►
Description
Modernize Regulatory Reporting: Get Ready for T+1 Settlement - Antoine Amend, Databricks; Stephen Goldbaum, Morgan Stanley; Ephrim Stanley, Goldman Sachs and Moderated by Ashley Trainer, Databricks
Speakers: Ephrim Stanley, Ashley Trainor, Antoine Amend, Stephen Goldbaum
Financial regulators and capital market participants have laid out a path to shorten the standard settlement cycle to the trade date plus one business day (T+1) by September 3, 2024. The transition to T+1 means that shortening the settlement cycle can mitigate risk, increase overall efficiency, and allow for better uses of capital. Panelists will cover the advantage of having a unified, open-source technology platform (like Morphir, Legend) and application interoperability to help manage this significant transition. In this talk, the speakers will:
Demonstrate the benefits of the Lakehouse in the ingestion, processing, validation, and transmission of regulatory data.
Address the need for organizations to ensure consistency, integrity and timeliness of regulatory pipelines.
Show how capital market firms could bring full transparency and confidence to the regulatory data, reducing operation costs and adapting to new standards like T+1. Given the timeline of implementation, 2022 has been labeled the year of impact analysis and securing budgets and management buy-in.
A
So
with
me
today,
the
clicker
doesn't
work
with
me
today:
ask
a
trainer
or
a
solution
architect
for
financial
services
at
databricks,
a
technical
director
for
financial
services
at
databricks
and
Ashley
and
I
are
absolutely
thrilled
to
co-host
this
session,
with
Stephen
goldbaum
and
Ephraim
Stanley,
respectively,
a
distinguished
engineer
at
Morgan
Stanley
and
a
technology
fellow
at
Goldman
Sachs,
to
talk
about
Legend
morphe.
How
those
different
open
source
initiatives
can
co-exist
can
be
integrated
and
harmonized
when
coupled
with
databricks
backend.
A
So
we
have
a
backed
agenda.
So
let's
get
started
right
away
with
that
code
or
probably
more
of
a
statement
than
a
code,
but
something
that
we've
seen
in
Wall
Street
back
in
2015,
when
the
industry
was
moving
from
T3
to
T2
so
two
days
to
close
and
settle
all
those
trades
as
a
way
to
minimize
that
risk
risk
of
those
trades
failing
and
therefore
minimize.
All
those
margin
calls
right
so
time
equals
risk.
Reducing
that
times
makes
sense,
but
that
was
back
in
2015
built
specifically
for
period
of
high
Market
volatility.
A
A
I,
wouldn't
be
surprised
if
today
I
hear
some
anime
star
physically
running
from
one
Mainframe
to
another,
with
a
floppy
disk
right
or
sending
that
via
FedEx
or
recoding,
an
entire
Excel
spreadsheet
on
a
database
on
every
single
day.
So
the
way
we're
going
to
achieve
T1
as
an
ecosystem
as
an
environment
is
through
automation.
A
An
automation
would
be
kind
of
naive
to
think
everybody
will
be
using
the
same
technology
and
the
same
proprietary
model.
Automation
will
need
to
be
about
standard
and
open
source
is
key,
so
somehow
it's
aligned
with
those
those
key
values
of
Open
Source
Technologies,
creating
new
standards
that
enable
that
interoperability
across
different
systems
that
may
be
using
different
Technologies
but
harmonized
around
those
those
different
standards
and
driving
that
collaboration.
A
So
Legend
and
Mophie
are
great
examples
to
enable
that
interoperability
data
as
a
contract
models
as
a
contract
logic
as
a
contract
that
can
be
interpreted
across
different
systems
and
before
I
hand
over
to
Stephen.
To
talk
about
morphe
just
want
to
to
stress
really
the
importance
of
having
that
right.
Data
culture
we've
seen
that
over
and
over
whether
it's
data
driven
whether
it's
about
Cloud,
whether
it's
about
open
source
organization
that
went
through
that
kind
of
Behavioral
shift
that
transition
in
terms
of
a
data.
Culture
win.
A
Why?
Because
they
can
adapt
to
all
those
changing
condition
much
faster
than
anyone
else,
and
to
put
that
in
perspective
of
T1,
when
dtcc
estimates
that
the
margin
calls
can
be
reduced
by
40
by
moving
to
t
plus
one
day,
that's
billions
of
dollars
that
could
be
reinvested
somewhere
else.
Assuming
we
can
connect
the
dots
assuming
we
can
cut
the
middleman,
kill
those
manual
process
and
streamline
that
entire
process
throughout
that
entire
trade
life
cycle.
A
We
will
be
looking
at
an
example
that
is
not
necessarily
a
boat's
T1
settlement,
but
you
will
be
looking
at
this
through
the
context
of
regulatory
reporting,
but
the
principle
is
that
all
those
Technologies
are
generic
and
can
be
adapted,
and
we
can
use
that
for
LCR
calculation
today,
the
same
can
be
used
for
trade
settlement
tomorrow.
All
those
different
Technologies
can
be
interoperated
across
different
systems.
So
without
further
Ado
over
to
you,
Stephen
and
yeah
thanks.
B
Okay,
so
yes,
so
we're
here
to
talk
about
how
we
can
bring
the
Finos
ecosystem
together
to
do
new
and
interesting
things
and
what
is
more
interesting
than
regulations
and
what
better
regulation
is
there
than
the
liquidity
coverage
ratio
right
and
so
the
liquidity
otherwise
known
as
the
LCR,
because
it's
a
mouthful
is
really
about
categorizing
cash
flow.
So
you
take
all
these
cash
flows
and
categorize
them
into
groups
and
then
once
you've
categorized
them.
B
B
Help
excellent
excellent,
thank
you,
and
so
one
of
the
things
you
might
ask
is
well:
how
can
we
Implement
that
and
that's
where
Morpher
comes
in,
so
what
we've
done
is
coded
the
LCR
and
Morpher,
and
just
to
kind
of
highlight
some
of
the
things
you
can
do
with
that
is
with.
Moreover,
you
can
interact
with
it
interact
with
the
logic
in
a
non-technical
way.
B
B
Can
do
is
we
can
use
Morphers
tools
to
generate
into
different
execution
contexts,
and
so
in
this
example,
we're
going
to
generate
spark
so
that
we
can
execute
into
the
databricks
lake
house
platform
and
by
doing
that,
we're
handing
off
execution
to
a
managed
platform.
And
then
the
other
thing
you
might
ask
is
well.
How
do
we
integrate
that
and
the.
B
Is
well,
we
can
take
the
information
of
the
the
structure
of
the
assets
that
needs
to
be
handed
to
this
calculation
in
order
for
it
to
run,
and
we
can
hand
that
to
an
integration
technology
like
Legend,
so
we
can
take
this.
This
is
the
structure
of
the
asset.
By
the
way
we
can
take
that
information
and
hand
it
to
Legend,
and
then
let
Legend
pull
that
data
in
from
on-prem
and
put
it
into
the
lake
house
for
execution,
and
so
that
way
we've
got
the
entire
ecosystem.
Again.
C
Sorry,
thank
you
hi
everyone
thanks
for
having
me,
my
name
is
E
from
Stanley
I
work
for
Goldman,
Sachs,
I'm,
also
a
contributor
on
the
legend
project.
So
today
what
I
want
to
do
is
give
you
an
overview
for
the
legend
data
platform
and
walk
you
through
a
real
demo
of
building
a
data
access
API
that
we
called
a
legend
service.
So
Legend
is
a
data
modeling
and
data
management
platform,
and
a
data
model
is
a
systematic
Way
by
which
you
describe
the
logical
shape
of
data.
C
Now
data
models
are
super
important
because
they
allow
multiple
parties,
whether
you
are
users
within
the
same
organization
or
even
across
multiple
organizations.
You
have
the
same
consistent
view
of
data
now.
What
Legend
lets
us
do
is
access
the
data,
that's
in
different
physical
databases
through
the
lens
of
a
logical
model
and
the
way
we
do
that
is
by
creating
a
mapping
between
The,
Logical
shape
of
data
and
the
physical
shape
and
using
this
mapping,
Legend
can
take
a
logical
query.
C
Convert
it
into
a
physical
query,
run
it
against
the
data
and
get
you
the
data
back
right,
and
this
is
again
like
super
important,
because
no
matter
where
your
data
is
irrespective
of
How
It's
laid
on
disk,
you
get
a
consistent
View
and
today,
thanks
to
our
partnership
and
collaboration
and
Antoine's
personal
contribution
like
Legend,
now
supports
data
and
databricks
databases
using
a
jdbc
connector.
So
with
that
I'm
going
to
have
everyone
help
me
with
a
live
demo.
C
So
what
you're
seeing
right
now
is
an
application
that
we
call
Legend
Studio,
which
is
a
very
interactive
Rich
data,
modeling
environment.
You
could
do
many
things
with
Studio,
like
you
could
Define
a
data
model.
You
can
manage
the
life
cycle
of
the
data
model
through
its
through
its
integration,
with
a
git
backend
like
gitlab,
and
we
can
also
execute
data
access
queries.
But
let's
just
start
by
looking
at
a
data
model.
So
here
we
have
a
very
simple
data
model
of
an
asset
which
is
modeled
as
a
class
with
a
bunch
of
attributes.
C
So,
on
the
left
side,
we
have
the
logical
shape
of
the
data
and
then
on
the
right
side.
We
have
the
physical
model
which
in
this
case
happens
to
be
a
relational
model
with
a
database
table
and
then
in
the
middle.
You
have
all
the
mapping
rules.
In
the
simplest
case,
you
can
take
an
attribute
like
business
line
and
map
it
one
to
one
with
a
database
table
column
in
the
more
complex
case,
for
example,
maturity
date.
C
So
a
connection
is
where
data
lives.
Now,
in
this
case
we're
saying
a
data
is
in
a
data,
bricks,
cluster
and
you
can
authenticate
with
one
or
more
supported
authentication
schemes.
Now,
with
all
of
this
in
place,
we
can
then
move
on
to
define
a
service.
So
let's
look
at
a
definition
of
a
service,
so
the
job
of
a
service
is
to
define
the
data
that
you
want.
So
in
this
case
we're
saying
we
want
to
project
certain
attributes
of
the
asset
class.
C
C
A
Yeah,
maybe
the
Wi-Fi,
but
so
we're
seeing
that
so
Stephen
created
almost
an
input
data
model
for
your
job
right.
So
you
were
expecting
data
to
come
in
a
specific
shape,
specific
structure,
specific
schema,
you
define
that
schema
and
frame
as
a
target
class,
and
we've
mapped
that
physical
model
that
physical
table
into
this
that's
right.
D
A
D
D
D
Dropping
all
that
kind
of
stuff,
and
so,
when
you
think
about
and
one
other
thing,
databricks
also
ensure
that
all
things
going
forward
will
be
open
source
in
Delta
Lake.
And
it's
that
openness,
the
insurance
that
everything's
going
to
be
open
in
Delta.
That
really
makes
Delta
an
integral
part
of
your
strategic
decisioning.
D
When
you
think
about
your
Tech
stack
in
data
moving
forward,
and
it's
not,
let's
replace
our
old
technology
with
Delta,
let's
bring
in
this
open
source
Tech,
it's
actually
harmonizing
the
technology
that
you've
decided
on
by
the
underpinning
of
these
open
source
Technologies
like
Delta.
So
what
we're
going
to
demo
today?
What
we're
going
to
hopefully
show
in
a
second
if
Anton
can
switch
the
the
thing.
D
So,
let's,
let's
see
here,
you
scroll
the
other
way,
then
be
right
all
right.
So
here
we
go
so
the
first
thing
we
want
to
show
so
the
model
that
we
saw
earlier
in
legend.
That
model
has
been
all
good
created
into
a
jar
file
which
been
added
as
a
dependency
to
our
databricks
spark
cluster.
Oh
wonderful!
D
So
what
you
can
actually
see
here
is
that
we
have
all
of
the
entities
of
this
Legend
model
available
to
us
in
this
databricks
notebook
that
is
full
access
to
a
databricks
spark
cluster.
So
we
see
here
the
entities,
the
services,
the
functions
that
were
predefined
and
these
are
all
actually
going
to
be
powered
by
spark
and
Delta
on
the
back
end.
So
this
will
allow
us
to
do
things
like
create
data
models
in
Delta
tables,
execute
queries
all
powered
by
Spark,
so
we'll
see
here.
D
This
is
the
actual
service
that
we
were
looking
at
earlier
and
so
really
simply
and
programmatically.
We
can
create
this
spark
data
frame
called
inflows
by
executing
this
Legend
query.
The
get
inflows
with
buckets
so
under
the
hood
What's
Happening
Here
is
spark.
Jobs
are
kicking
off
we're
reading
data
from
those
Delta
tables
and
we're
bringing
in
this
data
frame
that
complies
with
the
input
that's
required
for
our
Morpher
Transformations,
but
we
haven't
actually
had
to
write
any
of
that
as
a
user.
That's
all
been
abstracted
away
in
this
Legend
model.
D
So
now
we
have
that
data
frame
we're
going
to
pass
it
off
to
Morpher,
so
Morpher
can
actually
take
that
business
logic
that
we
saw
earlier
and
transform
it
into
Scala
spark
code
right
so
that
we
can
run
it
on
a
Spark
engine,
and
so
that's
exactly
what's
happening
here
and
we've
actually
compiled
that
spark
code
into
a
second
jar
that
we've
added
to
this
cluster
as
well.
So
with
a
simple
line
of
code.
D
So
now
that
we
have
our
results,
we
want
to
persist
this
report
somewhere,
so
we
actually
go
ahead
and
persist
it
into
a
Delta
table,
and
that
gives
us
a
lot
of
additional
functionality.
So
one
of
the
things
that
Delta
does
really
well
is
gives
us
full,
basically
DML
and
ddl
support
on
top
of
what
our
compressed
parquet
files
really
so
we're
able
to
do
things
like
evolve.
This
report
schema
over
time.
We're
able
to
do
things
like
add
access
controls
on
top
of
this
report,
and
one
other
thing
that's
really
interesting
is
Delta.
D
D
The
most
up-to-date
version
is
whatever
we
wrote
last,
but
if,
for
some
reason,
we
need
to
audit
this
report
as
of
a
different
point
in
time
either
because
it
was
updated
or
maybe
it
was
deleted,
we
can
actually
go
ahead
and
query
that
really
simple,
simply
using
either
a
timestamp
or
a
version
number
just
like
this
in
SQL.
So
we
can
actually
see
this
report
here.
As
of
December
2nd,
you
can
also
query
it
by
the
version
number
itself.
D
The
last
thing
I'll
say
about
putting
this
report
into
Delta
is.
It
also
gives
us
the
ability
to
use
another
open
source
tool
called
Delta
sharing
in
order
to
provide
this
report
out
to
other
users
who
may
need
access
to
this
report,
but
we
don't
want
to
actually
copy
this
report
to
another
location
or
maintain
data
pipelines
to
deliver
this
potential
for
data
scaleness.
All
this
kind
of
bad
stuff
that
comes
with
data
delivery,
so
Delta
Delta
sharing
is
another
open
source
tool
that
databricks
open
sourced
in
2021.
D
A
Yeah,
so
this
is
really
really
great
right,
because
first,
what
we've
seen
is
that
full
reproducibility
reproducibility
aspect
of
Delta
enforcing
that
reproducibility
of
your
data,
but
because
we
version
controlled
all
the
more
field
logic
unit
tested
that
logic
and
because
we
can
compile
and
automatically
deploy
that
logic
into
your
underlying
environment
and
same
with
Legend.
We
have
all
the
evidence
of
the
reusability,
the
whole
producibility
and
audit
requirements
from
a
code
from
a
data
from
a
data
model
perspective.
A
So
we
are
in
full
kind
of
comfortable
position
to
know
exactly
that
report
at
that
specific
point
in
time,
but
one
actually
just
showed
is
really
critical
to
enable
that
last
mile
that
delivery,
whether
it's
from
the
back
office
for
to
back
to
the
front
office
or
whether
it's
from
a
an
organization
to
other
organizations
elsewhere
regardless
of
the
underlying
technology.
So
you
mentioned
Excel,
which
is
a
good
example
right.
A
People
are
using
Excel
on
a
day-to-day
basis
and
to
use
that
information,
usually
we
will
be
sending
that
data
over
someone
will
be
responsible
for
bringing
that
data
coding
that
into
a
database
and
then
exporting
that
to
excel
here.
We're
not
even
transferring
that
information
we're
pointing
Excel
directly
to
where
your
data
sits.
With
that
specific
version
that
we
agreed
so
I
think
we
that
that
concludes
the
demo,
that
we
wanted
to
do
again.
A
Coming
back
to
T1,
settlements
is
really
about
Automation
and
automation,
means
protocols
and
standards
and
interoperability
of
code
of
data
of
models.
So
those
capabilities
are
generic
enough
to
adapt
to
all
those
different
environments
and
different
systems
that
you
may
have.
So
this
is
what
will
drive
that
automation.
A
So
we
want
to.
Thank
you
all
for
your
time
today.
If
you
have
any
question,
please
feel
free
to
visit
us
downstairs
at
the
booth.
I
will
answer
any
question
that
you
may
have.
If
you
want
more
information
about
all
those
those
great
initiatives,
so
I
think
the
the
best
bet
is
to
go
at
the
Fino's
website
and
all
the
Fino's
GitHub
organization,
where
you'll
find
Legend,
you
will
find
Mophie.
You
will
find
that
Legend
Delta
contribution
that
we
announced
last
year
as
a
Labs
project.