Software AG explores the Seven Pillars of Market Surveillance 2.0 that will lead you to the next generation of bigger and better market surveillance, leaving the fines and prison sentences behind.

1. The
Seven
Pillars
of
Market
Surveillance
2.0
SURVEILLANCE
TOTAL

2. A
combina*on
of
monitoring
and
surveillance,
involving
both
data
and
human
behaviour
across
mul*ple
asset
classes
and
geographies,
helps
firms
detect
early
warning
signs
and
an*cipate
–
or
even
avoid
–
anomalous
behaviours
in
the
future
SURVEILLANCE
TOTAL

3. A
combina*on
of
monitoring
and
surveillance,
involving
both
data
and
human
behaviour
across
mul*ple
asset
classes
and
geographies,
helps
firms
detect
early
warning
signs
and
an*cipate
–
or
even
avoid
–
anomalous
behaviours
in
the
future
With
the
growth
of
headline
grabbing
scandals…
It’s
*me
to
get
SERIOUS
about
surveillance
SURVEILLANCE
TOTAL

4. A
combina*on
of
monitoring
and
surveillance,
involving
both
data
and
human
behaviour
across
mul*ple
asset
classes
and
geographies,
helps
firms
detect
early
warning
signs
and
an*cipate
–
or
even
avoid
–
anomalous
behaviours
in
the
future
With
the
growth
of
headline
grabbing
scandals…
It’s
*me
to
get
SERIOUS
about
surveillance
There
are
seven
key
ingredients
required
to
achieve
the
next
genera*on
of
total
surveillance;
or
the
Seven
Pillars
of
Market
Surveillance
2.0
SURVEILLANCE
TOTAL

5. TOTAL
PILLAR
#1
SURVEILLANCE

6. TOTAL
A
single,
CONVERGED
threat
system
Seamlessly
monitor
across
the
en*re
enterprise,
including:
• Market
Surveillance
• Opera*onal
Risk
• Market
Risk
• Trader
Profiling
PILLAR
#1
SURVEILLANCE

7. TOTAL
A
single,
CONVERGED
threat
system
Seamlessly
monitor
across
the
en*re
enterprise,
including:
• Market
Surveillance
• Opera*onal
Risk
• Market
Risk
• Trader
Profiling
PILLAR
#1
SURVEILLANCE
1. Comes
with
sufficient
performance
at
scale
to
monitor
very
large
volumes
of
streaming
analy*cs,
both
pre-­‐
and
post-­‐
trade
2. Is
open
and
flexible
enough
to
enable
organiza*ons
to
tailor
the
monitoring
based
upon
their
unique
and
evolving
requirements
3. Is
seamlessly
pre-­‐integrated
with…
a) Complementary
technologies
such
as
enterprise
grade
integra*on
b) Ultra-­‐low
latency
messaging,
in-­‐memory
data
management
c) Real-­‐*me
data
visualiza*on
…
All
the
raw
tools
needed
to
break
down
monolithic
silos.
Prerequisites
for
SUCCESS

8. TOTAL
PILLAR
#1
SURVEILLANCE
Converge
siloed
systems
such
as
an*-­‐money
laundering,
opera*onal
risk,
and
trader
profiling
into
a
single,
monitoring
system
for
a
correlated
view
of
all
poten*al
threats.

9. TOTAL
SURVEILLANCE:
PILLAR
#2
Past,
Present
&
PredicNve
ANALYSIS

10. TOTAL
SURVEILLANCE:
PILLAR
#2
Past,
Present
&
PredicNve
ANALYSIS
Performing
analysis
on
historical
data,
real-­‐*me
data,
plus
predic*ng
and
warning
of
threats
before
they
occur
or
do
damage

11. TOTAL
SURVEILLANCE:
PILLAR
#2
An
out
of
control
algo
can
–
and
has
–
bankrupted
a
trading
firm
Past,
Present
&
PredicNve
ANALYSIS
Performing
analysis
on
historical
data,
real-­‐*me
data,
plus
predic*ng
and
warning
of
threats
before
they
occur
or
do
damage

12. Analyzing
the
‘fire
hose’
of
market,
trade,
and
social
media
data
to
prevent
fraud
and
market
manipula*on,
using
large
structured
and
unstructured
data
sets
TOTAL
PILLAR
#3
SURVEILLANCE
Support
for
fast,
Big
Data

13. Analyzing
the
‘fire
hose’
of
market,
trade,
and
social
media
data
to
prevent
fraud
and
market
manipula*on,
using
large
structured
and
unstructured
data
sets
TOTAL
PILLAR
#3
SURVEILLANCE
Support
for
fast,
Big
Data
By
connec*ng
to
disparate
sources
of
trading
rela*onship
informa*on
(i.e.,
IMs,
chat
rooms,
emails,
mobile
phones,
video
and
audio
surveillance)
-­‐
trader
behaviour
can
be
surmised…
• Was
the
trader
working
unusual
hours?
• Did
she
never
take
a
vaca*on?
• Did
he
buy
a
fishery
together
with
another
FX
trader,
who
happens
to
be
his
banks’
client?

14. Analyzing
the
‘fire
hose’
of
market,
trade,
and
social
media
data
to
prevent
fraud
and
market
manipula*on,
using
large
structured
and
unstructured
data
sets
TOTAL
PILLAR
#3
SURVEILLANCE
Support
for
fast,
Big
Data
By
Including
data
from
social
media,
email
and
chat
rooms,
the
tangled
web
that
fraudsters
weave
becomes
more
predictable
when
management
can
watch
the
threads
crea*ng
the
web.
Fast,
Big
Data
comprises
these
threads
of
anomalous
human
behaviour;
Market
Surveillance
2.0
grabs
and
analyses
the
threads,
then
creates
ac*ons…

15. TOTAL
PILLAR
#4
SURVEILLANCE
Cross-­‐asset
class
monitoring
Monitoring
across
trading
silos
for
mul*-­‐asset
surveillance
and
across
correlated
asset
classes…

16. TOTAL
PILLAR
#4
SURVEILLANCE
Why
do
we
care?
Because
events
that
impact
one
asset
class
can
and
do
have
a
knock-­‐on
effect
on
others.
EXAMPLE:
Between
2007
and
2012,
the
price
of
oil
was
highly
correlated
to
the
stock
market.
On
May
5,
2011,
the
crude
oil
market
experienced
its
second-­‐largest
daily
drop
ever
when
trading
algos
repeatedly
triggered
sell-­‐stops.
The
$13
drop
in
the
price
of
Brent
crude
knocked
the
Dow
Jones
Industrial
Average
down
by
140
points,
or
1.1%.
It
could
have
been
worse
if
the
oil
algos
had
not
been
caught
and
stopped
in
*me.
Today’s
markets
are
becoming
more
complicated
and
sophis*cated
by
the
day.
Humans
simply
cannot
monitor
and
react
to
mul*-­‐asset
classes
at
the
same
*me,
while
trading
at
lightning
speed.
Cross-­‐asset
class
monitoring
Monitoring
across
trading
silos
for
mul*-­‐asset
surveillance
and
across
correlated
asset
classes…

17. TOTAL
PILLAR
#4
SURVEILLANCE
…watching
for
paferns
that
signal
risk
or
opportunity,
then
kicking
out
real
ac*ons
to
take
Cross-­‐asset
class
monitoring
Monitoring
across
trading
silos
for
mul*-­‐asset
surveillance
and
across
correlated
asset
classes…

18. TOTAL
PILLAR
#5
SURVEILLANCE
Cross
Region
Monitoring
Monitoring
for
risks
across
geographical
and
regulatory
boundaries
and
differences…

19. TOTAL
PILLAR
#5
SURVEILLANCE
Cross
Region
Monitoring
Monitoring
for
risks
across
geographical
and
regulatory
boundaries
and
differences…
Cross
border
surveillance
becomes
increasingly
cri*cal
as
financial
services
firms
and
investors
trade
mul*ple
asset
classes
across
many
countries
and
disparate
regulatory
regimes,
which
can
cause
confusion
and
create
opportuni*es
for
error.
Regula*ons
in
different
countries
(e.g.
Dodd-­‐Frank
vs.
MiFID)
have
similari*es
and
differences.
Regulatory
arbitrage
is
a
concern,
as
trading
firms
could
choose
to
do
business
with
more
lightly
regulated
regimes;
taking
extra
risks
with
their
company’s
and
shareholders’
money
and
reputa*on.

20. TOTAL
PILLAR
#5
SURVEILLANCE
Cross
Region
Monitoring
Monitoring
for
risks
across
geographical
and
regulatory
boundaries
and
differences…
Cross
border
surveillance
becomes
increasingly
cri*cal
as
financial
services
firms
and
investors
trade
mul*ple
asset
classes
across
many
countries
and
disparate
regulatory
regimes,
which
can
cause
confusion
and
create
opportuni*es
for
error.
Regula*ons
in
different
countries
(e.g.
Dodd-­‐Frank
vs.
MiFID)
have
similari*es
and
differences.
Regulatory
arbitrage
is
a
concern,
as
trading
firms
could
choose
to
do
business
with
more
lightly
regulated
regimes;
taking
extra
risks
with
their
company’s
and
shareholders’
money
and
reputa*on.
…to
assure
adherence
to
different
regulatory
environments

21. TOTAL
PILLAR
#6
SURVEILLANCE
Known
&
Unknown
Threats
Benchmarking
behavior
and
performance
to
uncover
previously
unknown
paferns

22. TOTAL
PILLAR
#6
SURVEILLANCE
Known
&
Unknown
Threats
Benchmarking
behavior
and
performance
to
uncover
previously
unknown
paferns
Monitoring
for
‘unknowns’
can
be
achieved
by
benchmarking
behavior
that
is
“normal”
over
*me
and
then
spoing
behavior
that
deviates
from
the
norm.
To
spot
suspicious
behavior
could
involve
digitally
monitoring
loca*ons
and
in-­‐person
interac*ons
of
traders…
their
speech
and
facial
expressions,
for
example

23. TOTAL
PILLAR
#6
SURVEILLANCE
Known
&
Unknown
Threats
Benchmarking
behavior
and
performance
to
uncover
previously
unknown
paferns
Monitoring
for
‘unknowns’
can
be
achieved
by
benchmarking
behavior
that
is
“normal”
over
*me
and
then
spoing
behavior
that
deviates
from
the
norm.
To
spot
suspicious
behavior
could
involve
digitally
monitoring
loca*ons
and
in-­‐person
interac*ons
of
traders…
their
speech
and
facial
expressions,
for
example
Cyber-­‐terrorism
is
on
the
upswing
and
algorithmic
terrorism,
where
a
well-­‐funded
criminal
or
terrorist
organiza*on
causes
a
major
market
crisis,
could
be
the
next
itera*on.
Only
by
keeping
a
close
watch
on
the
markets
and
the
par*cipants
involved
can
these
unwanted
behaviors
be
nipped
in
the
bud

24. TOTAL
PILLAR
#6
SURVEILLANCE
Known
&
Unknown
Threats
Benchmarking
behavior
and
performance
to
uncover
previously
unknown
paferns
Monitoring
for
‘unknowns’
can
be
achieved
by
benchmarking
behavior
that
is
“normal”
over
*me
and
then
spoing
behavior
that
deviates
from
the
norm.
To
spot
suspicious
behavior
could
involve
digitally
monitoring
loca*ons
and
in-­‐person
interac*ons
of
traders…
their
speech
and
facial
expressions,
for
example
Monitor
for
‘unknown
unknowns’,
by
benchmarking
behaviour
that
is
‘normal’
over
*me
and
spoing
behaviour
that
deviates
from
the
norm
Cyber-­‐terrorism
is
on
the
upswing
and
algorithmic
terrorism,
where
a
well-­‐funded
criminal
or
terrorist
organiza*on
causes
a
major
market
crisis,
could
be
the
next
itera*on.
Only
by
keeping
a
close
watch
on
the
markets
and
the
par*cipants
involved
can
these
unwanted
behaviors
be
nipped
in
the
bud

25. TOTAL
PILLAR
#7
SURVEILLANCE
Dynamically
Evolve
Rules
Be
ready
for
the
next
threat.
Control
your
own
surveillance
and
con*nually
adapt
your
monitoring

26. TOTAL
PILLAR
#7
SURVEILLANCE
Dynamically
Evolve
Rules
Be
ready
for
the
next
threat.
Control
your
own
surveillance
and
con*nually
adapt
your
monitoring
Once
a
new
unknown
behavior
is
found,
it
needs
to
become
a
‘known
behavior’
and
a
new
rule
must
be
added
to
the
system.
It
is
cri*cal
to
be
able
to
add
new
rules
dynamically
rather
than
relying
on
a
“shrink-­‐wrapped
applica*ons”
that
do
not
provide
this
level
of
flexibility…

27. TOTAL
PILLAR
#7
SURVEILLANCE
Dynamically
Evolve
Rules
Be
ready
for
the
next
threat.
Control
your
own
surveillance
and
con*nually
adapt
your
monitoring
…Since
the
next
episode
of
an
‘known
behavior’
could
occur
at
any
*me;
it
would
be
complacent
to
think
that
because
one
has
been
discovered,
it
won’t
happen
again.
Once
a
new
unknown
behavior
is
found,
it
needs
to
become
a
‘known
behavior’
and
a
new
rule
must
be
added
to
the
system.
It
is
cri*cal
to
be
able
to
add
new
rules
dynamically
rather
than
relying
on
a
“shrink-­‐wrapped
applica*ons”
that
do
not
provide
this
level
of
flexibility…

28. TOTAL
PILLAR
#7
SURVEILLANCE
Dynamically
Evolve
Rules
Be
ready
for
the
next
threat.
Control
your
own
surveillance
and
con*nually
adapt
your
monitoring
Turn
a
new
unknown
behaviour
into
a
known
behaviour
by
dynamically
adding
new
rules
to
the
system
…Since
the
next
episode
of
an
‘known
behavior’
could
occur
at
any
*me;
it
would
be
complacent
to
think
that
because
one
has
been
discovered,
it
won’t
happen
again.
Once
a
new
unknown
behavior
is
found,
it
needs
to
become
a
‘known
behavior’
and
a
new
rule
must
be
added
to
the
system.
It
is
cri*cal
to
be
able
to
add
new
rules
dynamically
rather
than
relying
on
a
“shrink-­‐wrapped
applica*ons”
that
do
not
provide
this
level
of
flexibility…

29. The
Seven
Pillars
of
Market
Surveillance
2.0
1. Converge
siloed
systems
into
a
single
monitoring
system
for
a
correlated
view
of
poten*al
threats.
2. Perform
“con*nuous
analy*cs”
to
predict
what
might
happen
–
and
prevent
it.
3. Check
social
media,
email
and
chat
rooms
for
anomalous
behaviour
4. Monitor
all
asset
classes
5. Assure
adherence
to
regional
regulatory
environments
6. Benchmarking
“normal”
behaviour
to
spot
deviant
behaviour
7. Dynamically
add
newly
discovered
“unknown”

30. MEET
THE
AUTHOR
Theo
Hildyard
Theo
Hildyard
is
Head
of
Solu*ons
Marke*ng
at
Solware
AG.
This
team
harnesses
Solware
AG
technology
to
deliver
business
solu*ons
focused
on
turning
big
(and
streaming)
data
into
meaningful
insights
for
automated
decision-­‐making.
Solu*ons
include
Algorithmic
Trading,
FX
eCommerce,
Market
Surveillance,
Customer
Experience
Management,
and
Con*nuous
Monitoring
for
Governance
Risk
&
Control
(iGRC)
Get
the
comprehensive
Total
Surveillance
White
paper