December 13, 2024 • 15 mins
I spoke with Bryan Heller, Co-Founder and Chief Operating Officer of ClaimScore, a software platform designed to detect fraud in class action claims. We discussed how technology can help identify fraudulent claims in class action cases, whether certain cases are more vulnerable to fraud than others, and the future of fraud detection tools in litigation.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome to Reinventing Professionals, a podcast hosted by industry analyst Ari Kaplan,
(00:07):
which shares ideas, guidance and perspectives from market leaders shaping the next generation
of legal and professional services.
This is Ari Kaplan, and I'm speaking today with Brian Heller, the co-founder and chief
(00:27):
operating officer at ClaimScore, a software platform to help detect class action claims
fraud.
Hi, Brian, how are you?
How's it going, great, Ari, how are you?
I'm doing really well, really looking forward to this conversation, so tell us about your
background and the genesis of ClaimScore.
Absolutely.
First off, thanks for having me out.
I really appreciate it.
(00:48):
I graduated Penn State and I was a college athlete there, and then immediately jumped into
a career in consulting engineering, so leveraged some of my work ethic and dedication that
I use as a student athlete to really advance my career.
In that space, we used large data sets to attack environmental mediation projects and
(01:09):
then lay at full gas energy projects, so much different than the legal tech sector, but
another big data problem and really cut my teeth in the space through that career.
After that, I started a training app for athletes, so again, big data problem, a little different
of a use case in environmental consulting, but still moving into the tech sector for the
first time in my career.
(01:30):
Through that business, I actually met Don Bishada, who's the co-founder of ClaimScore.
He's a longtime class action attorney, plaintiff side, defense side, paid very close attention
to the administration process, and he saw the onset of class action claim fraud and came
to me and said, "Hey, I see this come."
Is this something that you can bring a team together and leverage your data skills to solve
(01:53):
as an industry-wide issue?
And I was eager to jump in and try, so we started this business in 2022 and been really
focused on helping the entire legal community, both to defend splent of lawyers, the administrators,
additional payout providers.
Everyone has a hole.
We're really just trying to use our technology to help them do the best job as we all can
(02:13):
collectively do to address this fraud problem.
How does ClaimScore work to detect fraud in class action claims?
When someone fills out a claim for, they provide some basic level information.
That information has passed to us.
We leverage a lot of first-party, third-party data providers and enrich that data to a much
bigger set.
(02:34):
If you think about the 10 points we're given, we end up with a couple hundred points
that we then narrow down to the 65 most intricate points that have the most meaning to us.
We then use ExpertSystemAI, it's an algorithm that evaluates all that data.
It's backed by a neural network machine learning tool, some of the more sophisticated
ML on the market, all built-in house, all proprietary.
(02:56):
And basically every claim that comes in starts with a score of a thousand.
Then as it fails criteria, that score is reduced.
And then we tag it with what we call a deduction code.
So you could think of this as the insight into why a claim is losing points.
Because an administrator is reviewing our data as the attorneys, as ultimately the judge
has to determine if this is a sound methodology, it was really important for us to build a backup
(03:21):
system so that it wasn't just black box ML.
We read data says it's fraud.
That wasn't really good enough for us internally.
And we didn't think the market would adopt that level of anonymity.
So when we did this, we wanted to make sure it was absolutely transparent.
And these deduction codes give those insights.
We tested the accuracy, right?
So we run control studies in these cases, there's what's known as known class members.
(03:44):
So if you think about a company is being sued and part of that process is turning over their
customer list, and those are what's defined as a known class.
So when we receive claims from those known class members, we can run them through our software.
And it's basically that check in balance.
And in that study, we're around 99.5% accurate identifying positive claims.
(04:04):
And then on the other hand, we run an analysis of what we think is true fraud.
And we're up in the 98.5 always growing by the month actually, but collectively, those
two numbers together.
So the being able to identify valid class members, which is precision and then being able to
accurately identify fraudulent claims, which is recall collectively brings our accuracy
(04:27):
to around 98.5%.
What types of cases are most commonly affected by fraud?
We focus on a lot of consumer based product cases, so false advertising or consumer fraud
cases.
We also work on data breach cases.
We've expanded into some other areas as well as an antitrust cases.
(04:49):
The thing about fraud is it doesn't discriminate.
The fraudsters aren't really reading settlement agreements and deciding which case to attack.
We believe that they probably have some sort of crawler bots or scrapers across the internet
looking for new claims sites that pop up these notice websites, do a dive on what is required
to submit a claim.
And ultimately, they think it's a lucrative opportunity.
(05:11):
They'll build an algorithm, build a system to basically reverse engineer the claim form
and submit claims programmatically into these forms.
There's all different types of fraud.
Sometimes it's just everyday people who decide they're going to try to submit 10 claims
on every case across the internet and that's duplication fraud.
We definitely want to stop it, but it doesn't blow these cases out of the water like the
(05:31):
programmatic attacks.
We've worked on cases where the class size was 100,000 people and millions of claims came
in.
We're working on cases that are also the classes hundreds of millions of people and they
still get millions of claims coming in.
They're not reading the fine print on whether or not a case is large or small.
They're ultimately just looking at is a proof of purchase required and they submit a
(05:54):
claim without a proof of purchase is authentication required.
So they have to put in any type of credentials to make a claim.
If not, that's a no attack.
And you'll see it ramp across the claim's period.
They start testing the waters.
They're pulling resources.
So there's money spent on submitting these claims.
So give it a try and as the claim starts to go through as the days go by as the claim
(06:20):
We're seeing it on a large percentage of consumer cases, state of breach and trust and the
likes.
Does this type of activity occur more frequently in certain jurisdictions?
We're seeing it everywhere.
We've done some state level cases that you see a ton of fraud and again, they're not reading
the five brands.
So a lot of it is as simple as just determining whether the claim was valid based on the
(06:43):
jurisdiction.
So we had a single state case and claims were being submitted all over the place or we
did a case in Canada hours required to be a Canadian citizen and they had claims coming
in from all over the place.
We've done big nationwide cases as well.
I don't think they're even looking at jurisdiction to be honest when they're deciding which
case to attack.
Who's your client?
(07:03):
We've worked with some of the big defense firms, some of the big plaintiffs firms.
We've also directly worked with several of the administrators.
We're working with digital disbursements who's the largest digital payment provider in
this space.
Ultimately, everyone has invested interest in making sure that we reduce fraud.
When we initially entered the market, we thought we might be more valuable to the defense
(07:24):
side.
But if you think about the two different settlement vehicles in the class space, one being
the claims made where it doesn't really matter how many claims are submitted, they're essentially
liable for paying out the total volume.
They're a common fund.
There's a fixed amount of money and no matter how many claims come in, that's the amount
of money that's getting distributed across the class.
Those common fund cases become even more problematic.
(07:46):
We worked on one case where they were expecting a couple of tens of thousands of claims to come
in.
They got six and a half million.
When it started to shake out before full involvement in the process, the payout, the
plan benefit was supposed to be around $50.
It dropped down below 15.
During the final approval hearing, the judge wasn't at a point where she wanted to grant
(08:08):
final approval.
She just thought that as these class members were waving their rights to sue in the
future and the defense was getting the freedom from this expense that they deserved more.
We were involved in the process more post that final approval hearing.
We were able to reduce significantly more fraud from the evaluation and brought the claim,
(08:29):
the per claim payout up around $35.
At that point, the judge felt that was a better compensation given where it started and where
it was and ultimately granted final approval.
Since the defense firm claims made case as a much higher appetite for reducing the fraud,
in the common fund, everyone does because these cases won't reach closure if the number
(08:52):
gets out of hand.
From the payment provider standpoint, they don't want to pay fraudulent actors.
It's just part of the banking industry.
There's a new customer laws.
There's a lot that goes in from a regulatory stance that they don't want to participate
in that.
It's also a big data problem.
(09:12):
The large players who have big data teams that can address this, which will excel has a
limit of a million rows.
If you start to pull claims into excel and there's more than a million, you're not going to
be able to look at them in a single file.
Identifying simple duplicates could be a problem if you're just sorting by names and trying
to find which claims are the same.
If it's bigger than a million, you're topping out.
(09:33):
It becomes a much bigger data problem.
That's where we help the administrators a lot from a time frame standpoint.
Using an algorithm is clearly more efficient than doing a manual review.
You mentioned being a student athlete at Penn State.
How has your experience as an athlete affected your leadership style and also your ability
(09:55):
to break down and deconstruct a challenge in order to drive success?
When you're an athlete and trying to reach a very high level of success, I could be in the
big 10, which was the hardest conference for wrestling at Penn State, which was a perennial
powerhouse, even breaking the line up was a huge accomplishment at the time.
You're forced to be very introspective, especially in wrestling.
(10:16):
There's not another person on the team you can blame for loss.
They're not a quarterback throwing a pass.
The pass was great, but the receiver didn't catch it, right?
There's no one else to reflect upon except for yourself.
That process of reflection and going back to the practice room, making adjustments and
giving it a shot, that whole process was great.
Early on in my career, I come in at college, you really don't know what you want to do or
(10:38):
what life is really about.
And consulting engineering was a great avenue for me to refine that.
I started coaching high school wrestling as well.
I had a parallel path of a professional career, but a coaching career as well.
That introspective reflection was even more important as a team leader, as a coach.
I'm working at my team of 40 athletes that I'm only setting 14 on the mat.
How can I make them of seeing themselves, what I saw in them, right?
(11:02):
And the way that you communicate with them and refining the messaging.
And one of the basic principles that I looked at from that mentorship perspective was the
idea that you need the information to be able to tell them how to excel.
But you also need to deliver that in a way that they're going to absorb it.
So the communication part is as important as the actual information part.
(11:24):
So as we build a team at claims school, we have 15 full time software engineers and developers
and product managers, the full nine.
And we are building a super strong team.
And I impart those same skills as I develop, help them develop in their professional careers.
I want to make sure that I can reflect and give them the proper information.
(11:44):
The other thing is the balance of work ethic and passion to be great at anything.
You really need to work hard at it.
But if you're passionate about it, that work ethic comes easy.
It's one thing that have the roll out of bed and then go to the job you don't really like
and try to work really hard to be successful.
You're just waiting for the end of the day to come.
You could punch your clock and go home.
(12:04):
But if you're truly passionate about it, you can bring a level of energy that accelerates
your growth as a business, but also as a professional.
As we build our team, that's what I'm looking for.
Everyone on our team is very into technology.
A people with master's degree in machine learning and big data and computer science and systems
engineering.
And they really are passionate about like developing a career.
And we're looking for avenues to help them with whether it's continuing education or formal
(12:27):
education.
They also work really hard.
There's ties where deadlines are approaching.
Milestones are appearing on the roadmap and they're able to push through and work hard because
they love what they do.
We just try to balance that environment from its fun, our Slack channels, comical.
We have fun emojis, but everyone's expected to contribute and work really hard.
So I think transitioning from an athlete to a coach and then a business leader, a lot
(12:51):
of those skills are just one and the same.
It's just which problem are you attacking and then applying that set of tools to have
the best outcome?
How do you see class action claims fraud, detection, technology evolving?
Early on, the problem wasn't so sophisticated.
It was more easy to detect.
This is broad across the internet.
(13:11):
I know the banking industry and the lending industry is under the severe attacks of the
class action spaces, e-commerce as well.
Early on, there was no detection.
They could get away with anything because they could submit anything on that form and get
paid.
But as everyone is trying to address the problem, even the basic level tooling, web application,
firewalls and cap show, they could stop bot submissions to a degree.
(13:34):
But then all of a sudden cap chakies are leaked all over the internet, phone forms evolve.
They're getting away from using AI to mimic human mouse movements.
Now those programmatic submissions aren't being detected, they're coming all the way through
the claims process.
The next evolution was just fake identities.
People just typing whatever they wanted, names, emails, phone numbers.
(13:55):
It didn't have to make sense.
They didn't have to be valid.
They were getting approved because there was a small volume.
No one was really paying that close of attention.
It wasn't at a level that caused an alarm.
If you expected 20,000 claims, you got 22,000 claims.
No one was all that concerned about it.
But when it hit the millions, the big shiny light went on to the process and said, what can
we do here?
(14:15):
Over the last 18 months, we've seen the level of sophistication increase, really what's
called synthetic identity fraud.
What's people scraping information off the internet, whether it's through the white pages
or dark web, and they're applying some different piece of information typically associated
with how they're going to get paid and submitting a claim on someone else's behalf.
(14:36):
But in a way, they can collect the benefit.
In COVID, I heard a lot of stories of people having unemployment benefits being applied on
their behalf and they would receive a call like your application has been approved.
Like, I never applied for this.
It's that same tactic that was deployed then now.
And we see just a bigger percentage of the fraud coming from that level of sophistication
early on.
(14:56):
There'd be a couple of these synthetic identity fraud claims coming through.
And now it's the large majority.
So definitely the shift from anything goes to slightly more sophisticated and significantly
more sophisticated.
This is Ari Kapland, speaking with Brian Heller, the co-founder and chief operating officer
at ClaimScore, a software platform to help detect class action claims fraud.
(15:21):
Brian has really been a privilege.
Thanks so much.
Thank you, Ari.
I really appreciate it.
Thank you for listening to the reinventing professionals podcast.
Visit reinventingprofessionals.com or recaplandadvisors.com to learn more.
[MUSIC]
Thank you for listening to the reinventing professionals podcast.
(15:42):
Visit reinventingprofessionals.com or recaplandadvisors.com to learn more.
Welcome to Reinventing Professionals, a podcast hosted by industry analyst Ari Kaplan,
(00:07):
which shares ideas, guidance and perspectives from market leaders shaping the next generation
of legal and professional services.
This is Ari Kaplan, and I'm speaking today with Brian Heller, the co-founder and chief
(00:27):
operating officer at ClaimScore, a software platform to help detect class action claims
fraud.
Hi, Brian, how are you?
How's it going, great, Ari, how are you?
I'm doing really well, really looking forward to this conversation, so tell us about your
background and the genesis of ClaimScore.
Absolutely.
First off, thanks for having me out.
I really appreciate it.
(00:48):
I graduated Penn State and I was a college athlete there, and then immediately jumped into
a career in consulting engineering, so leveraged some of my work ethic and dedication that
I use as a student athlete to really advance my career.
In that space, we used large data sets to attack environmental mediation projects and
(01:09):
then lay at full gas energy projects, so much different than the legal tech sector, but
another big data problem and really cut my teeth in the space through that career.
After that, I started a training app for athletes, so again, big data problem, a little different
of a use case in environmental consulting, but still moving into the tech sector for the
first time in my career.
(01:30):
Through that business, I actually met Don Bishada, who's the co-founder of ClaimScore.
He's a longtime class action attorney, plaintiff side, defense side, paid very close attention
to the administration process, and he saw the onset of class action claim fraud and came
to me and said, "Hey, I see this come."
Is this something that you can bring a team together and leverage your data skills to solve
(01:53):
as an industry-wide issue?
And I was eager to jump in and try, so we started this business in 2022 and been really
focused on helping the entire legal community, both to defend splent of lawyers, the administrators,
additional payout providers.
Everyone has a hole.
We're really just trying to use our technology to help them do the best job as we all can
(02:13):
collectively do to address this fraud problem.
How does ClaimScore work to detect fraud in class action claims?
When someone fills out a claim for, they provide some basic level information.
That information has passed to us.
We leverage a lot of first-party, third-party data providers and enrich that data to a much
bigger set.
(02:34):
If you think about the 10 points we're given, we end up with a couple hundred points
that we then narrow down to the 65 most intricate points that have the most meaning to us.
We then use ExpertSystemAI, it's an algorithm that evaluates all that data.
It's backed by a neural network machine learning tool, some of the more sophisticated
ML on the market, all built-in house, all proprietary.
(02:56):
And basically every claim that comes in starts with a score of a thousand.
Then as it fails criteria, that score is reduced.
And then we tag it with what we call a deduction code.
So you could think of this as the insight into why a claim is losing points.
Because an administrator is reviewing our data as the attorneys, as ultimately the judge
has to determine if this is a sound methodology, it was really important for us to build a backup
(03:21):
system so that it wasn't just black box ML.
We read data says it's fraud.
That wasn't really good enough for us internally.
And we didn't think the market would adopt that level of anonymity.
So when we did this, we wanted to make sure it was absolutely transparent.
And these deduction codes give those insights.
We tested the accuracy, right?
So we run control studies in these cases, there's what's known as known class members.
(03:44):
So if you think about a company is being sued and part of that process is turning over their
customer list, and those are what's defined as a known class.
So when we receive claims from those known class members, we can run them through our software.
And it's basically that check in balance.
And in that study, we're around 99.5% accurate identifying positive claims.
(04:04):
And then on the other hand, we run an analysis of what we think is true fraud.
And we're up in the 98.5 always growing by the month actually, but collectively, those
two numbers together.
So the being able to identify valid class members, which is precision and then being able to
accurately identify fraudulent claims, which is recall collectively brings our accuracy
(04:27):
to around 98.5%.
What types of cases are most commonly affected by fraud?
We focus on a lot of consumer based product cases, so false advertising or consumer fraud
cases.
We also work on data breach cases.
We've expanded into some other areas as well as an antitrust cases.
(04:49):
The thing about fraud is it doesn't discriminate.
The fraudsters aren't really reading settlement agreements and deciding which case to attack.
We believe that they probably have some sort of crawler bots or scrapers across the internet
looking for new claims sites that pop up these notice websites, do a dive on what is required
to submit a claim.
And ultimately, they think it's a lucrative opportunity.
(05:11):
They'll build an algorithm, build a system to basically reverse engineer the claim form
and submit claims programmatically into these forms.
There's all different types of fraud.
Sometimes it's just everyday people who decide they're going to try to submit 10 claims
on every case across the internet and that's duplication fraud.
We definitely want to stop it, but it doesn't blow these cases out of the water like the
(05:31):
programmatic attacks.
We've worked on cases where the class size was 100,000 people and millions of claims came
in.
We're working on cases that are also the classes hundreds of millions of people and they
still get millions of claims coming in.
They're not reading the fine print on whether or not a case is large or small.
They're ultimately just looking at is a proof of purchase required and they submit a
(05:54):
claim without a proof of purchase is authentication required.
So they have to put in any type of credentials to make a claim.
If not, that's a no attack.
And you'll see it ramp across the claim's period.
They start testing the waters.
They're pulling resources.
So there's money spent on submitting these claims.
So give it a try and as the claim starts to go through as the days go by as the claim
(06:20):
We're seeing it on a large percentage of consumer cases, state of breach and trust and the
likes.
Does this type of activity occur more frequently in certain jurisdictions?
We're seeing it everywhere.
We've done some state level cases that you see a ton of fraud and again, they're not reading
the five brands.
So a lot of it is as simple as just determining whether the claim was valid based on the
(06:43):
jurisdiction.
So we had a single state case and claims were being submitted all over the place or we
did a case in Canada hours required to be a Canadian citizen and they had claims coming
in from all over the place.
We've done big nationwide cases as well.
I don't think they're even looking at jurisdiction to be honest when they're deciding which
case to attack.
Who's your client?
(07:03):
We've worked with some of the big defense firms, some of the big plaintiffs firms.
We've also directly worked with several of the administrators.
We're working with digital disbursements who's the largest digital payment provider in
this space.
Ultimately, everyone has invested interest in making sure that we reduce fraud.
When we initially entered the market, we thought we might be more valuable to the defense
(07:24):
side.
But if you think about the two different settlement vehicles in the class space, one being
the claims made where it doesn't really matter how many claims are submitted, they're essentially
liable for paying out the total volume.
They're a common fund.
There's a fixed amount of money and no matter how many claims come in, that's the amount
of money that's getting distributed across the class.
Those common fund cases become even more problematic.
(07:46):
We worked on one case where they were expecting a couple of tens of thousands of claims to come
in.
They got six and a half million.
When it started to shake out before full involvement in the process, the payout, the
plan benefit was supposed to be around $50.
It dropped down below 15.
During the final approval hearing, the judge wasn't at a point where she wanted to grant
(08:08):
final approval.
She just thought that as these class members were waving their rights to sue in the
future and the defense was getting the freedom from this expense that they deserved more.
We were involved in the process more post that final approval hearing.
We were able to reduce significantly more fraud from the evaluation and brought the claim,
(08:29):
the per claim payout up around $35.
At that point, the judge felt that was a better compensation given where it started and where
it was and ultimately granted final approval.
Since the defense firm claims made case as a much higher appetite for reducing the fraud,
in the common fund, everyone does because these cases won't reach closure if the number
(08:52):
gets out of hand.
From the payment provider standpoint, they don't want to pay fraudulent actors.
It's just part of the banking industry.
There's a new customer laws.
There's a lot that goes in from a regulatory stance that they don't want to participate
in that.
It's also a big data problem.
(09:12):
The large players who have big data teams that can address this, which will excel has a
limit of a million rows.
If you start to pull claims into excel and there's more than a million, you're not going to
be able to look at them in a single file.
Identifying simple duplicates could be a problem if you're just sorting by names and trying
to find which claims are the same.
If it's bigger than a million, you're topping out.
(09:33):
It becomes a much bigger data problem.
That's where we help the administrators a lot from a time frame standpoint.
Using an algorithm is clearly more efficient than doing a manual review.
You mentioned being a student athlete at Penn State.
How has your experience as an athlete affected your leadership style and also your ability
(09:55):
to break down and deconstruct a challenge in order to drive success?
When you're an athlete and trying to reach a very high level of success, I could be in the
big 10, which was the hardest conference for wrestling at Penn State, which was a perennial
powerhouse, even breaking the line up was a huge accomplishment at the time.
You're forced to be very introspective, especially in wrestling.
(10:16):
There's not another person on the team you can blame for loss.
They're not a quarterback throwing a pass.
The pass was great, but the receiver didn't catch it, right?
There's no one else to reflect upon except for yourself.
That process of reflection and going back to the practice room, making adjustments and
giving it a shot, that whole process was great.
Early on in my career, I come in at college, you really don't know what you want to do or
(10:38):
what life is really about.
And consulting engineering was a great avenue for me to refine that.
I started coaching high school wrestling as well.
I had a parallel path of a professional career, but a coaching career as well.
That introspective reflection was even more important as a team leader, as a coach.
I'm working at my team of 40 athletes that I'm only setting 14 on the mat.
How can I make them of seeing themselves, what I saw in them, right?
(11:02):
And the way that you communicate with them and refining the messaging.
And one of the basic principles that I looked at from that mentorship perspective was the
idea that you need the information to be able to tell them how to excel.
But you also need to deliver that in a way that they're going to absorb it.
So the communication part is as important as the actual information part.
(11:24):
So as we build a team at claims school, we have 15 full time software engineers and developers
and product managers, the full nine.
And we are building a super strong team.
And I impart those same skills as I develop, help them develop in their professional careers.
I want to make sure that I can reflect and give them the proper information.
(11:44):
The other thing is the balance of work ethic and passion to be great at anything.
You really need to work hard at it.
But if you're passionate about it, that work ethic comes easy.
It's one thing that have the roll out of bed and then go to the job you don't really like
and try to work really hard to be successful.
You're just waiting for the end of the day to come.
You could punch your clock and go home.
(12:04):
But if you're truly passionate about it, you can bring a level of energy that accelerates
your growth as a business, but also as a professional.
As we build our team, that's what I'm looking for.
Everyone on our team is very into technology.
A people with master's degree in machine learning and big data and computer science and systems
engineering.
And they really are passionate about like developing a career.
And we're looking for avenues to help them with whether it's continuing education or formal
(12:27):
education.
They also work really hard.
There's ties where deadlines are approaching.
Milestones are appearing on the roadmap and they're able to push through and work hard because
they love what they do.
We just try to balance that environment from its fun, our Slack channels, comical.
We have fun emojis, but everyone's expected to contribute and work really hard.
So I think transitioning from an athlete to a coach and then a business leader, a lot
(12:51):
of those skills are just one and the same.
It's just which problem are you attacking and then applying that set of tools to have
the best outcome?
How do you see class action claims fraud, detection, technology evolving?
Early on, the problem wasn't so sophisticated.
It was more easy to detect.
This is broad across the internet.
(13:11):
I know the banking industry and the lending industry is under the severe attacks of the
class action spaces, e-commerce as well.
Early on, there was no detection.
They could get away with anything because they could submit anything on that form and get
paid.
But as everyone is trying to address the problem, even the basic level tooling, web application,
firewalls and cap show, they could stop bot submissions to a degree.
(13:34):
But then all of a sudden cap chakies are leaked all over the internet, phone forms evolve.
They're getting away from using AI to mimic human mouse movements.
Now those programmatic submissions aren't being detected, they're coming all the way through
the claims process.
The next evolution was just fake identities.
People just typing whatever they wanted, names, emails, phone numbers.
(13:55):
It didn't have to make sense.
They didn't have to be valid.
They were getting approved because there was a small volume.
No one was really paying that close of attention.
It wasn't at a level that caused an alarm.
If you expected 20,000 claims, you got 22,000 claims.
No one was all that concerned about it.
But when it hit the millions, the big shiny light went on to the process and said, what can
we do here?
(14:15):
Over the last 18 months, we've seen the level of sophistication increase, really what's
called synthetic identity fraud.
What's people scraping information off the internet, whether it's through the white pages
or dark web, and they're applying some different piece of information typically associated
with how they're going to get paid and submitting a claim on someone else's behalf.
(14:36):
But in a way, they can collect the benefit.
In COVID, I heard a lot of stories of people having unemployment benefits being applied on
their behalf and they would receive a call like your application has been approved.
Like, I never applied for this.
It's that same tactic that was deployed then now.
And we see just a bigger percentage of the fraud coming from that level of sophistication
early on.
(14:56):
There'd be a couple of these synthetic identity fraud claims coming through.
And now it's the large majority.
So definitely the shift from anything goes to slightly more sophisticated and significantly
more sophisticated.
This is Ari Kapland, speaking with Brian Heller, the co-founder and chief operating officer
at ClaimScore, a software platform to help detect class action claims fraud.
(15:21):
Brian has really been a privilege.
Thanks so much.
Thank you, Ari.
I really appreciate it.
Thank you for listening to the reinventing professionals podcast.
Visit reinventingprofessionals.com or recaplandadvisors.com to learn more.
[MUSIC]
Thank you for listening to the reinventing professionals podcast.
(15:42):
Visit reinventingprofessionals.com or recaplandadvisors.com to learn more.
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