years to accomplish it
today. So then theprioritization and becomes the
issue. It's not always that itcan't happen. It probably could.
But is it really worth havingthat happen for what it's going
to cost?
Matthew Yehling (09:19):
Yeah, my example would be the first
notice of loss and automation ofthat, and triggering, you know,
maybe keywords, you know, giventhe example of highlighting, you
know, more serious claim, so youtrigger in on words like
amputation, traumatic braininjury, and those go to a more
experienced high level claimsperson versus you have something
(09:42):
where it's like contusionlaceration, and those things get
assigned to a newer person orsomebody, maybe that's less
experienced. I mean, I've seensome examples of that through
automation and augmentation, Iguess. You know, the word we're
used And so that would be anexample I've seen of where it's
(10:02):
been applied in, in the industryversus having it go basically,
across the spectrum. A new claimcomes in and always goes to
Sandra, because Sandra reviewsevery new claim, it's like,
well, you know, if we canidentify, you know, things, and
you might, you know, identify80% of the stuff, and then
Sandra just gets 20%. And thenthat time that Sandra is freed
(10:25):
up, is now able to go in and dosome other tasks versus all
she's doing is reviewing newlosses coming in. That's a
recent example I've seen.
Sam Neer (10:35):
I love that example of that. Because I will say it's
like, it's a combination ofrouting rules saying, hey, and
we picking out words, but thisis where technology like we say
can assist is sometimes when youhave to write the exact if then
rules and then you forgot aderivation, right? Some people
call things very similarly,there's also this idea in
technology called stemming, or,you know, he's like do plurals
or ing or things like that,right. And that's where you can
(10:56):
say, hey, technology, withnatural language processing,
called NLP, come up with allvariations of this type of word,
or even recommend words. Andthat's what it's getting crazy
right now. It's like, Yeah, I'lljust recommend these 20 other
words, should these route tothis type of adjuster who's
going to handle this type ofcatastrophic claim, for
instance, or this cat claim? Sothat's where it can use the
combination of an if then ruleset this kind of basic, but it's
(11:18):
good and technology, but thencan technology, figure out a few
more examples around that, tohelp escalate that? I love that
example. And again, it's onewe're even looking Yeah, we
utilize here to some degree, andhow can we expand and enhance
that as well?
Matthew Yehling (11:32):
And it's small things, and then you can
continue to build on them too,right? Yeah, it's nice to have
the home run. But every time youknow, it's a lot of times it's a
little singles, and how do yougain little efficiencies, the
1%? Better improvement, and thenthat becomes exponential
improvement over time, right? Sonow has, you know, 80% of her
time freed up to do moremeaningful other tasks? You
(11:54):
know, we're not trying toeliminate her role. It's we're
trying to elevate her role intolike, okay, let's, you know,
you're you're better utilized inthis capacity. versus, you know,
breeding. Yeah, I mean, that'sanother example. I mean, the
difficulty I work with a lot ofattorneys and nurses, you know,
the difficulty here, I get alittle pushback from them, as
I'm in the show me state. I'm inSt. Louis, I'm in Missouri. They
(12:16):
want to see everything. Sothat's some of the difficulties
I experienced on the kind of thereverse. I'm like, no, let me
review the document, give me thedocument. And
Sam Neer (12:25):
one of the fun things about the changes in technology
is sometimes under the hood, itis not easy to decouple with
machine learning and AI andinsert buzzwords, and you know,
joke Skynet from the Terminatorback in the day, right? But to
that end is, it can't alwaysshow you that way. So that's
where sometimes again, the ideaof a buzzword back again,
augmentation where you could saythe computer, or ml or AI
(12:47):
recommended this, do you want totake action on it? So it's not
basically making the choice allthe way, but it's allowing those
to say, hey, oh, I want todouble look at the document the
Show Me side, I want to Okay,let me just double check to make
sure the computer got it right.
And if it does that 100 out of100 times, or 99 out of 100.
Maybe you start trusting it. Sothat's the idea is you don't
like you said we all want tousing a bad sports analogy. We
(13:08):
all like seeing the long pastbombed Randy Moss at the end,
you know, with a touchdown, Idon't know why I'm going to the
Vikings in the 90s. But, but inreality, the teams that just
chunk it forward to like fiveyards at a time, five yards at a
time, the small incrementalgains can eventually really get
you there. And it's not asglamorous as the star play. But
that's really what's gonna movethe needle in the long run. So
okay, so
Greg Hamlin (13:31):
one of the questions I had Sam is what is
technology good at in the claimspace?
Unknown (13:36):
Yeah.
Sam Neer (13:37):
And that's where I think, you know, the unglamorous
sides of technology is thepiping right? Technology is a
good thoroughfare of informationconnecting different systems,
facilitating seamless and easycommunication interactions, a
couple of ideas that come tomind that, especially in the
claim space, especially as we asadjusters need to be more easily
contactable or basicallyreaching out to claimants,
(13:58):
things like digital payments,don't go send my old school
check to my grandma, like mygrandma did. Can I get something
in? You know, Venmo? Right.
We've heard that or texting,right? Like which kid under the
age of 25 wants to have a phonecall they dread it that's like,
is that a punishment? Right? AmI being grounded? Because I have
to answer the phone. Soeverything from the piping to
also thinking about where canidentify patterns, right?
(14:19):
technology could say I'm seeinga lot of this problems. Let me
just take this as a problem.
What should we do with it? So itcan't always say the what to do
with it. That's the milliondollar question. For instance,
one of our partners at a similarcompany Midwesterners employers
casualties have this idea ofidentifying problematic claim
notes, hey, we've seen a bunchof these claim notes. This one
(14:40):
is flagged for attention. Oranother operating unit at
Berkeley is also saying we'veseen claims like this, you might
want to go check those out. Sothese are the ways that
technology can find thosepatterns to then surface it and
then at that point, the adjusteror the human can take it there.
And so again, the reticence isSometimes that technology is not
(15:01):
as good as humans andeverything, it's like, well,
that's majority clue we trulyhave a brain, we have the
ability to think. But it canalso look at 10 million claim
notes, and come up with somegroupings of problems and say,
Hey, I've seen similar ones thata human would never be able to
do. And so and then datavalidation, and anything that we
said earlier, it's not thinktime, it's just checking a box.
(15:22):
Because computers, they do makeerrors. Trust me, I've got a
backlog full of bugs to try totackle. But at the same time,
it's much more consistent inhumans at that part, but trying
to ask your computer or eventhis newfangled chat GPT, I
tried to you know, that's thebuzzword nowadays, I tried to
ask it to write a haiku aboutclaims adjustment, and it failed
miserably. So anything oncerespective, but it did actually
(15:45):
have a good song two claims.
This is one aside about claims,I asked it to do song about
claims adjustments to the tuneof Baby Got Back. And it did
says I like small claims, and Icannot lie and it actually went
there. So it started. It'sgetting closer. But the huge
nuance, you know, is not there'swhere it needs to be.
Matthew Yehling (16:02):
Great points.
Yeah. So when you're workingwith obviously, individual
adjusters, you're working withinjured employees, you're
working with differentcomponents? What are some of the
challenges and managing thoseexpectations of the claim staff?
When it comes to technology?
What comes to, you know,enhancements and things like,
(16:22):
you know, Greg alluded toearlier, like, and you alluded
to when you're like, fix all mystuff, like what? What are some
of the, those expectations likehow do you how do you normalize
those?
Sam Neer (16:34):
That's great. And that is like, it's the idea that I
want it all. And I want it now,right? Like that's the the
problem that we're dealing withright here is like, there's so
much that we want to fix. Greg,you made a great point earlier
saying, Hey, we don't alwaysnecessarily know the size, this
will save me five minutes, butit's going to take Sam six
months to build. Right. So thislike some of the misconceptions
(16:54):
that I wanted, when thinkingabout this question, it's a
really good question. Is thatone you hear about it elsewhere?
Well, we can do it right away.
Hey, I've heard about my friendwho works at similar insurance
company, they have this coolbutton that you know, brews and
coffee and does XYZ for my claimprocess. Why can't I do that?
Right? So misconception ispeople don't forget out all the
piping all the underlyingarchitecture that goes up to
make that right. Another is likethe response times, like we
(17:16):
think that everything in everysystem is supposed to be
blistering fast, right, whichshould be like my iPhone starts
up in a second, I can't wait asecond, give something pauses,
even briefly, we're like what'shappening. So the challenge is,
again, these are misconceptionsthat are out there. So the
challenge is trying to not justaddress them, but then overcome
them. And that's where again,product management comes in my
(17:38):
entire profession is to say is,hey, we understand that your
iPhone can boot up in a second.
And we understand that yourfriend can do XYZ at this other
carrier, here's how we'redifferent. Let's work within our
sandbox, and let's createamazing things within there. So
it's really us trying toovercome those by doing the
quick explanation, because itcan't just like not, we can't do
it, then you're just feel likeeither you're being talked down
(18:00):
to. But at the same time, ourrole is in product management,
and even within technology is toexplain what we can do. And then
we partner with again, GreatPlains groups who are coming up
with amazing ideas to figure outwhat we can do. I'll actually
turn this question back to youtoo, and saying, you've seen a
lot more of these mismanagedexpectations over time. And I
know, Greg, you said earlier asa mismanaged expectation of
(18:22):
sizing, what do you think ontechnology, technology teams can
do better to help in thatconversation? Like what is we as
technologists, what do we needto learn, when actually engaging
in that back and forth?
Greg Hamlin (18:34):
Great question, I think, on are end, sometimes we
don't understand how hardcertain things are not maybe how
difficult but how many hourswould be needed to accomplish
something. And so that wish listwe've talked about can get
really long, because we can allthink of things that would make
things easier. Well, if I justclicked here, then I wouldn't
(18:55):
have to do these five otherthings. So I think sometimes
understanding also what otherthings are being worked on that
are competing for that sametime. So we could spend five
weeks adding a new button to thescreen. Or we could do this
other thing that would allow usto automate small claims. And so
(19:16):
understanding the impact, Ithink, on both sides, sometimes
maybe the technology side mightnot understand the lift that
would come from something. Andthen that's our job to make sure
we're speaking up to explainthat. And then on the other end,
I think understanding how longit takes so that we're
prioritizing the right things. Idon't know what your thoughts
are, Matt?
Matthew Yehling (19:37):
Yeah, something similar. I think spending enough
time with the people doing thejob is a lot of times the
difficulty and making sure likewhen I say I want this, like
that five button example or likeI have to click on these eight
things to have this happen.
Instead of these eight things,like would it make sense to
click on it once and then itjumps to eight. What is that? Up
(19:57):
to 234567 doing? Are they doinganything like? Just, you know,
sometimes maybe it's the, youknow, the Data Architect, or
maybe it's the person that'sactually, you know, the business
analyst. Do they understand? Andthen sometimes the claims people
really don't understand well,the reasons you have to click on
(20:17):
steps three, seven, are thislike that triggers this piping
like that, like Sam was saying,like, I think there's definitely
roles on both sides. Like, thereason the phrase that everybody
hates is like, Well, the reasonwe do it that way, is the reason
why it's done that way. Right?
(20:39):
Yeah, yeah. Because an x rayover come over and hey, can you
just cut out this process?
Sam Neer (20:46):
Love those phone calls, we love this one goes.
And I'll actually add somethingout there. I think it's great to
say as we as technologists needto get better at actually going
and sitting down next to theperson saying, walk me through
instead of just saying, I wantXYZ at the eighth step, like
skipping steps two throughseven. Let me see why you don't
need those. And then again,that's where the dialogue can
occur, right? Hey, step numberthree. And I need to give a
(21:08):
shout out to my amazing team whothinks through all these things.
So again, we have a team calledclaim to fame where we really
focus on claims shout out tothat group, what they'll say is,
Hey, you forgot about regulatoryreporting here, or you forgot
about NCCI will have thesedifferent plugins of different
steps, we may need it. But thatdoesn't mean we can't, we may
need steps now. One and two, butwe may not need 345 and six,
right? So I think it's a goodback and forth to be able to
(21:30):
explain but too many times, wejust hear I want to go jump from
one to eight, we write it off astechnologists can't do it. And
then that dies, and you losethat potential savings over
time. So rolling up the sleevesand getting in the trenches
important, it just just takestime. Right.
Matthew Yehling (21:45):
So how would you tell Greg and I are both in
claims management. And we manageyou know, the claims adjusters
and claims analyst andexaminer's etc. So, you know, we
have in our prioritizations thatare important to us, you guys
obviously have prioritizationsthat are important to it and to
technology. So what's the bestway to manage that those
(22:09):
prioritizations in the limitedresources? Because, like the
example Greg gave earlier, howdo we manage something, when
there's only a limited resource,you know, IT resources and
technology resources?
Unfortunately, we've mentioned,you know, Google earlier, like,
we don't have Google money,we're not gonna like, you know,
how do we manage thoseprioritizations?
Sam Neer (22:31):
Oh, this, these are the questions that make us happy
that we're asking thesequestions, right? Well, the
there's a simple answer,actually, man, Greg, I will tell
you the way we prioritize hisliquor and guessing I wish it
was that easy, or just adartboard. It's like, close your
eyes. And that's no, Icompletely joke. But I think a
better way of like, for lack ofa letter buzz phrase, it's
basically like, It's the voiceof the tactical combined with
(22:52):
the insight of the strategic,right. So with that, as we need
to do, anytime you ignore us, orthe people who are living this
day in and day out, we talkedabout that dialogue earlier,
then you're not going to get thethings that will actually impact
people in the day to day. On theflip side, for all you're
focusing on some details in theweeds, you will miss the bigger
strategic opportunities outthere. So the first thing we
need to do in order toprioritize the chaos, that is a
(23:12):
huge backlog and seems like 15,new things have popped up in the
last, you know, 30 minutes,since we've been talking is
combining those two, get thosetwo inputs. And then after that,
you need to basically thennarrow it down quickly, because
there's always way too much thatyou want to do. Just like my
wife has 15,000 home projectsshe wants me to do. We can't
talk about 15,000 projects, weneed to talk about five, right?
(23:34):
So first is get the inputs, doyour homework, again, with
targeted homework, you can'tboil the ocean, narrow it down,
and then pick a direction andgo. I think, you know, it's sort
of the idea that a lot of placeshave analysis paralysis. But
again, there's a show calledPrison branco TV years ago,
where the guy Wentworth Miller,who played one of the characters
there had said, There's only forfour roles you need to remember,
(23:56):
make a plan, execute the plan,expect the plan to go off the
rails, throw away the plan. Soeven with that things will go
wrong at times. It's not anypoint. It's not perfect, but you
need to make it and you need tostart executing, right? So take
those inputs, Nora down and thengo. But in reality, if you're
not thinking about thestrategic, then you're missing
(24:16):
out. And Greg, I'll actually askyou as basically, as a, you
know, a claims leader, what'simportant when we on the
technology side should bethinking about the strategic
should we be coming to you withthe ideas, should you become the
US or is there some better forumfor us to make sure we're not
missing that voice of thestrategic?
Greg Hamlin (24:33):
Yeah, I felt like that conversation has to go both
ways. But I do think you'reright. It's really important.
You don't want to have yourtechnology department, so
focused on just fixing thebrakes, that they're never ever
thinking about the strategy, andthe bigger things that could
really move the needle becauseif all we're doing is keeping
the car running, that's great,but we haven't actually come up
(24:55):
with new innovative ways to dothings better and our
competition is going to belooking for those way is to do a
better, we're all trying. So ifall our attention is just trying
to make sure that the car runs,that's great, but we're gonna
miss out on some reallyimportant things. So I think
talking back and forth andcreating that dialogue of what's
possible versus what can bedreamed up is, to me some of the
(25:18):
first steps to really coming upwith that. That's what I would
say. Anyway, Matt, thoughts onyour end?
Matthew Yehling (25:23):
Yeah, like, Sam put it. I'm a big games clear
fan, where you just have tostart something. Stop, you know,
ultimately, like, you're alwaysgreat to have the plan. But
sometimes, you have to start andsomething. Yeah, keep worrying
about that being perfect. When,because then you'll you'll never
(25:44):
get anything started. And you'regonna never never finish
anything. If it's always got tobe, you know, 100% accurate.
There's going to be littleglitches, like you said, there's
going to be little bugs here.
Having people understand likethat, you know, we're going to
do that. And then Greg hit onit, too. Like, we can't create
something that is also so bad,though, that it's great. I see
(26:04):
people and technology, peoplewant to work on new things,
right? Unfortunately, likeclaims, a lot of times it's like
we're on legacy. There's a lotof legacy stuff. There's years
and years, there's there's legalcomponents in this component
and, you know, reporting upwardcomponents. So there's all that
that needs to be factored in.
(26:26):
And sometimes that, you know,that minutia that's not
glamorous, and nobody wants towork on that stuff. You know,
it's all the fun, new thingsthat everyone wants to work on.
Right? Yes, very much. So.
Greg Hamlin (26:39):
I've heard it said that objects in motion tend to
stay in motion. So I think thereis something to getting started
and just getting moving. Yeah,Sam, I know, on our end, our
claims people, we're pretty goodat understanding a lot of
medical terminology. Now, we'realso pretty good at learning a
lot of legal because we spend alot of time in both of those
worlds. But when we start to getinto the technical side, I think
(27:01):
maybe I'm the only one but I dothink sometimes we struggle a
little bit. So what are some ofthe challenges? Or what do you
do from your perspective, tobreak down some of these
concepts and help the folks inclaims understand what it is
you're trying to accomplish?
Sam Neer (27:18):
Yeah, I think that's a great question, Greg. Because
just as a technologist cominginto insurance, it's sometimes
your kids, it's actually goingboth ways, because insurance is
not the easiest and moststraightforward at times, right?
And I had alphabet soup for myfirst like, when I first joined,
I was like, Oh my gosh, let'splay this game. Which acronym is
this? Is this the insurancecompanies acronym? Berkeley
acronym? Is this like stateacronyms is? There's two
(27:38):
acronyms for the same thing. Howdo I survive it? Right. But I
think that idea is like,hopefully things like this
podcast where you're explainingthings like in metaphors or
analogies. This is like XYZ oreven my bad analogy of my wife
giving me the honey do list,right? How do we break this down
into a shared understanding thatwe all have a checklist as well,
but try to talk a similarlanguage. And that's what
(27:59):
product management does usuallytrying to take stakeholders, and
then developers. So first ofall, talk things something out.
And if it makes no sense, try itagain. And then say, explain it
like you're explaining it to afive year old, and everyone
seems to kind of like roll theireyes like no, okay, I can try to
explain that. But in reality,amazing if you to simplify it,
and they get to a way that youunderstand it. Another thing
(28:20):
that we do in our team a lot isbasically a diagram, diagram
diagram. Big Box is like you'regoing to a whiteboard. And
again, we use a digital toolcalled Miro, there's other
versions out there. But how dowe just like there's a box here,
and it goes to this box? And letme get this process to this box.
Right. So that's how sometimeswe explain the intricacies of
API's and batch jobs and all youknow, buzzword soup, when you
(28:44):
hear again, I'm sure when peoplehear our technologists talk to
like, their eyes are rolling,what are they talking about? And
so this is why at least what oneof our goals is to under explain
what we're doing and why is Isay, let's make it stupid, come
up with bullet points, not longparagraphs and explain to it
like in a common language. Andthen if there's follow up
questions and Nepalese like,well, let's, I actually care
(29:06):
about there's that one person inthe room who's like, I want to
ask the detailed question to bethe teacher's pet. Right? At
that point, then you say, Let'ssidebar this, and let's go to
the huge deep dive sames ideas,I think on both ends. Also, when
adjusters like you said earliermatters. Like, they may just
say, I just wanted to do this,but need to break it down that
way. I think both sides need tosay, explain it simply, and then
(29:28):
have the follow up questionsafter that.
Greg Hamlin (29:30):
That's really easy to forget all of the things we
just assume we know. And I couldsay, well, we denied the TTD
because of the IMEI but we needto keep in mind CMS and somebody
who's outside this world if youjust say somebody in claims
understands every word I justsaid. Yes. And I do think it
goes both ways.
Sam Neer (29:52):
I love that Greg and I think especially because like
again, a lot of developerssometimes they're not even
having that interaction. Soagain, if you ever get a
developer on a call Have, youwill rattle that off? And
they'll just be completely likethey're like, I understand none
of it. So I'm not even engaged.
Right. So I think the goal is issaying just like, you know, I've
seen great on this podcast,people will explain what, you
know, what are these terms? Letme explain the context, even if
it's baseline, or, you know,most insurance adjusters or
(30:14):
people will know, how do wehere's it, we all understand
what we're talking about. Yes,yes, yes or no? Okay, let's
explain. 30 seconds. There wego. Now, let's move on. And I
actually finally understoodlike, it took me a while but
actually understood exactly whatyou said. So that's where us as
technologists we're gettingthere's
Matthew Yehling (30:33):
so I when I was in college, I worked in a
restaurant. And you know, mostpeople paying credit now, right
credit card. So periodically,the credit card system would go
down. We still had the whole,the old. You know, Swiper makes?
Yeah, yeah. Sam's probably tooyoung to even have seen any of
(30:54):
those. But Greg and I aren't,you know, but no one knew how to
use it. And then there'soccasionally credit cards don't
even have those raised ridgesanymore. So then you have to
write it down. And people arelike, What do you mean? Anyway?
What happens when whentechnology goes down, and it's
all hands on deck for you guys?
Sam Neer (31:11):
You just You just panic? That's what you do. You
just can't? Just like I don'tknow, it's like, I never want to
claim home. Exactly. Just likeas a shutter down for the day. I
guess no one else is doing anymore work. Right? Like those
are? Those are the good old daysis like snow days or stuff. It
looks like it's gonna stay Iguess they just shut it down.
Right? But unfortunately, wewere not in middle school
anymore, right? And I'll have toask at the end bad how you
(31:32):
survive those fun, probably theteachings and people like, you
know, customers, I'm sure lovethat and love taking time.
Right? What I will say is likeanything like any types, you've
got an emergency on claims,whether it's technology, people
forget that the techniques thatyou use, whether and you know
either on the claim side, whenthere's a fire, whether it's at
home, whether it's via whetheranything is you just want shut
out the noise and focus, right?
The problem is with technologyis sometimes it's very hard to
(31:53):
diagnose the problem. But you'llhear Hey, the system's slow or
something like that, orsomething's not working, right.
And then of course, everyonetrying to be helpful is like,
Oh, and this other thing isn'tworking and this thing, and then
we have to like, okay, there's alot of noise. How do we focus
it? You start decoupling thesethings, breaking it down here?
Oh, yeah, that thing has beenbroken for the last two years.
Okay. Thank you for that help.
(32:15):
It's not helping right now andthe panic or the fire. But I
think communication is also key.
Because when something breaks,just like you had, there's, you
know, the difference is if youput up a sign and saying, Hey,
our credit card machine isbroken, it's going to take us a
little longer to processpayments, people like oh, okay,
they know, they understand atleast I'm going into it right
you see those sometimes i Bigfast food addict. When I go in,
(32:36):
someone had put the sign upsaying we're understaffed today,
please be patient. I'm like, Oh,okay. This is why the lines
taking so long. So communicationis is also a way during those
panic moments, to try tocommunicate it there, right. And
finally, send consistentmessaging. And then, at the end,
give a wrap up or summary, wehad technology called a buzzword
retrospective where we get in aroom and again, most people have
(32:59):
retrospectives. But we get in aroom and say, Why did everything
go so badly? Right? And it'snever a fun conversation. You
don't want to be having these.
But you really need to have thetactical discipline say, Here's
everything that went wrong,summarize it, and then send it
out to your stakeholders foraccountability. I've had this in
Greg a couple of his emails lastyear. And they're not fun to
(33:20):
descend. But I'm hoping that itgives the visibility saying,
Hey, we're taking ownership forwhat did break. And here's what
we're doing to solve it in thefuture. Right. Greg, when you
get those emails for me, are youyou're not happy to see them.
And I hoping you're at leastliking that we're taking
ownership that we brokesomething, right,
Greg Hamlin (33:37):
absolutely. And I think some of the things, you
know, we don't have these typesof problems very often. But when
they have happened, what Ireally appreciate one is just
the regular communication,knowing, you know, sometimes
every half hour hour, this iswhere we're at this is what
we're working on, this is whatwe're doing to fix it, updates
are going to follow. And then atthe end, talking about this is
what we found out, this is whatwe learned, this is how it
(33:59):
happened. And this is what we'regoing to do so it doesn't happen
again. I think that's a big partof it. And luckily, we have a
company culture where we canaccept ownership. And there's
not a lot of finger pointingthat allows us to get better. So
kudos to Berkeley for developingthat. But that's a big part of
the success.
Sam Neer (34:15):
This is what I love working about this organization.
And why I think we are theleader in like our respective
insurance areas is the fact thatwe've got a great product. And
again, like I said, it doesn'thappen often. But no company is
perfect. It's how the companyresponds, and we take ownership.
And then we say how do we notmake sure this doesn't happen?
Again, a lot of places that haveworked even passcode has tried
to sweep it under the rugsaying, hey, there was no
problem or let's just cross ourfingers and toes and hope it
(34:38):
doesn't happen again. Weactually take the methodical
time to really not just figureout what went wrong but enhance
so it doesn't happen in thefuture. And that's why we're
always pushing forward which isappreciated.
Matthew Yehling (34:46):
So that's awesome. I mean, how many
problems mobile down tocommunication, right? When it's
like a like you gave the examplelike put up the sign tell
people, you know, we're movingslower today because we're
understaffed or whatever. Weknow this is down, hey, we know,
emails responding slowly, orwe're not able to get outgoing
things or in going things orwhatever. So just that
(35:08):
communication, I think it'scritical. And hopefully, this
never happens at our industry.
But that's why I had to give therestaurant analogy, right. But
yeah, when those problems godown, you know, we have the
after action review, or whateveryou wanna call and, and, yep,
and we are able to able toimprove the process. Right? Very
much. So.
Greg Hamlin (35:29):
So Sam, this is the exciting part, I think, is where
do you see technology going toimprove claims outcomes in the
future? So we've talked aboutsome of the things we're doing
now. But where do you think thisgoes in the next five years?
Yeah,
Sam Neer (35:42):
I think that's a really great question, Greg. And
that's what again, exciting wedon't always just want to be
fixing the the bad things. Wealso want to be improving and
really enhancing and making morefun for everyone involved. The
technologists, the adjusters,policyholders, there's a lot
that technology can do to reallyenhance. And so for instance,
the research firm McKinseyrecently had a report and again,
they call it what it was calledhuman in the loop. We've talked
(36:03):
a little bit about this earlier,where technology can take some
of the mundane heavy lifting,but then keep the human apprised
of the steps, they need to stopor double click or click in or
as you said earlier, find thedocuments read the document to
show me they can the human isleast tangential to that
process, right. So they don'thave to do everything, but
they're in the loop. Another oneis, again, the buzzword that was
(36:24):
really big years ago, but youstill hear it a lot is cloud
computing. And most people don'teven know what that means, or
like, how does that impact me.
But the better way to thinkabout as the daft punk song from
years ago, Harder, Better,Faster, Stronger. So cloud
computing can make systemsfaster, more reliable, have
better, you know, uptime,better, you know, just overall
better experience. And so that'sanother shift to saying, hey,
(36:44):
let's get away from the thingthat's been in the closet, the
server in the closet for thelast 50 years, don't unplug, you
see the signs, like don't everapply this. More like we don't
have to worry about someonetripping and just like, oh,
gosh, the system's down, right.
Another one is the idea ofbuying versus building. Some
larger organizations like ourshistorically have been like, we
(37:04):
got to build everythingourselves. We got to do
everything ourselves and manageeverything ourselves. But really
the ones that the great thingabout Berkeley, while we're
leading sort of this insuranceindustry is we're thinking about
what are we great at, and let'sexecute on that. And that's
augment with other organizationsthat do their best that and then
we make an ecosystem the best,right? We, at Berkeley are
plugging the piping, that wedon't have to build everything
(37:25):
from scratch, sort of like,again, you're making your
recipe, you don't have to do itthe way grandma did, where you
make every single ingredient.
From there, I can get the bestingredient from here, the best
ingredient here best and greenfrom here, and still make that
delicious dessert. And I'mmaking myself hungry at this
point. And then the last one, aswe talked about machine learning
and AI, you're getting servedwith the idea of human in the
loop. But where can we leveragethose technology areas to really
(37:48):
solve problems that we didn'teven think about? Right? Like
one of the exciting things thatwe in the technology are citing,
go to the basic question, thejob to be done of what are we
trying to do here? And where cantechnology assist? So we're able
to ask bigger and biggerquestions, it seems like every
year, but then again, we hadenough to go back to the drawing
(38:08):
board to actually do it is ityou know, regulations. But it's
exciting to having to be aninsurance tech. So like I, you
know, gets me up in the morning.
Greg Hamlin (38:17):
I agree. And when I think back on when I started my
career, you know, maybe four orfive years in some of the data
analytics was starting to, youknow, really start to get into
insurance. And everything wasabout filling out a field,
right? There's a new box everyfive minutes. Here's a new box.
Here's a new box, right? Becausewe have to capture the data. And
so as a frontline adjuster, itwas a nightmare. Because I was
(38:39):
like, well, now I've got to makesure I find the 900 boxes, and
all the data goes in the box.
But I look at where we are nowin some of the advances in
analytics and being able to justcognitively not only read notes,
but start to make inferencesabout what those things mean.
That will allow us to do thingsthat instead of having to fill
900 boxes out, we can actuallyget to that same place without
(39:02):
having to do that. Which isexciting to me to think about,
like where that goes next. Idon't even know what that looks
like, you know, 10 years fromnow. But yeah, it's very
interesting to see very much,Sam. Overall, I've really
enjoyed having you on here. Ican't end the episode without
joking. You know, our audiencewill never hear it. But in the
middle of this technologyepisode, my computer had a blue
(39:23):
screen of death and we had tostop the entire podcast because
everything locked up and shutdown. So even in those moments,
the stuff can happen. But theshow must go on. So we show must
go on. That's right. We wereluckily we have a great editor
that will make the sound amazingat the end. But I wanted to end
this season by asking each ofour guests to tell us something
(39:45):
that they're grateful for. I'mreally a big proponent of
putting good vibes in theuniverse. I feel like there's
plenty of people doing theopposite. So my, my small
contribution to the world is tomake sure I put something good
and every time I could think ofit So, for you today, Sam,
what's something you're gratefulfor?
Sam Neer (40:03):
I love this question, by the way. And again, listen to
other amazing adjusted podcasts,go shout out, please go back to
the library with there's a tonof great episodes on Spotify,
your streaming, platform ofchoice, great content there. But
I have one professional and onepersonal. So the professional
thing I'm grateful for is anamazing claims product and
analysts and dev team. So again,I get to come on this podcast
(40:24):
and tell everyone about it. Butwe've got a lot of great people
who have been working fordecades, who really get all the
credit for supporting Berkeleyindustrial comp. And, again, our
alternative markets are bamtechIt's too much of a mouthful,
even for me to say it on apersonal level. I'll call it the
five F's, right? So my faith, myfamily, football, film, and
finally fast food. Okay, sothose five really get me up in
(40:47):
the morning and make me excitedto so I'm grateful for all of
those things. And, again,grateful for this podcast and
the best cohost group productmanager could ask for. So Vicki
offer a great conversation.
Greg Hamlin (41:00):
Thanks, Sam. Really appreciate having you as a guest
today. And with that, we willwrap up this episode, and hope
you'll follow us in futureepisodes releasing every two
weeks on Monday. You can alsocatch our blog on the off weeks
that is written by our wonderfulNatalie Dangles. So again,
remind everybody to do rightthink differently and don't
forget to care. And that's itguys
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