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From YouTube: Structured Finance Admin on Blockchain -Siddhartha CEO Intain - Capital Markets SIG APAC timezone
Description
Siddhartha the Founder and CEO of Intain shared the great work/success they have had with their Structured Finance Administration system using #hyperledger Fabric which has been in production since 2020 and now administrating over US$5bn of traditional institutional assets.
Structured Finance is more than a $2T industry that has suffered from information asymmetry and layers of cost, as the lender, issuer, servicer, rating agency, trustee and investor share data.
Intain brings all these parties on a common Hyperledger Fabric based platform, for one version of truth and then uses smart contracts to model all deal features, resulting in an integrated and an automated structured finance administration platform.
A
Okay,
welcome
everybody
to
this
asiatime
capital
market
special
interest
group
from
hyperledger.
We
have
an
amazing
speaker
today,
I'm
very
happy
to
introduce
to
sartha
who
has
been.
I
think
I've
seen
in
the
blockchain
community
since
way
back
and
has
been
building
this
company
he's
the
ceo
in
team
and
I'm
going
to
hand
it
over
to
him
just
before
I
do
just
two
quick
things
as
in
this
is
a
linux
foundation
meeting
I
have
to
remind
everyone
that
we
do
have
an
anti-trust
policy
and
we
also
have
a
code
of
conduct.
A
B
Thanks
julian
good
to
be
again
part
of
a
hyperledger
session,
I
will
spend
maybe
20
25
minutes
on
the
presentation,
but
in
between
also
if
there
any
questions
happy
to
take
them.
Otherwise,
let's
just
spend
some
time
discussing
at
the
end,
because
since
I
don't
think
the
presentation
is
going
to
last
that
long,
I
don't
think
you
all
expect
that
to
last
that
long,
either
perfect
yeah.
B
So
let's
just
first
look
at
the
problem
which
we
were
attempting
to
solve,
so
the
structured
finance
industry,
a
is
too
big
and
too
critical,
and
the
metrics
of
that
metric
of
it
being
too
big
and
too
critical,
or
one
of
the
proof
points
of
it
being
too
big
and
too
critical
is
that
it
was
big
enough
and
critical
enough
that
it
could
pull
the
world
economy
down
in
2007
right.
So
the
first
thing
to
look
at
is
how
big
and
how
critical
the
structured
finance
industry
is
to
the
world
economy.
B
So
it
is
a
very
big
and
very
critical
industry
where
everybody
is
interacting
via
excel
sheets
and
emails,
and
the
result
is
that
an
investor
has
a
clear
view
into
his
or
her
portfolio
about
six
week
past
the
event.
So,
for
example,
around
15th
of
june,
an
investor
will
get
a
report
about
may,
which
means
that
if
something
happened
on
first
of
may,
the
investor
is
getting
a
picture
of
that
occurrence
or
something
that
happened
to
the
assets
underlying
that
security
on
15th
of
june.
B
About
six
weeks
later,
so
it's
non-transparent
and
inefficient
and
then
the
question
is
that
why
this
could
not
be
solved
for
so
long
now?
One
easy
way
of
solving
any
such
problem
is
that
if
I
can
have
one
database
across
all
these
parties,
which
is
the
lender
servicer
trustee
issuer
investor
rating
agency,
then
I
can
automate
any
function.
Everybody
feeds
off
the
same
database
and
it
would
work
what
in
it
industry.
B
So
then,
if
you
see
it's
a
big
and
critical
industry,
it's
non-transparent
and
inefficient,
and
the
only
way
we
could
solve
this
problem
was
by
bringing
this
industry
onto
a
blockchain,
which
then
ensures
that
there
is
one
version
of
truth
across
all
these
parties,
and
then
we
can
write
smart
contracts
to
automate
all
the
interactions
between
them.
So
that
is
really
the
crux
of
the
problem.
That
in
pain
is
trying
to
solve.
B
Now
this
in
a
way
summarizes
each
of
the
problems
that
we
face,
which
is
siloed
data,
because
everybody
is
working
on
their
own
systems,
layered
costs,
because
every
transaction
has
to
be
reconciled
and
you
keep
adding
more
and
more
layers
of
opacity
and
with
each
layer
of
opacity.
You
are
adding
a
layer
of
cost.
All
these
processes
are
manual
and
we
talked
about
transparency.
B
B
Now
good
thing
for
us
in
structured
finances
that
the
structured
finance
industry
significantly
consolidated
with
the
trustees
for
people
who
have
understanding
of
fund
admin
or
fund
management
industry?
It's
very
similar
to
how
the
fund
admin
fund
management
industry
is
consolidated
with
custodians
right,
so
it
could
be
bny
ml
and
state
street.
Each
of
them
could
be
around
30
trillion
dollars
and
then
jp
morgan.
Northern
trust,
so
within
first
four
or
five
of
the
custodians
you
would
have
covered
30,
35
percent
of
the
market.
B
B
B
A
couple
of
points
about
technology.
I
am
not
an
expert,
but
our
broad
approach
has
been
to
solve
an
industry
problem,
not
be
a
blockchain
solution,
which
means
that
I
decide
that
okay,
these
are
seven
different
entities
I
have
to
bring
on
to
the
network,
then
I
start
looking
at
each
entity's
pain
point.
B
So
if
the
custodian
or
the
verification
agent
has
to
come
on
to
my
blockchain,
so
that
the
original
loan
pool
that
has
to
be
securitized
is
verified,
the
best
option
for
me
is
to
op
give
him
an
option
of
completely
automating
the
due
diligence
on
a
loan
pool.
Now
that
requires
artificial
intelligence.
Computer
vision
nlp
to
be
able
to
read
loan
contracts,
and
hence
the
solution
is
not
blockchain.
Solution
is
ai,
so
you
provide
an
ai
solution,
for
the
investor.
Investor
is
used
to
really
state
of
our
data
visualization
and
analytics
tools.
B
It
is
to
tell
them
that
we
have
a
better
software
solution
than
whatever
you
are
used
to
and
yes,
then,
the
additional
benefit
is
that
we
are
eliminating
all
reconciliation.
Everything
is
immutable,
etcetera,
etcetera
because
of
the
blockchain.
So
that
is
one
big
difference
we
had
in
terms
of
the
approach,
and
we
have
taken
this
approach
and
a
lot
of
things
so,
for
example,
onboarding
to
onboard
data.
Yes,
it's
good
that
everybody
is
a
node,
but
if
they
are
not
a
node,
we
have
provided
for
excel
sheets
to
be
uploaded.
B
We
have
provided
for
api
integrations
for
data
to
come
in
on
the
output
side.
Yes,
it's
good
that
if
the
investor
is
a
node,
but
if
the
investor
is
not
a
node
and
they
are
habituated
to
asking
their
admin
to
print
a
pdf
of
a
report,
we
have
provided
a
pdf
creator
for
that
report
online.
So
it's
really
small
small
things
which
ensures
that
any
resistance
to
adoption
is
eliminated.
B
And,
in
summary,
what
it
has
done
is
that
it
has
looked
at
four
or
five
of
the
key
problems
of
the
industry
and
we
have
solved
that
if
you
look
at
them
even
as
a
point
solution,
so
the
due
diligence
on
loan
documents
by
a
verification
agent.
I
talked
about
standardization
of
the
servicer
loan
tape,
which
is
a
big
issue
where
we
looked
across
the
world.
The
best
practice
was
the
esma
regulations
in
europe.
We
made
it
as
part
of
the
standard
platform,
so
we
solved
for
the
loan
tape.
B
Standardization
problem
deal
modeling
and
payment
waterfall.
We
do
using
smart
contracts,
that's
another
problem
we
solved,
and
then
I
talked
about
data
visualization
and
reporting.
So
if
you
just
ignore
that
there
is
a
blockchain
underneath
as
a
software
solution,
it
is
solving
some
of
the
biggest
problems
faced
by
the
structured
finance
industry.
B
I'll
just
spend
30
seconds
each
on
some
of
the
points
that
I've
talked
about.
So
this
is
the
solution
using
artificial
intelligence
to
actually
read
a
loan
pool,
so
it's
basically
using
ai
to
automate
due
diligence
in
a
structured
finance
transaction
and
that
is
eliminating
almost
90
percent
of
the
effort
which
verification
agents
were
putting
in
and
what
it
does.
Then,
is
that
when
I
am
on
boarding
a
loan
pool
onto
the
blockchain,
it's
like
what
the
blockchain
industry
would
call
an
oracle.
B
We
take
from
the
actual
indenture,
which
is
the
contract
for
a
structured
finance,
transaction
and
model
every
single
term
as
a
smart
contract.
So
generally,
these
structured
finance
pools
are
divided
into
multiple
tranches.
Stop
and
the
safest
tranches
may
be
invested
into
by
pension
funds
and
insurance
companies.
Bottom
tranches
may
go
to
hedge
funds,
etc.
Each
of
them
have
their
own
terms.
There
are
different
trigger
events.
B
But
here
we
pull
the
data
from
the
blockchain
into
an
analytics
data
store
and
then
run
a
standard
data
visualization
and
analytics
tool
on
top.
So
then,
what
the
investor
is
getting
is
a
plus
b,
which
means
that
how
much
payment
that
needs
to
be
made
to
each
investor
and
how
trigger
events
and
all
deal
terms
are
done.
They
are
being
calculated
using
smart
contracts
and
hence
they
are
immutable,
auditable,
etc,
but
that
is
still
not
constraining
the
investor
from
doing
on-demand
analytics,
which
otherwise
would
not
be
possible
just
with
smart
contracts.
B
So
investor
can
and
means
it's
like
traditional
data.
Visualization
tools
create
any
slice
and
dice
of
data,
because
we
have
pulled
the
data
out
of
the
blockchain
into
a
data
store.
They
couldn't
have
done
it
if
it
was
still
in
the
blockchain,
this
automation
of
the
models,
and
then
there
are
other
entities
who
were
consuming
this
data
by
sourcing
data
from
let's
say
the
trustee
or
the
servicer
like
the
rating
agency.
B
Now
the
rating
agency
just
has
to
login,
and
it
has
verified
data
available
in
real
time.
So
for
those
of
you
who
have
seen
the
big
short
movie,
which
is
about
the
problems
of
the
industry,
you
can
see
that
this
is
how
it
solves
each
of
the
problems
which
was
highlighted
by
that
movie,
including
the
data
that
the
rating
agency
is
getting
because
now
rating
agency
is
seeing
exactly
the
same
picture
that
the
issuer
is
seeing
that
the
investor
is
seeing
or
anybody
else
is
seeing
in
the
industry
right.
B
So
in
summary,
these
are
all
the
parties
which
are
being
brought
onto
a
common
fabric
based
platform
which,
because
of
the
nature
of
blockchain,
there's
nothing
unique
that
we
are
doing
which
ensures.
There
is
one
version
of
truth,
then
there
is
complete
process
automation,
some
which
is
kind
of
routine
software
process,
automation,
the
other
through
smart
contracts.
B
There
is
transparency
and
compliance.
I
explained
to
you
that
the
whole
deal
terms
is
automatically
getting
calculated
using
smart
contracts
and
this
platform,
which
we
call
intane
admin
intent.
Admin
will
continue
to
run
on
hyperledger
fabric.
We
are
creating
a
replica
of
intane
admin
in
avalanche
to
create
a
tokenized
layer.
On
top
of
that
which
we
call
intane
markers.
B
And
as
I
said
that
from
a
time
when
you
did
a
200
million
dollar
transaction
on
a
blockchain
platform-
and
you
went
and
did
a
press
release
about
it,
we
have
brought
it
to
a
stage
where,
almost
every
month
we
are
on
boarding
two
or
three
transactions
onto
a
our
platform.
Each
of
them
would
be
anywhere
between
100
to
300
million
dollars.
So
this
is
a
slightly
older
version
of
the
presentation,
so
we
are
close
to
about
5
billion
in
assets
under
admin
on
the
platform
about
1718
transactions.
B
And
this
is
what
I
was
mentioning
that
there
are
across
industry
use
cases.
There
are
very
few
blockchain
platforms
which
are
getting
more
out
of
a
smart
contract
than
we
are
getting
and
simply
the
reason
is
structured.
Finance
industry
has
the
most
complex
structures
and
we
are
modeling
the
whole
contract
with
smart
contracts.
So
all
the
terms
that
you
see
for
each
of
these
deals-
and
these
are
actual
deals
running
on
our
platform-
all
these
calculations
for
each
of
these
deals
are
being
done
executed
through
smart
contracts.
B
B
What
any
tokenization
platform
does
is
just
takes
this
last
step,
so
everything
is
done.
There
is
a
token
ready
to
be
created,
you
create
a
token
and
then
offer
it
to
investors
now
blockchain
is
pretty
useless
if
all
that
you
are
doing
with
the
blockchain
is
to
create
a
token
at
this
last
step,
because
the
problem
is
not
at
this
last
step.
Yes,
tokenization
ensures
liquidity,
but
the
precursor
to
liquidity
is
trust
and
transparency
and
tokenization
in
itself.
So
we
do
not
know
any
more
about
a
token
on
blockchain.
B
Then
we
would
know
about
that
instrument
of
a
blockchain
just
because
it's
a
token
right
and
hence
the
bigger
problem
is
here,
but
nobody
attempts
to
solve
it
for
two
reasons:
one.
It
is
not
as
remunerative
because
you
get
trading
volumes
on
tokenizations
and
second,
this
is
not
cross
leverageable.
So
when
I
say
cross
leveragible,
it
means
that
you
can
create
a
token
platform
where
you're
issuing
real
estate
tokens
structured
finance
tokens
any
kind
of
tokens,
because
all
that
you
care
is
create
tokens
and
put
it
on
a
platform.
B
The
admin
is
very,
very
asset
class,
specific,
so
even
an
asset-backed
security
to
mortgage-backed
security.
It's
not
that
easy
to
replicate,
and
hence
it's
a
very
complex
structure
for
administration
of
each
asset
class
that
you
have
to
create,
and
that
is
why
we
have
spent
almost
four
years
20
to
25
developers
at
one
point
in
time.
Solving
this
problem
in
structured
finance
that
get
this
part
of
the
industry
onto
a
blockchain.
B
The
last
part
will
automatically
take
care
of
itself
and,
as
I've
shown
with
the
numbers
on
assets
under
admin,
number
of
kinds
of
deals,
etc
that
we
have
on
the
platform.
It
does
prove
that
that's
a
successful
approach,
and
next
month
we
would
be
launching
in
10
markets.
So
yeah,
that's
about
it.
This
is
the
last
slide
to
show
what
our
vision
is.
As
I
said
that
deals
under
100
million
dollars
are
unviable.
B
B
The
fact
that
some
of
the
intermediaries
in
the
industry
are
willing
to
share
fifty
five
zero
percent
of
their
fees
with
us,
which
means
that
at
least
seventy
80
percent
of
the
transaction
has
to
get
automated
for
somebody
to
say
that
50
of
the
fees
that
they
will
give
to
us.
So
these
are
already
proof
points,
and
this
is
really
the
end
goal
in
this
so
yeah.
So
that's
kind
of
the
presentation
happy
to
take
any
questions.
C
Sorry
I
was
a
couple
of
minutes
late,
but
I'm
here
now
anyway
great
great
presentation,
you
know
the
heavy
lifting
that
is
associated
with
the
process,
automation,
which
is
the
main
point
that
you're
making
in
your
and
the
success
that
you're
having.
But-
and
you
mentioned
one
thing
in
the
end-
which
is
that
a
lot
of
these
are
bespoke.
C
That
means
you
you,
you
know
you've
created
something
that
is
specific
to
structured
finance,
but
there
are
elements
of
it
which
are
common
across
I
mean
you,
you
showed
the
mortgage
platform.
For
example,
you
know
there
is
a
prospectus
in
mortgage
which
is
very
similar
to
the
to
what
you
sort
of
automate
in
terms
of
reading
the
document
with
with
a
ai
and
then
taking
the
deal
structure
and
then
automating
it
in
in
smart
contracts.
C
C
B
Yeah
so
syndicated
loans-
you
talked
about
so,
for
example,
in
clo,
so
most
of
what
you
saw
is
mostly
cdos.
So
in
close,
the
role
is
of
a
collateral
agent
who
holds
the
collateral
right.
So
at
the
conceptual
level.
Yes,
there
are,
I
think,
in
u.s
it's
called
lsda.
They
are
lsta
structured
documents
of
each
of
these
loans
which
need
to
be
read
and
those
are
collaterals
which
need
to
be
maintained.
B
B
B
So
in
implementation
it
gets
very,
very
complex
if
you're
trying
to
administer
more
asset
classes
and
that's
not
only
for
structured
finance.
If
you
see
traditionally-
and
I
ran
jv
with
state
street
as
a
ceo
in
state
street
or
bank
of
new
york,
which
are
biggest
fund
administrators
in
the
world,
how
they
administer
fixed
income
funds
is
completely
different
from
how
they
administer
equities
funds.
The
software
systems
are
different,
the
teams
are
different.
Everything
is
different,
so
at
the
administration
level,
it's
very
very
difficult
to
create
commonalities
across
asset
classes,
whether
blockchain
or
otherwise.
A
A
B
Yeah,
it's
one
thing
is
that,
yes,
it
pushes
the
envelope
in
terms
of
what
you
can
do
with
smart
contracts.
So
when
we
started
or
even
till
second
third
year
when
we
used
to
struggle,
we
used
to
talk
to
folks
at
ibm
and
they
would
advise
us
against
using
smart
contracts.
For
these
complex
deal
structures,
they
said
in
fabric
or
anywhere
smart
contracts
were
never
created
for
you
to
take
a
200
page
contract
with
so
many
terms
and
convert
everything
into
a
smart
contract.
D
D
So
if,
if,
if
I'm
correct,
is
that
from
your
solution,
I,
if
I'm
one
of
the
counterparty
between
for
the
structure,
finance
as
a
client,
so
I
may
not
necessarily
be
dealing
or
create
with
the
counterparty
in
anything
which
is
related
to
in
a
tokenized
form.
So
I
will
continue
the
way
I'm
doing
the
business
it
may
be
on
excel
or
in
the
document
which
I'm
creating
it
once
the
deal
is
done.
D
B
B
D
B
D
D
So
if,
if
I'm
trying
to
compare
it's
something
which
is
a
typical
otc
derivatives
market
for
that,
for
example,
if
is
that
a
good
similar
case
study
or
case
use
case,
which
can
have
a
similar
platform
which
can
be
built
around
that?
But
as
what
I've
understood
from
you,
because
every
deal
may
be
done
through
a
different
counterparty
and
and
the
ssi
basically
settlement
instruction
would
also
be
different
between
these
two
counterparties.
D
B
Yeah,
so
the
use
of
trustees
is
less
because
there
is
a
common
party
to
which
is
interacting
with
everybody.
For
example,
trustees
don't
really
interact
with
rating
agencies.
Trustees
is
more
in
terms
of
our
gtm,
our
go
to
market
right
that
the
trustee
is
allowing
me
to
access
10
percent
of
the
market
in
one
go.
So,
for
example,
if
I
were
to
shut
my
shop
today
means
I
reduced
intent
to
a
two
employee
company.
B
Inten
will
still
continue,
onboarding
deals
because
that
trustee
is
10
percent
market
share
and
that
trustee
will
keep
selling
more
and
those
deals
will
keep
getting
onboarded
and
once
the
deals
are
on
board,
then
all
parties
who
are
involved
in
that
transaction
will
keep
getting
onboarded
on
my
platform.
So
this
schematic
that
you
saw
about
trustees
being
hub
is
less
of
how
a
transaction
happens.
It's
more
about
our
go
to
market
that
we
have
just
right.
So
yeah.
B
D
B
Yeah
so
as
of
now,
because
they
said,
trustees
are
part
of
every
deal,
so
the
deals
would
come
to
the
trustee,
and
if
the
trustee
has
moved
to
our
platform,
then
deals
would
by
default,
move
to
our
platform.
So
we
are
not
doing
any
selling
for
individual
deals.
The
deals
are
being
onboarded
to
our
platform
by
default,
as
long
as
we
can
get
trustees
to
realize
that
it's
in
their
interest
to
move
to
our
platform.
A
C
Yeah,
so
we
were
taught
you
know,
you're,
basically
saying
how
the
go
to
market
and
deals
get
created
now.
The
second
part
of
it
would
be
how
a
secondary
market
evolves
from
this,
and
also
the
performance
of
the
deal
which
you
obviously
becomes
important.
At
that
point,
it's
like
the
mortgage
in
florida,
the
guy
is
able
to
pay
every
month
or
was
he
late
or
she
late
on
the
mortgage
payment
that
becomes
important
for
the
secondary
market,
a
secondary
market?
C
C
B
Yeah,
so
we
for
ourselves
also
have
been
doing
a
lot
of
experiments
on
this.
Obviously,
everybody
who's
trading,
a
token
in
secondary
market
is
not
going
to
do
the
kind
of
drill
down
that
we
provide
to
an
institutional
investor
and
a
routine
course.
Obviously
they
will
have
access
to
this
level
of
analytics.
B
So
that's
just
at
conceptual
and
software
level,
as
we
launch
in
10
markets,
we
do
not
intend
to
make
the
tokens
to
be
tradable,
so
they
will
be
yield
generating
tokens,
at
least
for
next
9
to
12
months,
because
we
want
to
be
absolutely
sure
about
regulations
before
we
offer
them
into
the
secondary
market.
So
and
that's
why
I
wanted
to
divide
into
two
parts
at
conceptual
and
software
level.
B
We
have
experimented
with
aggregating
this,
creating
machine
learning
based
models
so
that,
even
if
the
investor
who's
trading
in
these
tokens
in
the
secondary
market
is
not
going
to
be
spending
10
15
minutes
drilling
down
all
the
data
that
we
provide
them.
They
still
have
an
nysct
kind
of
thing
about
each
pool,
but
the
real
implementation
of
this
is
at
least
a
year
away,
because
we
are
not
going
to
allow
our
tokens
to
be
traded
for
now.
C
B
I
have
to
figure
out
what
is
the
implication
of
covet
on
my
pool.
The
implication
of
covet
is
related
to
where
the
borrower
is
implication
of
property.
Price
dropping
is
related
to
where
the
property
is
and
the
borrower
and
the
property
could
be
in
two
different
states.
Now
these
are
things
nuances.
We
have
realized,
as
we
have
started
tracking
the
pools
on
our
platform,
because
that
is
the
level
of
granularity
to
which
we
can
track
data
now.
B
So
you
can
really,
as
I
said,
go
into
every
single
loan,
every
single
repayment
of
every
single
loan
and
all
the
collateral
associated
with
that
loan.
In
fact,
we
did
a
small
pilot
in
india
on
a
5000
motorbike
loan
pool
on
which
we
even
tested
out
things
like
whether
the
vehicle
is
hypothecated,
not
hypothecated
and
sure
not
insured.
Is
there
a
correlation
on
the
pool
performance
based
on
these
properties
so
that
level
of
data
is
available.
A
So
basically,
the
last
the
last
financial
crisis
happened
because
of
a
liquidity
crisis
which
is
based
on
people,
not
knowing
whether
those
mortgage-backed
securities
really
had
mortgages
that
really
were
working
and
which
tranche
would
this
solve
that
problem,
so
not
so
mitigate
that
problem.
When
we
have.
B
Will
create
far
yeah
earlier
warning
signals.
So,
as
I
said,
the
easy
thing
is
to
think
of
the
big
shot
movie
and
think
of
how
much
of
what
you
see
in
that
movie
is
related
to
information.
Asymmetry
right
means,
everybody
has
their
own
versions
of
truth
and
everybody
is
doing
their
things,
and
so
only
as
a
movie
viewer,
you
realize
that
everybody
is
doing
their
own
thing
now.
That
is
something
that
the
platform
completely
eliminates,
that
everybody
knows
exactly
the
same
data,
so
that
would
create
so
that
property
market
is
crashing.
B
People
are
not
able
to
pay.
That
is
not
something
that
the
platform
can
address,
but
you
cannot
have
a
situation
that
I,
as
an
investor,
realize
that
my
pool
became
really
defunct
three
months
back
or
four
months
back
when
the
florida
real
estate
market
crashed
that
won't
happen.
That
will
become
real
time.
B
C
B
Underlyings
are
in
case
of
mbs,
obviously
mortgages
within
mod
gauges,
because
us
is
a
funny
country
where
there
are
all
kinds
of
structures
with
so
within
mod
gauges.
There
are
fixed
and
flip
loans
which
have
their
own
deal
structures.
There
are
helocs,
which
is
home,
equity
line
of
credits
which
have
their
own
structure,
so
we
have
multiple
structures
within
residential
mortgages,
commercial
mortgages
in
asset-backed
securities
we
have
auto
student
loan,
etc.
B
B
Are
leaving
the
issuers
not
so
originators
in
some
cases
may
be
the
issuers,
so
we
have
very
little
to
do
genuinely
with
the
originators
right
because
they
loan,
so
the
lending
is
not
happening
on
our
platform.
We
are
looking
at
creating
e-wallet
so
that
the
moment
the
loan
is
given,
the
data
is
captured
on
our
platform,
but
till
that
happens
we
do
not
have
much
to
do
with
the
originator
unless
the
originator
is
the
issuer
themselves,
which
means
the
originator
has
another
legal
entity
which
is
packaging,
their
loans
and
selling
it.
B
C
C
You
know
one
month
or
something
like
that
so
month,
plus
one
month
later,
and
we
used
to
run
a
lot
of
analytics
based
on
the
you
know
what
each
bond
contained
in
terms
of
the
vintage
where
it
originated
from
the
you
know
all
kinds
of
msas
metropolitan
statistical
areas
where
the
loan
originated
from
all
kinds
of
other
data,
like
that,
so
that
level
of
granularity
is
normally
very
difficult
to
handle.
Because
there
are
so
many
different
loans
backing
a
single
bond.
B
So,
as
I
said,
we
took
so
there's
something
that
european
regulators
did
really
well
early.
2019
esma
came
up
with
their
securitization
regulation
where
they
created
excel
sheets,
where
every
cell
is
marked
and
described
in
the
format
in
which
data
has
to
be
reported
means
it's
as
bureaucratic
as
only
european
regulators
can
be,
but
we
took
that
as
a
standard
and
made
that
the
standard
asset
class
wise
template
on
our
platform.
So
we
are
capturing
data
to
that
level
of
granularity
in
the
us.
Now.
B
C
B
C
B
Mean
you
have
any,
it
will
be
like
hundreds
of
thousands,
so
each
deal
some
of
these
deals,
maybe
a
few
thousand
loans.
Each
loan
may
have
up
to
30
data
points
being
reported
every
month.
Some
of
the
deals
we
have
on
our
platform
are
two
years
old,
so
you
can
just
do
5
000
into
30
into
24
months
on
a
deal.
B
B
C
C
B
B
Now
all
deals
may
not
have
all
these
data
points,
so
this
excel
is
the
european
regulators
excel,
which
is
what
our
template
is
based
on.
So
all
these
deals
so
till
now
here
it
was
only
the
loan
level
information
now
collateral,
which
means
the
information
about
the
house,
because
the
loan
is
different
from
the
house.
So
you
need
the
information
about
the
loan.
Then
you
need
the
information
about
the
house.
B
D
So
so
that
one
more
question
so
in
in
terms
of
when
you
have
onboarded
any
of
the
deal-
and
there
is
a
payout
say,
a
monthly
or
whatever
the
frequency
has
been
set
up
at
each
payment-
is
that
the
payment
triggers?
What
is
the
mechanism
or
what?
What?
What
is
the
role
of
a
platform
of
say,
there's
a
trench
which
needs
to
be
paid
every
month.
So
yeah.
B
Currently
yeah
so
currently
our
platform
does
the
calculation
exactly
how
a
trustees,
internals
or
paying
agents
internal
software
would
have
done
based
on
which
the
payments
will
be
made.
The
payments
are
executed
manually,
that's
as
far
as
intane
admin
is
concerned
in
interior
markets,
the
payments
will
get
executed
automatically,
but
because
of
that
in
pain,
markets
will
allow
to
begin
with
only
standard
deal
structure.
So
the
big
difference
is
that
currently,
the
deal
can
be
anything
that
the
participants
in
a
deal
want
it
to
be.
B
They
do
draft
the
contract,
we
take
the
contract
and
create
smart
contracts
in
in
markets.
There
will
be
standard
sets
of
deal
terms
coded
as
smart
contracts
and
the
people
underwriting.
A
deal
can
only
choose
those,
and
that
is
important
because
in
international
like
any
way
would
happen
on
excel
sheets
or
whatever
software
systems.
There
is
a
gap
between
calculation
and
somebody
paying
and
you
can.
You
have
a
chance
to
catch
an
error,
make
a
correction
etc,
because
in
markets
the
payment
will
get
executed
based
on
the
smart
contract
calculation.
B
B
B
Which
means
that
the
moment
an
investor
invests
in
a
pool
that
us
dollar
investment
is
converted
into
usdc
and
the
only
stable
coin
we
allow
is
usdc,
which
has
two
advantages,
one
obviously,
as
everybody
knows,
backed
by
us
dollar
dollar
for
dollar
two,
because
we
are
doing
this
on
avalanche,
it's
natively
minted
on
avalanche
and
we
don't
have
to
use
a
bridge,
so
it
automatically
converts
into
usdc,
obviously
telling
the
investor
that,
as
you
are
executing
this,
I
am
immediately
converting
to
usdc
the
moment.
It
hits
the
issuer
who
has
to
collect
that
money.
B
Usdc
converts
back
into
usd
again.
Similar
thing
happens
with
the
services
so
servicer
who's
collecting
the
monthly
payments.
Us
dollars
immediately
gets
converted
into
usdc
on
usdc
collections.
The
smart
contract
based
calculations
apply,
and
hence
that
usdc
is
distributed
to
investors
across
the
branches
as
per
whatever.
The
algorithm
has
been
coded
into
the
smart
contract
and
the
moment
the
investors
receive
their
monthly
collections
money.
It
will
immediately
convert
back
into
us
dollars.
So,
basically,
just
for
the
period
that
it
is
running
on
the
blockchain,
it
will
be
in
usdc
the
moment
it
hits
the
party.
D
B
D
B
B
No,
no,
no,
they
are
completely
relying
on
this
data.
It
is
not
automatically
executing
the
payment.
The
payment
execution
is
off
the
blockchain
in
case
of
intent.
Admin
means,
I
don't
know
if
fabric
platform
even
allows
to
automatically
execute
the
payment.
I
don't
know
how
it
would
happen,
so
intent
admin
runs
on
fabric
in
case
of
fabric.
B
D
Yeah,
possibly
the
the
market
data
which
the
platform,
which
is
using
say
for
the
calculation
based
on
the
libor
or
any
other
benchmark,
whereas
the
actual
payout,
which
is
happening,
it
is
how
it
will
be
very
same
or
it
will
always
be
same
because
that's
something
at
this
point
in
time.
The
the
calculation
the
payout
is
happening
outside
the
the
platform.
D
So
that's
something!
Maybe
I'm
missing
on
that
point,
because
I
understand
when,
when
you
have
this
integrated
payment,
usd
token
converting
into
esd,
then
it's
it's.
It's
a
seamless
thing
right,
whatever
the
calculation
which
the
platform
is
doing,
that
exact
amount
which
is
being
paid
to
to
the
investor.
B
Means
any
data
means
whosoever
is
going
to
make
the
payout
wherever
whatever
the
additional
component
is
that
additional
component
will
also
have
to
be
captured
by
the
blockchain.
So
there
is
no
calculation
happening
of
blockchain
to
make
any
payouts
of
the
blockchain.
Just
the
manual
task
of
executing
a
payment
is
happening
off
the
blockchain.
There
is.
B
D
B
D
C
C
We
are
focused
on
d5,
we
focused
on
you
know,
financial
infrastructure,
which
is
what
seems
to
be,
I
mean,
and
most
of
us
are
concerned
with
that
so
to
group
all
that
to
embrace
it
all,
we
are
going
to
call
it.
The
finance,
sig
and
I'll
be
circulating
a
some
kind
of
a
proposal
for
that
and
to
get
the
agreement
of
all
the
members
thanks
again.
Julian,
of
course,
does
a
wonderful
job,
and
I
don't
even
need
to
be
here.