►
Description
With guests Jim Schoening and Casey Rock from the Linux Foundation and the IAM Project, plus, the monthly update on Blockchain in the mortgage industry with James Hendrick; led by Marvin Bantugan, MI Subgroup Chair.
Timestamps:
00:00 Welcome and presentation
02:21 Community information
03:31 The state of Blockchain in the mortgage industry
13:03 IPDS Wallet and Demonstration
45:33 Future agenda items
Meeting notes from the September 8th Meeting: https://wiki.hyperledger.org/display/CMSIG/2022-09-08+Meeting+notes
Join the FMSIG Mortgage Subgroup Wiki: https://lists.hyperledger.org/g/financial-markets-sig
Follow the Hyperledger Financial Markets Special Interest Group LinkedIn page: https://www.linkedin.com/showcase/hyperledger-financial-markets-sig
----
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License
Creative Commons Attribution license (reuse allowed)
A
Welcome
to
the
september
financial
markets,
mortgage
subgroup
meeting
under
the
hyperledger
umbrella
before
we
get
started,
I'd
like
to
express
our
appreciation
to
the
financial
markets,
special
interest
group
and
the
hyperledger
foundation
they're
the
group
that
really
makes
these
meetings
possible.
So
I
always
like
to
say
thank
you
to
them.
A
Okay,
as
always,
please
note
that
this
meeting
is
being
recorded
and
is
under
the
umbrella
of
the
hyperledger
foundation,
as
I
just
mentioned,
and
to
please
abide
by
the
antitrust
policy
that
I'm
sharing
on
screen
and
the
code
of
conduct.
The
anti-trust
policy
states
that
we
avoid
discussions
of
company,
specific
pricing
products
and
projects.
We
don't
make
negative
remarks
about
other
companies
or
other
products,
and
the
code
of
conduct
means
that
we
treat
each
other
with
respect,
never
discriminate,
communicate
constructively
and
we
fully
support
hyper
ledger's
policy
of
openness,
equity
and
inclusion.
A
A
Here's
our
agenda
for
today,
we've
already
gone
through
the
welcome
and
housekeeping
there's
a
brief,
hyperledger
community
information
portion.
Then
james
is
going
to
discuss
the
state
of
the
blockchain
in
the
mortgage
global
mortgage
industry.
We
have
a
demonstration
and
a
discussion
of
the
ipds
wallet
and
a
mortgage
industry
example
from
james
soning
and
casey
rock,
and
then
we'll
go
over
some
future
agenda
topics.
A
So
with
that,
I
we
always
like
to
start
off
with
this
same
set
of
slides,
because
we
want
to
reinforce
that
we're
all
on
the
same
blockchain
journey.
I
know
people
that
have
been
here
have
heard
me
say
this
multiple
times
this
group
is
meant
to
help
everyone
on
their
blockchain
journey
to
demonstrate
the
feasibility
of
blockchain
technology
through
mortgage
industry,
use
cases
we're
going
to
see
one
of
those
today
and
to
define
potential
implementation
paths,
we're
all
on
the
same
journey,
we're
just
at
different
points.
A
A
A
The
next
three
slides
I
always
mention
for
those
that
are
new
to
the
group.
This
first
one
is
just
a
site
map
that
shows
some
of
the
links
that
provide
information
on
hyperledger,
the
second
one
from
the
bottom.
There
is
the
financial
markets,
mortgage
subgroup
wiki.
So
take
a
look
at
that
one
if
you're
new,
also,
if
you
do
want
to
access
some
of
this
information,
you'll
need
an
lfid.
A
So
this
slide
shows
you
how
to
do
that
and
then
the
last
one
is
just
some
free,
blockchain
training.
I
always
mentioned
that
I've
taken
this
and
it
was
very
helpful
for
me.
So
please
avail
yourself
of
this
information
with
that.
I'd
like
to
turn
it
over
to
james
hendrick
and
he's
going
to
go
over
the
state
of
blockchain
and
the
global
mortgage
industry.
Take
it
away
james.
B
B
2022
you
can
see
we've
done
a
variety
of
different
articles
spanning
across
pretty
much
every
continent
on
the
planet.
You
know
as
we
get
into
this
month's
updates.
I
want
to
talk
about
cd
insights
and
their
recent
release
of
the
state
of
the
blockchain
report,
as
well
as
some
information
going
on
in
the
uk
with
berkeley's
marvin
next
slide,
all
right
for
the
state
of
the
blockchain
q2
report.
B
So
back
in
january
we
published,
or
we
posted
to
the
wiki
page,
the
2021
annual
blockchain
report
from
cv
insights,
it's
an
extremely
valuable
report,
they're
very
detailed
150,
200
pages
long
or
so,
and
it
breaks
down
blockchain
activity
that
is
going
on
across
industries
via
countries
pretty
much
every
way
that
you
want
to
slice
and
dice
it.
You
know
some
of
the
big
things
that
came
out
of
the
q2
report
summarizing
the
first
two
quarters
of
this
year.
There
was
1.4
billion
record
blockchain
funding
in
europe.
B
These
first
two
quarters
europe
was
the
only
global
region
with
growth
in
blockchain,
venture
funding
and
deals,
52
percent
of
deal
growth
in
infrastructure
and
development,
one
of
the
only
blockchain
spaces
with
real
deal
growth
in
q2
of
2022,
reaching
a
new
high
of
47
deals
and
then
in
silicon
valley,
they
represented
28
share
of
all
the
u.s
funding
with
new
york
following
closely
678
million
man,
los
angeles
with
492
million,
but
not
all
is
dreamy
in
crypto
land.
The
report
also
talks
about
what's
happening
in
the
crypto
markets.
B
B
B
Copper
has
drawn
investments
from
big
names
in
the
global
venture
capital
sector,
such
as
local
globe,
dawn
capital
and
mmc
ventures,
and
barclays
is
expected
to
invest
relatively
modest
sum
in
the
millions
of
dollars
as
a
part
of
the
round.
All
of
this
is
occurring
while
a
number
of
major
marketing
participants,
including
three
arrows
capital
and
celsius,
which
you'll
hear
me
mention
later,
have
filed
for
bankruptcy
in
the
most
recent
weeks,
marvin
moving
on
to
the
next
side,
so
in
the
u.s
sector,
we've
had
a
lot
of
different
stuff
going
on
again.
B
B
Now
we've
talked
previously
about
decentral
and
I
think
it
was
back
in
around
march
or
so
of
this
year.
In
fact,
we
had
a
whole
presentation
in
march
with
mark
d'angelo
talking
about
the
meta
universe,
jp,
morgan
and
other
companies
are
estimating
the
metaverse
to
represent
a
one
trillion
market
opportunity
so
you're,
starting
to
see
more
and
more
banks
actually
take
advantage
of
it.
B
B
Quatonic
claims
to
be
the
first
u.s
bank
to
offer
its
customers
a
bitcoin
rewards
program,
a
feature
the
bank
launched
in
2020,
and
they
also
claimed
to
be
the
first
bank
to
introduce
a
tap
to
pay
mobile
payment
ring
to
the
u.s
market,
a
product
which
they
officially
rolled
out
in
april
and
actually
had
to
go
research
and
find
out
what
that
was,
and
it's
literally
what
it
sounds
like.
It's
a
ring
that
allows
you
to
walk
up
to
a
kiosk
and
pay.
B
Much
like
you
do
with
tapping
your
phone
or
or
tap
in
a
credit
card.
What
was
kind
of
fun
about
this
is
to
mark
the
occasion.
Quater
bank
hosted
a
virtual
launch
party
for
the
space
which
they
included
a
dj.
They
had
non-fungible
token
giveaways
and
things
like
that,
so
people
are
really.
Companies
are
really
starting
to
take
advantage
of
the
metaverse
and
then
the
last
article
I've
got
for
today
is
talking
about
seven
different
companies
that
are
in
the
mortgage
and
dlt
industries.
B
So
this
article
was
just
recently
updated
again
in
june
of
this
year
and
it
talks
about
companies
like
liquid
mortgage
figure.
We
trust
that
we
previously
talked
about
throughout
this
year
and
our
presentations
a
couple
of
the
other
companies.
I
wanted
to
highlight
that
they
talk
about
there's
unchained
capital
out
of
austin
texas.
B
The
unchained
capital
holds
the
crypto
in
a
blockchain
secured
vault
requiring
permissions
of
both
the
borrower,
the
company
and
a
third
party
to
avoid
any
single
point
of
failure,
and
they
offer
loans
for
personal
small
business
as
well
as
real
estate
use
salt,
which
is
out
of
denver,
colorado,
they're.
Also,
a
company
that
use
blockchain's
flexibility
to
offer
cash
loans
that
leverage
digital
assets.
B
B
The
company
loans
started
a
5.5
apr
and
a
borrower
receives
all
of
their
crypto
back
upon
making
their
final
payment.
It'll
be
interesting
to
see
now
that
in
the
last
couple
weeks,
since
celsius
moved
into
bankruptcy,
what's
actually
going
to
wind
up
happening
with
that
company
and
what
we'll
see
them
doing
in
the
future
so
more
to
come
as
we
keep
an
eye
on
them.
B
And
just
a
quick
update,
this
is
our
wiki
page.
All
the
resource
articles
I
mentioned
are,
on
the
right
hand,
side
of
the
page.
In
fact,
if
you
scroll
down
the
page
even
farther
on
the
right
hand,
side
that's
where
you're
going
to
find
the
blockchain
information
and
research
section.
That's
where
you'll
see
that
cb
insights
data
blockchain
report
and
then
you
know
over
on
the
left
hand,
side
we
have
sub-links
in
our
groups
that
provide
information,
have
links
to
our
previous
meeting
recordings
as
well
as
the
presentation.
B
So,
if
you've
missed
out
on
any
of
those
previous
presentations
I
mentioned
earlier,
do
come.
Take
a
look
at
the
wiki
they're
all
accessible
directly
from
there,
and
in
fact,
for
everybody's
quick
reference.
I
will
go
ahead
in
a
moment
and
I
will
drop
the
wiki
into
the
chat
as
well,
so
you
can
save
it
as
a
favorite
link
and
marvin.
That
sums
up
my
update
for
the
month
back
over
to
you.
A
Next,
I'd
like
to
introduce
james
schoening
and
casey
rock
from
the
im
project,
james
leads
the
linux
foundation.
I
am
project
chairs
the
ieee
ontology
standards.
Working
group
is
an
and
is
an
advocate
for
self-sovereign
identity
and
personal
data
stores.
Casey
rock
is
a
computer
scientist
with
the
us
army,
futures
command
working
group.
Excuse
me
doing
cyber
security
r
d,
both
james
and
casey,
introduced
the
integrated
personal
data
store
in
a
previous
meeting
and
now
they're
going
to
talk
about
a
mortgage
specific
application.
So
I'm
going
to
turn
it
over
to
them.
C
C
C
So
we're
going
to
introduce
a
new
one
and
we're
going
to
propose
an
actual
pilot.
You
know
we
saw
marvin's
chart.
You
know
you
start
with
an
idea
and
at
some
point
you
get
to
where
you're
ready
for
a
pilot,
so
we're
going
to
be
proposing
a
pilot
and
to
just
give
you
a
quick
preview.
It's
a
pilot,
that's
user-centric!
C
Where
a
user
has
a
an
identity
wallet,
that's
that
also
stores
all
of
their
personal
data,
and
they
they
collect
this
data.
They
collect
credentials
and
eventually
you
know,
and
if
they
are
a
motivated
borrower.
You
know
if
they're
looking
to
take
out
a
mortgage,
they
collect
all
the
information
and
then
they
are
in
a
position
to
to
go
out
for
quotes
to
post
somewhere
on
the
internet.
I
am
interested
in
quotes
for
my
mortgage.
C
D
Yeah,
so
one
of
the
things
that
we've
realized
when
it
comes
to
these
different
types
of
blockchains
and
verifiable
credentials
is
that
you
realize
that
there
are
standard
ways
to
represent
the
metadata
or
the
header
data
of
these
verifiable
credentials,
and
this
is
going
to
be
talking
about
the
credential
id
that
the
timestamp
when
it
was
created
and
w3c
has
created
a
a
standard
for
that
now.
The
the
part
we've
realized
is
one
of
the
big
problems.
With
these
verifiable
credentials
is,
they
are
specifically
designed
for
the
model
that
the
issuer
uses.
D
D
C
C
Okay,
all
right
so
just
like
the
mortgage,
just
like
this
community,
has
a
vision
for
the
future
mortgage.
The
vision
we're
coming
from
is
that
the
individual
has
a
multi-purpose
wallet.
They
have
one
wallet
today,
there's
a
lot
of
wallet
projects,
but
for
every
project
you
get
it.
The
user
gets
another
wallet,
there's
no
general
purpose
wallets,
but
the
industry
is
moving
towards
that.
You
know
this.
The
interoperable
standards
which
we're
part
of
are
working
towards
the
vision.
C
A
person
has
one
one
digital
wallet
that
includes
both
their
it's
an
identity
wallet
for
their
credentials,
but
it's
also
a
personal
data
store
and
it
stores
their
data
and
the
data
is
integrated
and
I'll.
Explain
that
okay,
so
what
we
envision
is
when
a
person
is
ready
to
apply
for
a
mortgage,
they
already
have
most
of
what
they
need
in
order.
They
already
have
a
lot
of
the
data,
a
lot
of
their
identity.
C
You
know
maybe
they've
already
proven
their
income
and
who
they
work
for
and
where
they
live,
and
what
property
property
they
might
own.
They
already
have
most
of
it.
So
that's
going
to
make
it
a
lot
easier
and
and
then
when
they
need
a
mortgage,
if
they
have
all
this
data
and
if
there
are
standards,
because
the
data
has
to
be
collected
to
a
standard,
then
then
they
can.
C
C
C
C
You
know
the
small
scale
solution
to
that.
Is
you
do
a
mapping
from
one
application
application
to
another?
You
map
the
terms
in
a
data
model,
and
that
does
work,
but
it
just
doesn't
scale
because
the
more
and
more
applications
they
want
to
reuse
the
same
piece
of
data.
You
have
to
keep
mapping
it
and
it
actually
scales
at
an
n
squared
rate,
so
with
n
is
less
than
five
or
three
or
four
you're
okay.
C
But
if
n
gets
to
be
10,
it's
too
much
mapping,
you
really
need
a
common
hub
and
that's
where
that's
part
of
our
technology
now
the
hub
really
cannot
be
a
traditional
data
model
and
I'm
just
gonna
state
that
there's
no
time
to
prove
it.
If
anybody
wants
to
challenge
whether
a
traditional
data
model
could
ever
be
a
hub
for
any
kind
of
data,
I'd
be
happy
to
to
get
involved
in
a
discussion
on
that,
but
I'm
just
going
to
state
that
you
can't
be
a
traditional
data
model.
C
However,
it
could
be
what
we
call
an
ontology,
a
and
I'll
explain
that
in
the
following
chart:
ontologies
don't
model
data,
ontologies
model
reality
and
reality
actually
does
scale.
Now,
of
course,
when
you
model
it,
you
can
kind
of
get
the
different
perspectives,
but
at
least
it
starts
with
something
that's
scalable.
C
Unless
you
want
to
go
to
quantum
levels,
then
then
maybe
reality
is
not
what
we
think
it
is,
but
for
the
rest
of
us
reality
is
logically
consistent
and
it
does
scale
and
that's
why
these
ontologies
can
scale.
Ontologies
are
developed
in
a
hierarchy
at
the
top.
We
have
what
we
call
the
top
level
ontology,
and
this
is
something
that
just
in
the
past
year,
we
have
achieved
a
iso
standard
for
a
top
level
ontology,
it's
called
the
basic,
formal,
ontology
and
that's
kind
of
small.
C
It
just
really
gives
the
framework
for
more
specific
ontologies,
but
but
that
was
a
huge
leap
because
we
proved
we
actually
can
standardize
ontologies
and
that's
something
that
the
semantic
web
claimed
was
not
possible
20
years
ago
and
then
semantic
web
came
out.
They
said
you
should
never
have
standard
ontologies,
you
should
always
have
stovepipe
ontologies
and
just
connect
them
together,
and
I
think
by,
I
think,
we've
partially
proven
that
you
can
have
a
standard
ontology
and
many
people
can
reuse.
C
It
the
next
level,
is
the
mid-level
ontology,
and
this
is
it's
a
lot
bigger
about
1600
terms,
and
these
are
the
terms
that
are
commonly
used
across
domains
if
it's
specific
to
a
domain.
That's
the
third
level
down.
That's
where
you
get
a
domain
ontology,
but
if
it's
common
across
domains,
it's
what
we
call
a
common
or
a
mid-level
ontology.
There
is
one,
and
only
one
mid-level,
ontology
that
anybody
has
ever
tried
to
develop
and
that
was
developed
by
my
colleagues
and
I
within
the
us
government
and
over
the
past
ten
years.
C
I
think
we've
invested
about
seven
million
dollars
in
developing
that
mid-level
ontology.
It's
very
mature
still
needs
some
more
work,
but
it's
going
through
it's
going
through
standardization.
Now
we're
not
doing
it
through
iso,
because
we
want
to
invite
individuals
to
help
with
developing
that
and
iso
it's.
You
know
it's
more
of
an
organization
based
standards
and
you
have
to
pay
significant
amount
of
money
just
to
go
to
meetings
so
we're
doing
it
within
ieee
and
the
domain
ontologies
we're
doing
within
ieee
too
all
right.
C
So
so
that's
some
of
our
technology.
That's
that's
backing
up
and
what
we
call
an
integrated
personal
data
store
now
also
most
data
is
for
the
individual
is
not
going
to
come
from
the
individual.
You
know
it's
going
to
come
from
other
organizations
like.
If,
if
you
want
to
prove
what
your
annual
income
is,
you
don't
get
to
make
up
that
number.
You
have
to
get
that
from
your
employer
or
maybe
from
the
irs.
C
So
vendors
are
going
to
create
most
of
this
data
so
that
data
is
going
to
come
out
of
disparate
data
sources.
So
what
with
this
these
common
ontologies
any
any
structured
data
can
be
mapped
to
these
standard
ontologies
and
then
once
you
map
them,
then
the
instance
data
can
be
transformed,
and
then
you
can.
This
data
then
fits
together.
D
D
So
the
whole
picture
that
we
were
presenting
and
talking
about
in
the
slides
was
that
there
are
standard
ways
to
issue
out
credentials.
In
this
case,
we
have
three
credentials
inside
of
our
issuers
template
now.
What
they
do
is
they'll
offer
a
credential
by
typing
in
different
types
of
values
for
the
holder.
D
Now,
in
this
case,
if
you
notice,
we
have
we'll
come
to
the
the
credential,
the
credential
has
standardized
details,
so
the
schema,
the
credential
id,
whether
or
not
it's
revocable,
but
when
we
get
to
the
attribute
level.
This
is
where
we
start
talking
about
the
individual's
data.
There's
no
standard
way
of
representing
this.
So
this
causes
the
problem
where,
if
multiple
issuers
are
sending
credentials
each
pointing
to
the
same
value,
it
could
be
street
address,
but
if
the
credential
references
that
attribute
a
different
way,
the
data
doesn't
match.
D
D
D
D
So
as
you
notice,
the
values
are
going
to
be
very
similar
across
multiple
credentials.
You
know
if
my
first
name
in
this
example
is
jane.
I
may
have
four
different
verifiable
credentials
that
reference
that
same
value
but
use
a
different
attribute
name
other
than
first
name.
It
could
be
user
first
name,
first
name
name
but
we're
all
referencing
the
same
data,
the
same
data
point,
which
is
the
you
know,
the
name
jane.
D
D
D
D
D
D
Now,
I
think,
what's
important
to
note
is
that
this
is
truly
the
center
of
the
data
model,
because
each
connection,
each
representation
between
the
individual
and
the
data
that
they
receive
has
a
defined
relationship.
So
in
this
example,
we
have
a
relationship
between
the
individual
and
their
full
name.
D
D
So
from
this
view,
we
showed
that
we
can
represent
any
type
of
data
that
we
need
to.
More
importantly,
I
want
to
talk
about
this
relationship
between
the
person,
this
red
node
and
this
yellow
node,
which
is
the
active
ownership,
so
we've
represented
data
that
shows
how
the
individual
participates
in
this
action,
which
is
this
red
node
right
here
we
represent
and
the
action
has
an
object
which
is
the
digital
wallet.
D
D
Now,
if
we
break
this
further,
we'll
show
how
the
digital
wallet
has
verifiable
credentials
in
it,
and
these
verifiable
credentials
track
right
back
to
the
values
these
tan.
Looking
boxes
are
going
to
be
the
values
of
the
attributes,
so
the
important
part
to
show
here
with
all
of
these
nodes
and
edges
going
along
on
this
graph
structure,
is
that
we
can
now
represent
what's
really
happening
in
reality,
when
a
person
has
a
certain
attribute
and
a
verifiable
potential
maps
to
it,
bringing
us
back
to
this
picture
right
here
when
we
abstract
out
the
data
structure.
A
A
Names
of
the
data
fields
to
what
extent
yeah
that's
the
exact
screen.
I
was
talking
about
first
name
and
last
name.
To
what
extent
have
you
guys
mapped
those
attributes?
First
name
last
name
to
the
mismo
naming
standards,
because
everything
that
we
do
in
mortgage
or
what
we
hope
to
do
when
mortgage
is
really
starting
to
standardize
around
mismo
and
their
naming
standards.
D
Yeah,
absolutely,
I
think
one
thing
that
we
we've
done
is
our
ontology.
The
way
it
maps
to
different
data
models
like
mismo
is
is
practically
you
find
a
an
attribute
inside
of
a
datum
also,
for
example,
the
way
mismo
represents
first
name
and
it'll,
be
common
among
any
representation
of
first
name.
So
I
think
that
the
big
important
part
that
I
want
to
make
here
is
that
we're
not
our
ontology
will
not
be
a
you
know
a
mismale
ontology.
It
won't
be
a
my
data.
D
Entology
it'll
be
just
a
picture
of
reality,
so
I
think
we
could
probably
say
we're.
Probably
at
a
you
know,
20
coverage
rate
with
the
terms
we
currently
have,
but
since
ontologies
are
very
flexible
and
you
can
use
a
combination
of
terms
to
represent,
you
know
a
single
term,
it
allows
us
to
cover
a
lot
more
data
inside
these
data
models.
So
that's
kind
of
my
my
best
answer.
Jim
did
you
want
to
add
anything
to
that.
C
C
All
right,
can
you
see
my
last
slide
here.
Pilot
proposed
pilot
okay,
so
this
is
a
way
in
which
we
can
work
together
here,
and
so
our
concept
is
we're
proposing
that
we
do
do
a
pilot
in
which
a
a
real
person.
This
is
not
a
just
a
tech
demo.
Now
we
would
just,
of
course,
do
these
things
in
a
tech
demo
fashion
before
the
real
person
is
is
introduced
to
to
it.
But
but
the
pilot
would
be
a
real
person.
C
We
would
need
to
recruit
a
person
who
is
planning
to
apply
for
a
mortgage
and
probably
not
somebody
who
needs
to
get
that
mortgage
in
the
next
month.
Probably
somebody
who's
planning
for
it
in
the
next
three
to
six
months,
so
we'd
have
to
recruit
somebody
like
that
and
then
the
team
that's
going
to
support
this.
You
know
casey
and
I
would
be
on
that
team.
C
C
Then
we'd
also
have
to
recruit
some
well
okay,
so
the
borrowers
this
real
person
would
then
gather
their
real
data
and
credentials,
and
they
would
do
it
privately.
Even
though
this
is
a
pilot,
they're
not
gonna
share
their
data.
Unless
you
know
it's
in
their
interest
to
share
their
data,
probably
what
we
would
do
is
we
would
have
a
parallel.
C
C
They
might
not
be
looking
for
it,
but
we'd
go
recruit
some
lenders
that
would
agree
to
try
to
give
this
person
a
quote
for
their
for
their
mortgage
and
but
the
lender
would
define
their
terms
and
conditions,
especially
when
it
comes
when
it
comes
to
sharing
their
data.
You
know,
and
it
would
be
basically
I'm
sharing
my
data
for
you
to
give
me
a
quote,
but
you
don't.
C
You
do
not
keep
a
copy
of
my
data
and
you
certainly
can't
share
it
or
sell
it
or
anything
like
that,
and
then,
if
the
lender
agrees
to
the
terms
and
conditions,
the
borrower
would
then
share
the
data
and
credentials
and
and
then
the
lender
would
would
map
this
data
to
their
applications.
Now,
my
guess
is
a
lender
has
as
an
application
they
plug
in
the
information,
and
it
comes
back
and
computes
whether
it's
a
good
good
loan
or
not.
C
But
if
this
were
to,
you
know,
take
off
that
mapping
could
be
done
in
advance,
so
it
could.
Theoretically
quotes
could
theoretically
be
given
automatically
based
on
input
data,
or
at
least
a
preliminary
quote,
so
that
that's
the
type
of
pilot
we
think
we
could.
You
know
help
if
we
could
recruit
some
participants
and
that's
our
offer
and
it's
potentially
a
way
that
this
could
help
this
ecosystem
evolve,
because
some
mortgage
borrowers
are
highly
highly
motivated
to
get
a
mortgage.
C
I
mean
my
first
mortgage
was
in
1984
and
when
interest
rates
were
in
the
upper
teens
and
my
state
had
some
state-backed
mortgages,
which
I
stood
in
line
overnight,
to
get
a
state-backed
more,
I
was
highly
motivated
to
get
a
mortgage.
If
I
was
told
I
got
to
collect
a
bunch
of
data
credentials,
no
problem,
I
would
have
done
that,
so
it
could
contribute
to
the
you
know
this
vision
of
an
ecosystem.
A
Okay,
jim
casey-
I
I
think
you
guys
are
looking
for
a
couple
things.
You
want
a
real
person
or
several
real
people
to
go
through
this
process
to
set
up
a
a
digital
wallet
and
then
to
go
through
the
mortgage
application
process,
but
you're
also
looking
for
lenders
or
mortgage
companies.
That
would
be
willing
to
accept
those
credentials
as
part
of
the
mortgage
application
process
or
have
you
guys
already
identified
those
types
of
companies.
A
Okay,
okay,
so
then
you
are
proposing
a
pilot
that
would
where
there
are
going
to
be
real
people
and
they're,
going
to
be
lenders
with
a
loan
origination
system.
That
would
accept
the
credentials
that
you
guys
would
identify
where
you
guys
would
develop.
That's
what
you
guys
are
looking
for
in
your
pipe.
C
Yes,
and
and
the
first
implementation
that
that
mortgage
lender,
you
know,
is
going
to
get
these
data
and
credentials
and
they're,
probably
going
to
manually,
have
to
replug
it
into
their
applications
or
whatever
for
the
pilot
purposes.
But
theoretically
that
could
all
be
automated.
You
know,
because
these
these
ontologies,
this
data
can
be
mapped
to
any
other
app.
You
know
unique
business
application.
A
Okay,
well,
there
are
several
lenders
and
quite
a
few
people
that
work
with
lenders
that
are
a
part
of
this
group,
jim
casey,
if
you
guys,
could
put
your
contact
information
in
the
chat
and
then
for
anyone
on
the
call,
anyone
that
downloads,
this
recording
and
it's
interested
in,
participating
or
helping
out
your
pilot.
I
would
encourage
them
to
reach
out
to
you,
either
as
a
real
person
taking
getting
mortgage,
quotes
or
being
of
one
of
the
lenders
that
would
provide
those
quotes
and
help
process.
The
mortgage
application.
A
Okay,
great,
thank
you
guys
for
walking
us
through
this
information
and
this
pilot.
I
think
I
I
think
it
leverages
the
type
of
blockchain
technology
and
use
case
that
we
advocate
for
in
our
subgroup,
and
I
think
this
is
great
technology
that
is
going
to
be
useful
and
it's
just
getting
a
pilot
out
there.
I
think
that's
usually
the
hardest
part.
So
if
there's
anyone
that's
interested
or
can
help
you,
I
encourage
them
to
reach
out
to
you.
A
B
A
Great
okay,
thanks
again
guys
and
I'm
going
to
go
to
the
next
slide,
so
going
back
to
future
agenda
topics,
we
do
have
someone
lined
up
for
our
october
13th
meeting.
This
is
going
to
be
david
fitzgerald,
he's
going
to
talk
about
property
records
and
a
smart
contract
project
that
he
did
in
wise
county.
A
I
think
this
may
have
been
something
that
we've
covered
or
briefly
mentioned
in
an
earlier
meeting,
but
david
will
be
able
to
walk
through
us
through
this
project
in
a
lot
more
detail,
I
think
it's
another
example
of
a
great
use
case
for
blockchain.
So
hopefully
you
guys
can
join
that
the
meeting
after
that
november
10th
we're
contemplating
a
blockchain
panel
discussion
or
another
demo
from
another
blockchain
los
provider.
I'm
still
trying
to
finalize
that
and
then
december
8th.
A
So
those
are
our
future
agenda
topics
if
there's
anything
that
the
people
on
this
call
are
interested
in
or
would
like
to
discuss.
Please
let
us
know
either
in
the
chat
or
shoot
us
an
email.
We
want
to
make
this
as
interactive
as
possible
and
also
support
the
interest
of
the
old
entire
team
with
that.
That
brings
us
to
the
end
of
our
session.
I
want
to
open
it
up
to
any
questions.
A
Okay,
if
there
aren't
any
questions,
I
I
show
9
48.
Thank
you
to
everyone
for
joining
this
call.
This
call
has
been
reported
and
will
be
available
in
the
next
couple
days
when
the
hyperledger
team
converts
it
and
posts
it
onto
the
youtube
link.
Thank
you.
Everyone
for
joining
us
and
have
a
great
rest
of
your
day.