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From YouTube: Hosted Discussion: Experience with openIDL to Build Industrywide Applications - Truman Esmond, AAIS
Description
Hosted Discussion: Experience with openIDL to Build Industrywide Applications - Truman Esmond, AAIS
A brief overview of the openIDL architecture will be followed with how the network serves many types of analytic applications from state mandated regulatory reporting to collaborative efforts to build a platform to serve the industry in assessing catastrophe risks and the impact on the insurance industry. Three ongoing proof-of-concept trials will be presented including with a demonstration of the way regulators and carriers agree upon reports and how the output of analytics is made available to those who contributed to the solution.
To learn more about Hyperledger, the open source community focused on developing a suite of stable frameworks, tools and libraries for enterprise-grade blockchain deployments. Which serves as a neutral home for various distributed ledger frameworks including Hyperledger Fabric, Sawtooth, Indy, as well as tools like Hyperledger Caliper and libraries like Hyperledger Ursa click here: https://www.hyperledger.org/
Session Details & Slides (if available)- https://sched.co/j3fq
Access All Sessions: https://events.linuxfoundation.org/hyperledger-global-forum/program/schedule/
----
Hyperledger – Open Source Blockchain Technologies
Hyperledger is an open source community focused on developing a suite of stable frameworks, tools and libraries for enterprise-grade blockchain deployments.
It serves as a neutral home for various distributed ledger frameworks including Hyperledger Besu, Fabric, Sawtooth, Indy, as well as tools like Hyperledger Avalon, Cactus and libraries like Hyperledger Aries, Ursa. Learn more about Hyperledger projects: https://www.hyperledger.org/use
Case Studies: https://www.hyperledger.org/learn/case-studies
Training & Certification: https://www.hyperledger.org/learn/training
Tutorials: https://www.hyperledger.org/use/tutorials
Webinars: https://www.hyperledger.org/learn/webinars
Events: https://www.hyperledger.org/events
Vendor Directory: https://www.hyperledger.org/use/vendor-directory
Subscribe to the Hyperledger Newsletter: https://www.hyperledger.org/newsletter
Follow-us on Twitter @Hyperledger
Learn about Hyperledger Membership: https://www.hyperledger.org/about/join
#Hyperledger #HyperledgerGlobalForum2021 #Blockchain
A
B
A
Well,
thanks
everybody
for
joining.
We,
hopefully,
as
we
were
just
talking
about
everybody's-
got
a
little
energy
left
for
this
last
session
in
the
hyperledger
global
forum
for
the
2021
session,
as
brian
was
just
saying.
Hopefully,
everybody
was
able
to
hear
that
that
they
did
in
fact
save
the
best
for
last,
with
this
hosted
discussion
with
talking
about
experience
with
open
idl
to
build
industry-wide
applications.
A
A
The
last
couple
of
days.
By
way
of
introduction,
I'm
truman
esmend,
you
can
hit
me
on
email.
You
can
hit
me
on
twitter,
I'm
pretty
easy
to
find
on
linkedin
as
well
just
reach
out
to
me.
Let
me
know:
you're
a
human
and
I'll
be
happy
to
connect
and
see
what
we
can
do
together.
My
primary
job
is:
I'm
the
vice
president
of
membership
and
solutions
at
aais
a
bit
about
aeis.
Is
that
we're
a
not-for-profit
association
of
member
insurers,
so
we're
kind
of
a
natural
consortium
we've
been
around
for
almost
90
years?
A
A
My
job
as
the
vp
of
membership
and
solutions
is
to
engage
our
member
insurers
work
with
them
directly
to
understand
their
their
pain
and
their
opportunity
and
build
solutions
both
within
aeis
helping
our
carriers.
Our
member
jurors
do
the
same
thing
and
then
more
broadly,
to
fulfill
our
mission
for
the
insurance
industry,
which
leads
us
to
open
idl.
With
vote
for
open
idl,
I
serve
as
the
lead
architect,
I'm
also
the
chair.
Since
joining
the
linux
foundation
of
the
technical
steering
committee
open
idl,
it
stands
for
the
open
insurance
data
link.
A
But
the
first
thing
that
we
have
today
is
a
regulatory
reporting
data
network
and
that's
the
part
that
we're
going
to
be
able
to
leverage
and
start
to
build
more
industry-wide
applications
both
for
insurance
and
and
far
beyond.
So
with
that
we're
going
to
start
with
a
quick.
Why
of
open
idle
with
blockchain
technologies
and
really
any
sorts
of
efforts
like
this?
It's
critical
to
start
with
the.
A
Why
and
the
purpose
first
to
hopefully
a
five-minute
architecture
overview,
seeing
how
the
network
the
types
of
nodes,
the
what's
inside
a
node
and
how
the
network
works
itself
and
we'll
look
at
that
through
three
different
application.
Examples
that
we've
used
for
for
open
idl
looking
at
the
data
flows,
how
it
reinforces
privacy,
the
sorts
of
logic
a
bit
about
how
the
uis
interact
in
the
work
underway
and
we're
gonna.
Do
that
through
three
examples
that
we
have
today.
A
So
the
purpose
of
openidl
and
the
real
reason
is
here's,
and
I
want
to
make
sure
we
start
with
the
the
takeaways
first
so
that
in
case
we
run
out
of
time
or
we
get
into
deeper
discussions
discussions.
I
do
have
a
tendency
to
go
down
rabbit
holes,
but
it's
really
important
to
understand
that
open
idle
is
a
mechanism
that
allows
us
to
ask
and
answer
very
important
questions.
A
The
community
is
ready
and
open
for
business
a
lot
the
linux
foundation.
Now
that
it's
official
you
can
go
to
the
website.
Look
at
our
the
wiki
join
the
social
net,
social
forums
and
slack
and
then
hit
us
up
on
git
and
look
at
the
repositories
and
test
out
the
the
reference
code.
That
is
that
is
out
there
and
it
is
currently
being
developed.
A
Data
privacy
and
information
delivery
through
the
linux
foundation,
governance
networks,
of
which
open
idle
is
fits
right
in
that
that
strategy,
the
key
difference
or
the
key
differentiator
of
open
idl
is
the
regulatory
connection
through
property,
casualty,
insurance
and
the
property,
and
risk
of
that
allows
us
to
connect
a
lot
of
other
things
that
we
have
up
in
the
air
and
in
the
marketplace
to
go
forward
quick
introduction
to
ais.
I
won't
read
this
to
you,
but
it's
as
a
consortium
or
as
a
association
of
insurance
companies.
A
We
serve
three
functions
that
we
are
actually
licensed
by
each
state,
as
insurance
is
state-based
to
to
do.
First
thing
is
a
statistical
agent
and
we
at
that
role
we
aggregate
information
anonymize
it
that
are
so
that
it
is
supplied
to
us
by
our
member
carriers.
We
summarize
that
and
then
distribute
that
in
the
form
of
data
reports
to
the
state
regulators.
So
with
that,
so
it's
all
the
data
is
kept
anonymous
and
they've
got
a
general
understanding
of
what's
from
a
pricing
perspective
and
coverage
perspective.
A
A
We
also
reflect
it
back
to
the
industry,
so
it's
a
limited
anti-trust
exemption,
essentially
providing
the
cost
of
goods
sold
for
insurance
to
allow
new
products
to
be
developed,
as
well
as
experience
to
be
absorbed
by
the
industry
and
then
we're
an
advisory
organization
to
build
actual
insurance
products
on
that
as
standard
for
homeowners.
Auto
business
owners.
Things
like
that,
so
that
the
industry
is
better
prepared
to
to
offer
risk
transfer
mechanisms
and
where
there
may
be
less
experience
in
the.
A
Challenges
to
our
in
terms
of
providing
those
functions
is
that
we've
got
some
really
recurring
problems
as
data
owners.
It's
critical
to
keep
that
data
private
and
in
their
control
as
we
are
seeking
information
seekers
are
knowing
are
seeking
more
and
more
detailed
data
more
and
more
frequent
frequent
data
that
that
privacy
issue
is
increasingly
important
and,
as
a
result,
in
order
to
get
to
the
quality
and
the
needs.
A
We
need
to
make
sure
that
the
purpose
and
the
people
playing
in
that
situation
are
aligned
around
that
purpose
and
the
goals
of
that
network
data
and
building
open
idl.
We
hit
a
couple
of
key
things
in
the
development
you
probably
heard
reflected
over
the
course
of
the
last
few
days
requiring
collaboration
and
empathy
across
stakeholders
who
typically
don't
work
together.
A
We
definitely
don't
trust
each
other
when
we're
talking
about
regulators
and
insurance
companies
and
then
establish
a
community
which
you
know
a
network
but
a
network
for
a
particular
purpose
in
this
case
for
regulatory
reporting
and
then
establishing
those
standards.
The
rules
that
everybody
agrees
to
work
by
and
a
transparent
governance
mechanism
that
allows
those
rules
to
be
changed
over
time
and
then,
of
course,
being
allowing
that
to
be
delivered
on
an
open
technology
platform
which,
of
course,
we
have
with
the
hyperledger
fabric
fabric
as
a
foundation.
A
A
Regulators
have
an
opportunity
to
get
much
more
reliable,
timely
and
enhanced
information
so
that
they
can
develop
more
responsive
and
more
effective
policy
for
their
job,
about
fairness,
adequacy
and
and
and
discrimination
and
ais
is
the
intermediary.
We
can
actually
do
our
job
far
better.
We
can
stop
moving,
stop
storing
data,
delivering
information
faster,
an
ability
to
serve
more
of
the
industry
in
a
way
that
will
give
us
the
new
experiences
that
we
like
and,
as
I
often
say
so,
we
can
get
to
flying
cars,
so
I'll
top,
really
quick
and
see.
A
So
the
as
you
mentioned,
the
the
network
serves
three
major
roles:
the
network
governance
role.
It
serves
in
this
in
this
initial
case,
with
the
aeis
as
the
statistical
agent
enforcing
our
current
responsibilities.
Today,
data
owners,
in
this
case
the
insurers
who
are
deploying
data
to
their
own
peer
that
data
remains
private
and
secure.
And
again
it's
important
to
understand
that
the
data
that,
while
it's
deployed
to
the
peer,
the
data
itself,
is
not
on
the
network
on
the
blockchain
or
on
the
ledger.
A
Only
the
evidence
of
that
data
being
present
and
having
gone
through
the
requirements
to
be
to
be
used
for
the
purposes
of
regulatory
reporting.
Then
the
information
seekers,
in
this
case
the
state
regulators
who
are
preparing
the
data,
calls
the
questions
they'd
like
to
ask
of
the
network
that
are
those
transparent
government
interactions
with
that
data
and
then,
when,
when
they
issue
those
data
calls,
carriers
can
agree
to
them
and
permit
those
trusted
interactions
and
the
information
seekers.
A
The
state
regulators
get
assurance
that
that
data,
the
information
that
they're
getting
is
based
on
the
actual
real
information
that
was
present
in
the
system
that
maintains
integrity.
So
we
look
at
what's
happening
on
each
of
the
clusters
or
each
of
the
nodes
in
the
open.
Idl
network
is
based
on
kubernetes
a
series
of
clusters
providing
services
that
are
all
tightly
linked
or
entrusted
to
provide
a
suite
of
services.
A
What
we
call
open,
idl
in
a
box,
first
and
foremost,
is
the
hyperledger
fabric
up
here,
including
the
private
data
collection,
which
we
leverage
for
pretty
extensively.
We'll
talk
about
that,
then.
The
application
cluster,
which
provides
the
the
user
interface
that
the
humans
interact
to
consent,
the
data
calls
and
issue
draft
and
issue
them,
and
then
the
applications
underneath
the
hood,
the
insurance
data
manager,
which
brings
the
data
in
the
smart
contracts
that
bring
the
data
in
usable
for
regulatory
reporting
and
then
the
data
call
processor,
which
is
the
extraction
patterns.
A
That
brings
the
data
that
leverages
the
data
for
our
reporting
and
then
the
open
idl
data
call
app,
which
is
the
framework
that
that
deploys
those
things.
Essentially,
that
is
the
the
chain
code
and
the
smart
contracts.
There's
an
additional
cluster
for
etl
as
we're
talking
regulatory
reporting.
We
are
talking,
you
know,
often
hundreds
of
gigabytes,
of
data
on
a
monthly
or
quarterly
basis.
So
we've
got
a
robust
etl
process
that
can
be
varied
depending
on
the
the
needs
of
the
particular
node
and
that
exists
in
a
trusted
cluster.
A
And
then
we've
got
different
applications
that
that
provide
the
different
services
that
may
be
cloud
dependent
like
certificate
managers
or
or
application
security
protocols,
as
well
as
the
actual
data
stores
themselves.
In
this
case,
we're
using
mongodb
for
the
for
the
carrier
nodes,
let
me
dig
under
the
hood
a
little
more
to
get
the
idea
of
what
technologies
serve
those
different
components.
A
They
are
all
designed
to
be
replaceable
by
the
different
deployment
and
the
need
of
the
of
that
particular
node,
as
mentioned
using
mongodb
for
the
harmonized
data
store,
which
is
a
key
component.
That
data
that
is
known
to
be
of
have
met
the
guidelines
of
the
community
for
regulatory
reporting
that
could
be
other
data
stores
in
a
net
in
an
analytics
node.
It
might
be
oracle
or
something
like
that.
Talk
about
the
harmonized
data
store,
extraction
and
logic-
you
know
mapreduce
could
be
simple,
sql
queries.
A
It
could
also
be
syndicated
logic
that
we
might
deploy
as
chain
code
or
as
as
trusted
applications
as
part
of
the
network.
That's
similar
those
data
transformations
we
started
with
nifi
we've,
also
leveraged
different
implementations
for
our
reference
implementation,
as
well
as
for
aais's
own
use.
That
allows
us
to
scale
and
be
much
more,
a
much
higher
degree
of
service
and
throughput
application
levels,
node.js
for
the
user
interfaces
and,
of
course,
hyperledger
fabric.
A
Using
ibm
blockchain
platform
for
some
management
efficiencies,
but
ultimately
it's
not
the
fabric
node,
that's
really
the
critical
component
and
really
just
the
key
included
features,
including
the
private
data
collection
of
hyperledger
that
we
built
on
for
open
idl
when
those
nodes
are
deployed
it.
It
looks
a
bit
like
this
in
the
in
across
the
network.
There's
really
three
types
of
nodes
that
are
out
there,
starting
at
the
starting
at
the
bottom.
We've
got
carrier
nodes.
Who
are
the
data
owners?
A
Each
carrier
would
deploy
a
node
or
nodes
that
allow
them
to
contain
their
referential
data
and
their
own.
Harmonized
data
stores,
aeis
deploys
a
multi-carrier,
node
or
multi-tenant
node.
This
allows
us
to
serve
our
traditional
capacity
where
we're
reporting
on
behalf
of
companies
who
provide
us
data
directly
and
we
can
participate
in
our
trusted
capacity
using
a
multi-tenant
node,
but
they
can
still
get
all
the
benefits
of
the
broader
network
in
a
much
more
facilitated
way
to
participate.
A
Then
there's
the
analytics
node,
and
this
is
the
destination
of
the
data
that
is
retrieved
from
each
of
the
data
calls
from
each
of
the
private
data
collections
on
the
node
and
then,
as
that
data
is
output
and
visualized
that
could
be
visualized
on
a
workbook,
a
tableau
workbook,
for
example,
resident
on
that
node
or
the
output
data
could
be
delivered
through
a
more
traditional
or
what
we
might
call
legacy
mechanisms
in
today's
insurance
regulatory
reporting
world.
How
it
generally
works
is
a
is
a
two-phase
commit
the
carriers.
A
Are
loading
data
to
their
nodes
on
a
periodic
basis
might
be
daily
weekly.
We
ask
for
at
least
the
monthly
and
that
data
is
high
resolution.
It's
got
key
data
points
in
there
like
address
and
primary
keys,
but
it's
completely
private
completely
within
the
right
in
the
carrier's
control.
On
the
other
side
of
the
field,
regulators
are
creating
data
calls
questions.
They
would
like
to
ask
the
industry
to
help
them
build
policy
to
understand
what's
happening
in
the
market.
A
They
create
those
data,
calls
and
then
publish
them
over
the
over
the
open
ideal
network
to
carriers
and
issue
them
for
their
participation,
as
carriers
agree
to
participate
in
those
consent,
not
enough
in
a
consensus
thing,
but
they
agree
to
participate
in
those.
Those
transparent
interactions
with
their
data
are
executed,
harmonized
anonymized
and
the
identified
data
is
delivered
to
the
analytics
peer
and,
ultimately,
to
the
regulator
allowing
integrity
transactions
to
occur
at
each
step.
A
Ensuring
that
the
resulting
information
provided
to
the
regulator
has
provenance
and
traceability
all
the
way
back
to
the
to
the
source
data.
But
the
real
key
thing
for
carriers
to
participate
in
this
is
that
carriers
maintain
control,
carrier
identified
or
address
level
data,
or
any
really
any
data
they
don't
choose
to
never
leaves
company
control
never
leaves
the
network.
They
have
control
over
all
of
the
ingestion,
storage
and
transformation
operations,
leveraging
trusted
code
with
integrity
verified
through
the
network
and
those
and
all
of
it
is
transparent.
A
A
It's
not
quite
ready
yet
because
the
stat
agent,
in
this
case
aeis,
but
in
the
future,
really
could
be
any
organization
that
would
develop
the
extraction
pattern.
That
would
actually
be
the
code
that
interacts
and
queries
that
data
across
those
carriers.
That
does
the
data
transformation
and
the
input
for
the
report
that
extraction
pattern.
That
code
is
actually
attached.
The
data
call
logged
within
the
ledger
so
that
there's
nothing
there's
no
way
for
any
other
code,
but
that
to
be
run
when
it
comes
time.
A
Carriers
can
then
review
that
data
call
and
see
that
extraction
pattern,
and
if
they're
satisfied
the
test
runs,
they
see
how
their
data
is
going
to
respond
and
they're
willing
to
do
it.
They
can
consent
to
that
data.
Call
at
that
point.
The
system
executes
the
extraction
pattern
sends
the
result
to
the
analytics
node
using
the
private
data
collection.
That
information
is
is
sent
to
the
statistical
agent,
in
this
case
aais,
where
we
run
additional
quality
and
ultimately
deliver
the
report
to
the
state
regulators,
in
whatever
mechanisms
they
prescribe.
A
Let's
pop
up
in
the
hood
of
the
extraction
pattern,
because
this
is
really
where
the
rubber
hits
the
road
and
where
you
say,
we're
really
starting
to
play
with
some
sharp
objects
and
that
we're
looking
at
data
and
dealing
with
data
with
trusted
interactions
to
very
sensitive
data.
It
could
be,
you
know,
for
pnc
insurance.
It's
things
like
address,
maybe
location
information
as
well
as
coverage
information
claims
experience.
A
But
as
this
grows
into
other
insurance
mechanisms,
it
could
easily
be
much
more
pii.
So,
as
a
carrier
consents
to
it,
the
they
consent
to
the
data
call,
and
that
is
obviously
logged
onto
the
ledger.
The
regulators
are
aware
of
an
additional
consent.
They
don't
know
who
who
actually
performed
the
consent.
A
In
addition,
we
have
the
opportunity
at
this
point
to
not
just
query
the
data,
but
we
can
actually
run
additional
applications
and
decorate
that
data
relating
it
using
those
key
fields
or
private
data,
or
even
internal
data
stores,
to
things
like
the
payroll
protection
program
in
this
case.
Well,
in
our
first
example,
we'll
talk
about
how
we
related
private
data,
which
is
policy
record
to
a
loan
business
loan
application
record
through
the
through
that
address
and
how
that
was
able
to
be
really
really
powerful.
A
Then,
once
that
that
functionality
is
performed,
the
data
is
extracted
and
then
matched
and
performed
decorated,
appropriately
to
using
related
to
to
other
data
stores
that
is
placed
in
the
private
data
collection
and
then
is
replicated
to
the
analytics.
Node
data
is
ultimately
delivered
to,
in
this
case,
the
statistical
agent
on
the
analytics
node.
We
do
additional
quality
checks
and
then
it
can
provide
a
delivery
mechanism
into
a
reporting
database,
potentially
a
tableau,
ui
or
report,
or
a
data
set
output.
A
A
Obviously,
regulators
had
a
key
in
interest
in
as
they
were
issuing
policies
to
close
down
businesses
of
what
kind
of
interruption
or
what
kind
of
impact
that
was
going
to
have
on
their
small
businesses.
You
know
how
you
know
where
how
where
bill's
going
to
get
paid,
how
we're
paying
this
payroll
going
to
be
met,
and
they
did
a
data
call
that
gathered
some
high
level
information
about
policy
and
premium
and
kovic
19
related
claims
and
losses.
A
Well,
the
insurance
industry
had
long
ago
recognized
that
pandemics
and
viruses,
and
things
like
that
were
generally
excluded
from
typical
process.
They
only
happened
once
in
a
hundred
years
and
the
costs
would
be.
You
know
disproportionate
to
the
potential
value
for
that,
so
those
are
typically
excluded
risks.
So
the
initial
data
call
that
was
issued
indicated
that
there
was
very
little
coverage
out
there
for
for
pandemic
risks
and
business
interruption
and
virtually
no
claims
were
paid.
A
As
a
result,
we
we
had
nine
states,
including
california,
texas,
maryland
virginia
north
dakota,
connecticut,
new
jersey,
do
a
proof
of
concept.
Issuing
the
same
data
call
using
open
idl.
This
allowed
us
to
leverage
data.
That
was
already
available
in
the
marketplace.
Not
do
it
as
a
one-off
query
of
the
industry,
but
leverage
information
that
we
captured
from
statistical
data
and
then
augment
that
data
using
the
private
data
privacy
capabilities
of
open
idl
to
add
some
keys
to
that.
A
Even
though
they
had
business
interruption
insurance
that
would
have
covered
the
risk
and
what
it
shows
is
the
dramatic
gap
in
payroll
payroll
loans.
Take
pvp
loans
taken
out
and
relative
to
the
premium
paid
by
the
industry
for
business
interruption,
and
there
was
just
not
enough
premium
being
paid
to
remotely
close
that
gap,
but
it
gives
us
a
mechanism
from
a
regulatory
perspective
to
connect
that
and
close
that
gap
so
that
good
policies
can
be
made
encouraging
good
behavior.
A
Carriers
have
to
respond
to
sixteen
hundred
of
these
data
calls,
including
the
annual
reporting
things
they
have
to
do
just
for
doing
that
sort
of
business
in
a
particular
state
and
more
and
more,
are
being
created
every
day
that
that
hairball
in
the
upper
right
hand
corner
this
slide
represents
the
the
data
model.
That
indicates
all
the
different
data
that
is
necessary
by
all
the
different
data
calls
that
are
issued
because
each
state
issues
their
own.
A
When
enterprises
take
advantage
of
open
idl,
then
we
start
to
get
into
the
the
future
cases,
and
where
are
we
going
next
and
we
one
of
the
big
challenges
we've
had
as
an
insurance
industry
is
certainly
flood
and
catastrophe
and
and
the
changing
risk
environment
and
and
the
need
to
get
far
more
precise
in
terms
of
our
management
of
those
risks.
We
know
where
water
is
going
to
go
when
it
comes
down,
for
example,
using
these
models
and
incorporating
data
providers.
A
The
two
leading
flood
and
catastrophe
models
in
the
industry
proposed
is
allowing
them
specific
potential
access
to
those
models.
So
they
understand
the
risk,
can
develop
policies
and
mechanisms
to
collaborate
with
the
industry
to
both
mitigate
and
respond
to
that
risk
to
help
those
events
become
less
impactful,
and
those
includes
your
new
building
construction
codes,
for
example,
as
well
as
infrastructure,
to
help
mitigate
and
and
respond
in
a
collaborative
way.
A
That's
really
important
and
then,
as
the
as
those
types
of
cases
work
through.
Regulators
are
already
saying
they
are
that
they
have
data
owners
as
well.
They
have
rich
experience
that
has
been
reported
in
the
past.
They
have
lots
of
information
across
their
networks
that
for
licensing,
whether
it's
department
of
motor
vehicles
and
transportation,
you
know
street
maintenance
as
well
as
permitting
that
they
do.
For
everything.
A
So
as
we
go
beyond
what
we're
doing
today,
we've
certainly
have
lots
of
other
working
groups
going,
but
hopefully
everybody's
able
to
see
how
this
this
fundamental
ecosystem-
you
know,
is
this
really
baseline
privacy
technology
can
facilitate
all
the
different
applications
we've
seen.
You
know
that
are
leveraging
hyperledger
today,
but
also
how
openidl
is
a
fundamental
technology
again
and
community
to
and
through
open
idl,
to
do
or
through
linux
foundation,
to
do
network
of
networks
and
governance
networks.
A
Allow
us
to
develop
immediate
industry-wide
applications
for
within
the
insurance
industry,
supporting
specific
processes
and
new
stakeholders
like
reinsurers
and
agent
networks,
as
well
as
those
data
providers,
the
enterprise
deployments
for
dramatic
efficiencies
within
organizations,
as
well
as
mechanisms
to
integrate
their
enterprise
data
strategy
with
that
of
the
external
data
strategy.
So
they
can
start
to
really
do
some,
some
real
technical
investment
for
the
enterprise
and
their
own
purpose
and
leverage
product
verticals.
A
So
they
can
start
to
integrate
that
will
allow
you
know
across
the
industry
or
for
particular
policyholder
or
customer
experiences
that
will
be
broadly
impactful
and
require
those
regulatory
inroads
pun
intended
for
them
to
be
involved
and
allow
the
broader
impact
that
we
know
distributed.
Ledger
technologies
can
provide,
including
the
things
the
regulators
need
to
be
accountable
for,
like
smart
roads
and
street
maintenance
and
rules
around
when
we
can
have
autonomous
vehicles
and
flying
cars
based
on
weather
or
other
other
factors.
A
Beyond
that,
open
idle
is
at
a
point
ready,
for
you
know
to
start
being
introduced
to
other
communities
and
networks
insurance
related
or
not.
As
I
say,
often,
everything
is
related
to
insurance
at
one
degree
or
another,
but
the,
but
the
underlying
technology
allows
communities
to
start
to
establish
themselves
and
set
the
boundaries
of
their
purpose
and
then
open
idle
allows
them
to
interoperate
for
the
purpose
of
of
of
completely
new
experiences
and
value
across
these
communities
of
untrusted
stakeholders
so
getting
started
on,
learn
and
join.
A
I'm
joined
linux
foundation
most
folks,
so
you're
already
there
join
open
idl
for
the
in
the
community
start
to
to
to
get
to
get
to
get
busy.
We've
got
all
kinds
of
resources
out
there.
We
mentioned
on
git
on
the
wiki
and,
of
course,
on
the
website
and
invite
you
to
please
reach
out
to
me,
and
anybody
know
at
aais
the
linux
foundation,
to
help
you
get
started
so
that
I
will
stop
sharing
and
if
we
have
some
and
get
to
some.
C
C
I
first
heard
about
this
about
actually
at
the
last
hyperledger
global
forum
is
where
truman
and
joan
and
I
met,
and
we
started
talking
about
other
ways
that
the
linux
foundation
could
help
with
this
project,
help
with
open
sourcing
of
the
project
and
help
with
a
bit
of
the
governance
model
for
it
as
well,
and
so
we've
been
working
with
them
to
to
put
this
project
together
and
roll
it
out
and
and
and
so
it's
live
now.
Oh,
it
looks
like
there
is
a
question
in
the
q
a.
A
Yes,
yep,
let
me
see
go
for
it,
yes,
regulatory
compliance,
monitoring
and
regulatory
bodies
to
get
on
board
with
them
and
blockchain
in
the
first
place.
Actually,
it's
a
great
question.
Regulators
have
been
actually
very
on
board
with
this,
as
they
recognize
the
challenge
they
have
and
and
the
fights
they
kind
of
pick
with
the
industry
when
they're
trying
to
get
new
information
and
the
kovid
19
data
call
is
a
great
example
of
that
in
that
they're.
A
You
know
we're,
starting,
as
I
said,
to
start
to
play
with
some
sharp
objects
we're
getting
near
some
sensitive
areas.
When
we
talk
about
you
know,
area,
you
know
things
that
you
know
pain
claims
on
a
broad
basis
that
are
very
touchy
for
for
insurance
companies
and
for
compliance.
That's
actually,
you
know
our
role
as
aeis
and
where
this
is
going
to
have
objective
rules
for
compliance
that
allow
those
and
allow
those
rules
to
change,
get
faster.
A
Get
more
detailed
and
regulators
see
the
the
opportunity
there,
as
we
mentioned,
that
we've
got
they're
they're,
actually
kind
of
pushing
the
envelope
with
our
in
our
industry
because
they're
trying
to
they
want
to
say.
Yes,
but
they
just
need
a
mechanism
to
do
so
and
and
what
they're,
what
they
see
with
open
idl
is
an
opportunity
is
a
way
to
build.
A
A
A
You
know
until
they
can
permit
that,
and
they
know
that
if
an
aerial
wheel
falls
off,
it's
going
to
be
okay
and
there's
a
mechanism
that
you
know
it's
not
just
going
to
get
get
fought
about
in
court
and
people
are
going
to
get
made
whole
quickly
and
that's
what
we
need
to
do
for
all
these
different
things
that
are
changing.
Cyber
great
example:
securing
our
pipelines
and
food
supply.
Maybe.
B
You
have
to
bring
that
community
together
and
then,
if
you're,
in
a
regulated
industry
like
the
insurance
industry,
you
do
need
to
bring
the
regulators
to
the
table
as
well,
and
we
have
been
collaborating
with
the
regulators
all
along
and
the
example
that
truman
gave
earlier
around
the
coba
19
data.
Call
the
the
regulators
really
do
want
to
move
in
this
direction,
where
they
can
begin
expanding,
how
we
bring
data
together.
How
do
we
bring
that
closely
held
private
information
that
that
companies
have
it
could
be
a
bank?
It
could
be
an
insurance
company.
B
B
So
I
think
that's
a
really
important
point
that
we
have
to
have
the
regulators
there.
The
other
part
is
that
there
are
millions
of
dollars
a
year
spent
on
all
sorts
of
market
conduct
and
financial
auditing
by
the
government
and
open
idl
is
a
platform
that
enables
that
to
be
automated
and
instead
of
sending
large
blocks
of
data
to
a
regulator,
just
a
simple
dashboard
was
would
be
all
that
would
be
needed.
So
that's
why,
again
to
echo
what
truman
said:
come
join
the
open,
idl
project,
because
it's
more
than
just
regulatory
reporting.
A
Some
do
you
know
what
really
perks
them
up,
though,
is
what
we're
able
to
show
them,
because
the
industry
protects
itself
from
discrimination.
So
things
like
you,
know,
really
sensitive
things
like
race
income
levels,
and
you
know
geographic
disparity
are
kept
at
arm's
length,
which
makes
it
very
different
difficult
for
regulators
to
to
see
if
those
things
are
are
happening
correctly,
but
they,
you
know,
that's
the
you
know
the
the
conundrum.
B
A
B
B
A
Yeah,
because
we're
going
to
need
them
to
change
the
laws
right,
because
how
they
do
regulatory
reporting,
the
requirements
that
they
need,
that
they
put
on
an
industry
and
how
that
industry
responds
is
is
at
least
policy,
if
not
actual
law.
So
it's
not
something
that
happens
overnight
and
it's
kind
of
a
yes
and
thing.
We
all
the
things
we're
doing
now
with
open
ipl
that
are
really
cool.
We
still
have
to
do
the
old
way
too.
B
It's
there
are
many
things
we
can
imagine
that
we
would
like
to
do,
but
for
many
of
them
in
regulated
industries,
we
can
only
do
them
if
the
regulators
understand
what
we're
doing
they
for
those
things
that
are
important
to
them,
a
fair,
open,
transparent
marketplace.
They
need
to
come
along
with
us,
because
we
can
do
great
things
with
technology,
but
then
get
you
know
stops
short
because.
A
B
For
a
long
time,
but
they're
black
boxes
and
the
state
said
no,
we
cannot
approve
the
use
of
these
as
just
black
boxes.
We
have
to
look
under
the
cover
and
make
make
sure
we
really
understand
what
you're
doing
and
that
it's
fair
to
the
consumer.
So
the
same
is
true
here
with
the
with
the
exchange
of
information.
C
Jennifer
feel
free
to
share
your
audience
video
and
come
on.
If
you
want
to
ask
other
questions
too,
it's
good
to
hear
that
you're
working
in
the
same
space
that
it
resonates.
It
sounds
like
you're,
somewhere
between
water,
utility
control
and
critical
infrastructure,
cyber
security
infrastructure,
certainly
a
hot
topic
since
the
the
pipeline,
hacks
and
and
other
things
that
happen
so
anyways
feel
free
to
ask
more
questions
as
well.
If
I
see
another
one
from
iwana,
could
the
models
for
climate
change
evolve
soon?
How?
C
A
Sorts
of
things
yeah,
the
first
thing
it'll
do-
is
start
to
do
parametric
experiences.
You
know
to
you
know,
for
communication
efforts,
for
example,
may
not
be
paying
claims,
you
know,
or
necessarily
creating
a
whole
bunch
of
first
notice
of
losses,
which
means
there's
a
whole
bunch
of
claims
that
might
get
paid,
but
start
to
do
some
things
like.
Oh,
you
know.
We
know
that
this
policy
is
here.
We
know
this
weather
is
there.
We
can
send
notifications
like
that
and
down
the
hatches.
Here's
some
sandbags.
A
You
know
things
like
that.
That'll
happen
more
parametrically,
even
if
it's
not
actually
underwriting
issuing
insurance
or
paying
claims,
but
those
will
come
as
we
get.
You
know
more
trust
in
the
network
and
more
capability
that
the
regulators
will
permit
on
a
on
a
broad,
an
increasingly
broader
scale
basis
right,
they'll
allow
different
things
in
high
risk
areas.
You
know
for
houses
on
stilts
on
the
coast,
then
they
might
allow.
You
know
in
in
underserved
areas
in
the
in
an
urban
area,
for
example,.
D
D
Well
so
well,
first,
thank
you
for
for
inviting
me
to
to
be
part
of
it.
You
know
I
I'm
kind
of
in
a
unique
position
where,
as
you
can
see
behind
me,
you
know
I'm
a
water
nerd,
like
I'm
just
kind
of
dipping,
my
toe
for
lack
of
better
phrasing,
down
the
the
rabbit
hole
of
blockchain
and
and
the
entire
industry.
D
I've
been
in
wastewater
treatment
for
about
11
years
now
and
over
the
course
of
that
time
I
kept
finding
myself
saying
there
has
to
be
a
better
way
of
doing
this
when
it
comes
to
like
the
flow
of
information,
and
so
I
I
eventually
learned
what
blockchain
was,
and
I
heard
about
the
the
cryptocurrency
part
of
it.
First
I
was
like
okay,
you
know,
that's
that's
cool,
but.
D
The
supply
chain
management
aspect-
and
I
was
like
oh
oh,
that
has
that-
has
potential,
so
I
started
my
my
utility
had
had
some
some
issues
going
through
with
actually
our
industrial
users,
so
we
every
wastewater
utility,
has
stuff
kind
of
sending
them
our
industries,
sending
them
stuff.
Whether
it
be
you
know,
residential,
you
know
every
everything
that
gets
flushed
out
a
toilet,
swirled
down
a
drain.
D
You
know
chemical
manufacturing
all
that
stuff
gets
washed
down
and
sent
to
us
for
for
treatment
nine
times
out
of
ten
everything's
fine
every
once
in
a
while
at
like
three
o'clock
in
the
morning
on
a
sunday
when
no
one
is
really
here
paying
attention,
we
get
something
that
just
wipes
us
out.
It's
it's
equivalent
to.
You
know
a
disaster
situation.
D
You
know
so
in
addition
to
all
of
the
the
physical
stuff
that
we
have
to
do
to
prepare
for
that,
there's
also
paperwork
that
that
has
to
follow
primarily
billing
paperwork
between
the
utility
and
the
industrial
user.
You
know
we
have
a
hard
copy
contract
with
them
right
now,
where
you
know
our
attorneys
kind
of
battle
it
out
and
come
up
with
something
where
you
know.
If
it's,
if
it's
under
this
threshold
limit,
you
pay
one
fee.
If
you
exceed
the
threshold
limit,
you
pay
another
right
now,
nobody
trusts
each
other.
D
Nobody,
if
something
happens,
nobody
has
any
proof
of
it.
There's
no
way
for
for
anybody
to
get
financial
compensation.
It's
it's
kind
of
a
mess.
So
I
kind
of
started
thinking.
Okay!
Well,
you
know
I
I
had
recently
learned
about
you,
know
blockchain
technology
and
smart
contracts
and
their
their
use.
D
Cases
in
you
know
like
walmart
shipping,
and
I
was
like
now
what
if
we
could
apply
something
like
that
to
this
and
that's
kind
of
how
I
got
involved
in
this
and
learning
about
it
and
that
eventually
kind
of
morphed
into
after
the
the
cyber
attacks
in
february,
at
the
at
the
wastewater
plant
or
at
the
water
treatment
plant
down
in
florida,
and
especially
after
the
the
pipeline
hack.
You
know,
cyber
security
in
critical
infrastructure
has
really
come
to
the
forefront
of
national
headlines
and
nobody
likes
making
headlines
for
all
the
wrong
reasons.
Not.
D
You
know
so,
can
we
again
apply
that
kind
of
supply,
chain
management
thought
to
water
and,
by
extension,
to
basically
all
of
critical
infrastructure,
I
mean
the
the
the
industry
is
moving
towards
digitizing.
All
of
that
stuff,
like
you
said
you
know,
with
all
this
paperwork
floating
around
and
with
you
know,
calling
for
information.
D
A
D
All,
but
also
if
we
keep
the
human
element
at
least
somewhat
involved,
and
if
we
do
it
smartly
and
through
collaboration,
collaborative
efforts
like
hyper
ledger,
you
know
I
again,
I'm
I'm
still
learning
all
of
this
stuff.
You
know
as
I
go.
I
am
I
I've
talked
through
things
like
this.
I
I've
taught
myself
enough
to
like
overall
architecture
kind
of
stuff.
I
know
what
needs
to
go
in
place.
D
I
am
not
a
coder
I'm
trying
to
find
developers
so
shameless
plug
if
anybody's
interested
in
trying
to
create
not
skynet
skynet
reach
out
to
me,
my
linkedin
profile
is
attached
to
my
profile
here,
but
you
know
it's
one
of
those
things
where
this
could
work.
We
have
to
do
it
right.
We
have
to
do
it
smartly,
but
it
could
really
work
and
it
could
have
so
much
potential
benefit
for
the
same
reasons
why
it
would
benefit
that
the
insurance
industry.
A
Well,
those
public
private
partnerships
is
really
the
critical
piece
right.
You
know:
water
infrastructure,
energy
infrastructure,
you
know
utilities
you
know
from
you
know
again.
I
mentioned
street
maintenance
and
things
like
that.
We've
got
to
have
objective
things.
That
say,
you
know
you
can
again
autonomous
vehicles.
You
can
drive
on
it
when
it's
dry,
you
can't
drive
out
when
it's
wet,
you
can
drive
on
it.
A
If
the
trees
have
been
trimmed
in
the
last
month,
you
can't
drive
it
if
they
have
in
in
the
and
never
more
important
than
with
water
infrastructure,
energy
infrastructure,
food
supply
to
have
those
collaborative
mechanisms
because
nobody
wants.
You
know,
we've
been
talking
about
a
you
know,
romaine
that
seems
so
quaint.
A
B
Professionals
in
the
country-
and
they
have
echoed
the
same
pain
points
that
we
were
echoing
around
the
sharing
of
information-
and
you
just
described
in
your
case
as
well,
where
you
need
to
share
information
within
a
lot
of
folks
who
don't
trust
one
another.
But
you
have
to
work
together
and
you
have
a
lot
of
old
manual
processes
and
barriers
to
getting
good
information
just
because
of
the
way
it's
being
done
in
the
case
of
the
nfpa.
B
They
can
continue
to
use
those
systems
because
they
don't
turn
over
in
in
a
matter
of
months
or
even
years
very
often,
but
they
could
put
that
data
from
the
system
that
they
trap
it
in
today
into
the
harmonized
data
store
for
open
idl,
and
then
it's
available
to
be
linked
to
federal
data
resources.
Nfpa
data
resources
that
they
they
collect
and
then
they
can.
A
D
B
The
problem
that
you
just
described
each
of
the
entities
that
you're
working
with
from
the
businesses
that
may
send
something
into
your
water
treatment
facility
by
accident
for
those
who
have
to
clean
it
up
for
those
of
you
who
have
to
test
or
monitor
or
govern
each
of
you
have
your
own
systems,
but
you
do
have
points
where
you
want
to
connect
that
information
together.
So
what
the
open
ideal
offers.
B
You
is
a
way
for
those
companies
that
are
going
to
be
nervous
about
collaborating
with
you
and
and
others
that
are
maybe
municipalities
go
ahead
and
continue
using
the
systems.
You've
been
using
continue
to
keep
your
data
private,
but
find
a
way
to
answer
the
question
by
by
using
the
mechanisms
of
open
idl.
So
within
the
network
only
the
existence
of
the
data
we're
here.
We
have
the
data
available.
B
If
you,
if
you
need
to
query
it
and
then
each
entity
agrees
to
answer
a
question
using
a
smart
contract
where
the
data
is
never
stored
on
the
network,
but
but
the
ability
to
ask
the
question.
So
it's
all
about
trust
right,
you
don't
have
trust.
You
mentioned
that
there
are
there's
no
trust
between
the
parties.
This
enables
a
first
step.
Go
ahead,
leave
your
data
where
it
is.
We
know
we
don't
trust
one
another,
but
if
we
do
things
this
way,
we
can
at
least
start
to
answer
some
questions
together.
Yeah.
D
D
A
C
C
C
Yeah
definitely
well,
it
looks
like
we
are
just
over
time,
so
I
probably
should
wrap
up
with
that.
I
really
thank
you
truman
for
for
presenting
on
the
open,
ideal
vision
and-
and
this
will
be
recorded
and
published
online
in
a
little
bit
of
time.
I
don't
know
that
it's
put
up
there
instantly,
but
it'll
be
put
up
there
pretty
soon,
so
that
will
be
able
to
see
and
thank
you
all
for
coming
any
last
thoughts
or
comments
joan
or
or
truman.