1.
Transcending
Traditional
Service
Models
with
Disruptive
Technology
Julian
Fung,
jzf1358@truman.edu,
(872)
203-­‐4854
Lasse
Fuss,
lmf5136@truman.edu,
(816)
872-­‐0016
Tommy
Ng,
hn1746@truman.edu,
(660)
998-­‐4500
Truman
State
University
Charles
Boughton
boughton@truman.edu,
(660)
785-­‐4521

2. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
2
Executive
Summary
In
order
to
secure
the
enduring
success
of
the
wealth
management
industry
and
gain
absolute
advantages
over
e-­‐services,
financial
services
companies
need
to
incorporate
Big
Data
technology,
advances
in
behavioral
finance,
and
alternative
services
into
a
holistic
service
model.
With
only
24%
of
wealth
managers
prepared
for
the
upcoming
challenge
due
to
technological
advancement,
there
seems
to
be
an
urgency
to
redefine
the
wealth
management
industry.
In
the
next
two
years,
financial
advisors
expect
to
increase
social
networks
usage
by
40%
and
mobile
and
tablet
usage
by
85%.1
Identifying
and
incorporating
disruptive
technology
into
a
holistic
service
model
is
essential
for
financial
advisors
to
adjust
to
the
new
environment.
This
paper
addresses
the
future
of
financial
decision-­‐making
and
its
impact
on
financial
services
companies.
As
the
amount
of
open
data
increases
exponentially,
data
analytics
are
becoming
a
crucial
emerging
disruptive
technology
that
can
provide
competitive
differentiation
among
financial
services
firms.
Thus,
firms
need
to
incorporate
Big
Data
to
develop
and
gain
insights
into
customers,
provide
personalized
offerings,
discover
investment
opportunities,
reduce
risk
and
assist
with
compliance.
In
addition,
building
on
advances
in
behavioral
science,
financial
advising
software
has
to
incorporate
behavioral
models
to
augment
client
interactions
with
wealth
managers
and
financial
planners.
A
holistic
service
model
has
to
account
for
unsound
client
behaviors
and
aid
practitioners
in
moderating
or
adapting
to
such
behavior.
At
the
same
time,
behavioral
nudges
are
instrumental
in
encouraging
clients
to
save
and
invest.
The
growing
expectations
from
investors
are
poised
to
reshape
the
entire
industry.
Emerging
e-­‐
services
provide
investors
platforms
to
seek
investment
consultation
free
of
charge,
track
portfolios
in
real
time,
and
automate
financial
decision
making
based
on
efficient
algorithms.
Conventional
service
models
should
incorporate
adaptable
and
innovative
financial
advising
alternatives
to
serve
various
customer
needs
in
order
to
improve
wealth
management.
Ultimately,
the
purpose
of
wealth
management
is
to
create
a
desirable
value
to
customers.
In
order
to
stay
competitive
and
defend
themselves
against
the
growing
threat
of
“robo-­‐advising”,
knowing
what
investors
are
looking
for
and
embracing
technological
usage
has
become
compulsory
for
financial
advisors.
Thus,
the
holistic
service
model
should
incorporate
Big
Data
usage,
behavioral
finance,
and
user-­‐friendly
technology
to
surpass
e-­‐services
competitors.
1
Crosby,
C.
Steven,
Jensen,
Jeremy,
Ong,
Justin.
Navigating
to
Tomorrow:
Serving
Clients
and
Creating
Value.
PDF
file.

3. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
3
Capitalizing
on
Big
Data
Along
with
new
growth
opportunities
from
the
advancement
of
technology,
the
financial
services
industry
faces
extraordinary
challenges
such
as
sustaining
clients’
confidence
and
meeting
their
demands
for
convenience
and
higher
returns,
while
restraining
escalating
operating
expenses
and
improving
productivity.
In
their
effort
to
overcome
these
challenges,
financial
services
firms
must
leverage
their
information
assets
to
gain
a
comprehensive
understanding
of
the
various
key
aspects
in
the
financial
services
industry
and
contribute
to
better
service
models.
Thus,
a
holistic
service
model
needs
to
incorporate
Big
Data
to
gain
insights
into
customers
and
prospects,
discover
investment
opportunities,
assist
with
risk
and
compliance,
and
provide
competitive
differentiation.
Bill
Gerneglia,
COO
of
CIOZone.com,
describes
Big
Data
as
“a
process
of
collecting,
storing,
and
analyzing
fragments
of
information
that
can
be
rapidly
assembled
to
identify
subtle
macro
trends
or
create
actionable
profiles
that
precisely
target
unique
individuals”.2
Customer
segmentation
is
a
Big
Data
use
case
that
can
bring
great
value
to
financial
services
firms.
In
the
industry,
customer
segmentation
is
a
key
tool
for
sales,
promotion,
and
marketing
campaigns.
Firms
can
implement
better
marketing
plans
and
strategies
for
customers
if
they
can
group
customers
with
differing
demands
into
different
segments.
Firms
often
segment
customers
by
demographic
information,
but
with
more
advanced
analytical
software,
firms
can
now
segment
customers
by
their
behaviors.
Firms
can
use
analytical
software
such
as
the
MapR
distribution,
an
enterprise-­‐grade
distributed
data
platform,
to
collect
and
analyze
all
available
customer
data.
This
includes
daily
transactions,
customer
interactions
(e.g.,
social
media,
call
centers),
house
price
index,
and
merchant
records
in
real
time.
Once
these
data
sets
are
gathered,
companies
can
group
customers
into
one
or
more
segments
based
on
their
needs
in
terms
of
products
and
services,
and
plan
their
sales,
promotion
and
marketing
campaigns
accordingly.3
With
these
segmentations,
we
recommend
that
firms
take
a
step
further
and
include
these
segments
in
an
urgent/important
matrix
as
shown
in
attachment
A.
Using
this
matrix,
firms
are
able
to
obtain
a
clearer
view
of
the
importance
and
urgency
of
each
segment
and
prioritize
accordingly.
If
a
particular
segment
is
deemed
important
and
urgent,
companies
know
they
must
approach
this
segment
first
by
creating
personalized
promotions
and
marketing
2
Gerneglia,
Bill.
“Finding
Value
in
Open
Data
Vs
Big
Data.”
myBigDATAview.,
Blog.
21
Nov.
2014.
3
"Big
Data
and
Apache
Hadoop
for
Financial
Services."
MapR,
Hadoop.
n.d.
Web.
21
Nov.
2014.
<https://www.mapr.com/solutions/industry/big-­‐data-­‐and-­‐apache-­‐hadoop-­‐financial-­‐services>.

4. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
4
strategies
for
the
segment.
Conversely,
firms
should
spend
less
time
in
tackling
segments
that
are
categorized
as
unimportant
and
not
urgent.
Through
technology,
emerging
online
services
companies
have
been
able
to
produce
advanced
financial
advising
algorithms
to
reduce
investment
risk
and
costs,
and
claim
that
customers
have
the
potential
to
obtain
higher
returns
with
these
algorithms
than
they
might
with
a
traditional
advisor.
While
this
may
be
true,
Big
Data
provides
more
human
oversight
than
automated
advisors
and
handles
market
anomalies
in
a
more
pragmatic
manner.
With
accurate
and
up-­‐to-­‐date
customer
segmentation,
firms
can
use
Big
Data
to
further
understand
customers
on
a
micro-­‐level,
enabling
personalized
customer
service
and
product
offering.
This
allows
for
prediction
of
new
products
and
services,
and
therefore,
firms
can
customize
relevant
offers
based
on
these
predictions
to
segmented
customers.
Achieving
these
benefits
requires
real-­‐time
analysis
of
unstructured
data
from
customer
decisions,
purchase
frequency
and
timing,
browsing
data
on
financial
services
and
products,
social
media
activity,
and
other
sources.
This
will
enable
customer
and
market
sentiment
analysis
to
learn
customer
preferences
and
sentiments
about
products
or
services
offered,
assess
customer
sentiment
through
the
study
of
converging
trends,
and
identify
the
current
feel
or
tone
of
the
market.4
For
example,
financial
services
software
can
use
the
MapR
distribution
to
analyze
and
track
customer
movements
and
responses
on
social
media
or
product
review
sites.
This
new
insight
can
help
firms
respond
to
emerging
problems
in
a
timely
manner
and
also
predict
what
kind
of
investments
or
retirement
plans
appeal
to
individual
customers.
Western
Union,
a
financial
services
company,
has
adopted
Cloudera’s
data
hub
to
acquire
important
insights
from
initial
contact
with
customers.
One
insight
revealed
by
Cloudera’s
hub
was
that
many
web
and
mobile
customers
frequently
process
repeated
transactions
to
the
same
recipient
at
the
same
time
each
month.
This
data
prompted
Western
Union
to
add
a
“Send
Again”
button
to
make
the
process
of
repeating
payments
more
convenient
for
customers.5
As
predictive
analytics
have
not
advanced
far
and
may
not
always
provide
accurate
results,
we
suggest
that
financial
advisors
combine
their
expertise
in
the
industry
with
these
predictive
tools
to
provide
appropriate
proposals
and
solutions
to
clients.
New
legal
requirements
and
increasing
demand
for
better
internal
management
support
lead
many
firms
to
focus
on
finance
and
risk
management.
Big
Data
can
help
with
risk
management
by
enabling
a
centralized
risk
data
management
that
can
quickly
and
flexibly
address
new
requirements.
Firms
can
create
real-­‐time
individual
risk
profiles
for
customers
based
on
the
ample
amount
of
4
Kumar,
Anjani.
“Big
Data
use
cases
in
financial
services.”
Infosys.,
19
Jul.
2014.
Web.
21
Nov.
2014.
5
Saraf,
Sanjay.
“Western
Union
Implements
Enterprise
Data
Hub
on
its
Path
to
Deliver
an
Omni-­‐channel
Customer
Experience.”
Cloudera.
n.d.
Web.
21
Nov.
2014.

5. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
5
unstructured
data
available.
Similar
to
the
micro-­‐level
customer
analysis
and
personalized
product
offerings,
Big
Data
uses
customer
segments
to
further
analyze
customer
behavior
and
spending
habits
to
increase
the
accuracy
of
risk
profiles
and
improve
firms’
risk
management
capabilities.
In
addition,
firms
can
draw
data
on
market
events
from
news,
reports,
social
media
and
other
sources
to
provide
further
insight
in
real-­‐time.
Firms
can
also
use
these
data
to
form
predictive
credit
risk
models
that
can
help
prioritize
customers
and
collection
activities.6
The
data
platform
should
be
flexible
and
adaptable
to
various
types
of
analytical
software,
and
be
able
to
process
data
in
real-­‐time.7
United
Overseas
Bank
successfully
tested
a
risk
system
based
on
Big
Data
and
managed
to
reduce
the
calculation
time
of
its
total-­‐bank
risk
from
about
eighteen
hours
to
only
a
few
minutes.
Thus,
banks
can
carry
out
stress
tests
in
real
time
and
react
more
quickly
to
new
risks
in
the
future.8
With
better
risk
management
capabilities,
firms
can
improve
fraud
detection.
Credit
card
fraud
has
become
more
sophisticated.
Today,
most
credit
card
thieves
avoid
making
big
purchases
with
credit
cards.
Instead,
they
make
many
smaller
transactions
that
amount
to
the
same
lump
sum.
For
example,
it
would
be
highly
suspicious
if
a
large
transaction
of
over
$50,000
was
made
to
purchase
a
diamond
ring,
but
if
a
customer
made
5,000
ten
dollar
transactions
at
various
locations,
it
would
be
harder
to
detect
the
fraud
purchase.
However,
these
frauds
can
be
easily
identified
with
the
help
of
Big
Data
through
proactive
analysis
of
geolocation,
point
of
sale,
authorization
and
transaction
data.9
For
example,
Big
Data
can
help
identify
ATMs
that
are
likely
to
be
targeted
by
fraudsters.10
In
many
cases
when
fraud
is
anticipated,
the
transaction
can
be
blocked
even
before
it
takes
place.
Zions
Bank,
a
subsidiary
of
Zions
Bancorporation
that
operates
more
than
500
offices
and
600
ATMs
in
ten
Western
U.S.
states
uses
MapR
as
a
critical
part
of
their
security
architecture.
By
using
MapR,
the
bank
is
able
to
predict
phishing
behavior
and
payments
fraud
in
real-­‐time,
and
minimize
their
impact,
as
well
as
run
more
detailed
analytics
and
forensics.
Zions
Bank
has
been
able
to
lower
storage
and
capacity
planning
costs
significantly,
as
well
as
increase
the
speed
of
their
analytics
activities.11
By
aggregating
all
these
data,
we
believe
that
it
may
be
possible
to
create
a
system
that
assigns
every
customer
a
latent
risk
score
in
the
near
future
that
will
greatly
assist
in
the
firms’
risk
management.
This
score
is
determined
based
on
past
transactions,
behaviors,
and
customer
interactions.
It
indicates
the
6
Kumar,
Anjani.
“Big
Data
use
cases
in
financial
services.”
Infosys.,
19
Jul.
2014.
Web.
21
Nov.
2014.
7
Shamgar,
Idor.
“5
Big
Data
Use
Cases
for
Banking
and
Financial
Services
–
Part
2.”
SAP.,
Blog.
21
Nov.
2014.
8
Huber,
Andreas,
Hannappel
Hauke,
Nagode
Felix.
“Big
Data:
Potentials
from
a
risk
management
perspective.”
Banking
Hub.,
01
Jul.
2014.
Web.
21
Nov.
2014.
9
“Financial
Services.”
Datameer.
n.d.
Web.
21
Nov.
2014.
10
Kumar,
Anjani.
“Big
Data
use
cases
in
financial
services.”
Infosys.,
19
Jul.
2014.
Web.
21
Nov.
2014.
11
“Combating
Financial
Fraud
with
Big
Data
and
Hadoop.”
MapR,
Hadoop.
18
Dec
2013.
Web.
21
Nov.
2014.

6. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
6
potential
risk
a
customer
possesses
and
the
threat
it
poses
to
the
firm.
With
this,
financial
services
firms
can
rank
their
customers
from
lowest
to
highest
in
terms
of
latent
risk,
and
can
put
more
scrutiny
and
attention
to
customers
of
high
risk.
With
the
relentless
growth
of
Big
Data,
financial
services
firms
need
to
acquire
the
right
talent
and
expertise
to
take
charge
of
the
data
analytics
in
their
firms.
Rising
demand
for
Big
Data
expertise
has
created
a
severe
skill
shortage
in
the
field
that
has
pushed
the
average
salary
to
$55,000
–
31%
higher
than
the
average
IT
position.
According
to
Financial
Times,
“Financial
service
was
also
the
most
commonly
cited
employer
in
Big
Data
advertisements,
accounting
for
about
20%
of
all
positions
in
the
industry
in
2013.”12
With
all
this
demand
and
competition
for
data
scientists,
firms
should
begin
to
scout
for
relevant
expertise
to
ensure
a
smoother
transition
into
Big
Data.13
Firms
should
also
invest
in
professional
training
and
development
for
current
employees
to
better
prepare
them
for
the
adoption
of
Big
Data
in
their
companies.
Overall,
Big
Data
is
of
great
value
to
the
financial
services
industry.
Financial
services
firms
need
to
invest
in
data
analytics
through
research
and
development,
training,
and
other
possible
ways
to
prepare
themselves
for
the
Big
Data
tidal
wave.
Firms
also
need
to
identify
and
define
business
capabilities
through
improved
insights
achieved
through
Big
Data,
and
develop
a
holistic
service
model
for
execution.
While
Big
Data
is
pertinent
to
the
transformation
of
the
industry,
behavioral
finance
is
yet
another
crucial
aspect
that
must
be
integrated
into
the
holistic
service
model.
Incorporating
Behavioral
Finance
Behavioral
economic
research
has
spent
many
years
in
the
“ivory
tower”
before
developing
into
a
more
mainstream
topic.
Acknowledging
that
investors
do
not
always
make
rational
decisions
benefitting
their
own
interests
is
an
essential
aspect
of
financial
decision-­‐making
and
needs
to
be
reflected
in
a
holistic
service
model.
Oftentimes,
financial
advisors
would
like
to
address
these
behavioral
issues
but
lack
diagnostics.
14
Thus,
a
holistic
service
model
needs
to
incorporate
behavioral
aspects
to
augment
client
interactions
with
wealth
managers
and
financial
planners.
Most
financial
advisors
use
a
standard
asset
allocation
program
in
which
they
first
administer
a
risk-­‐tolerance
questionnaire,
discuss
clients’
financial
goals
and
constraints,
and
then
follow
the
output
12
Warrell,
Helen.
“Demand
for
Big
Data
and
skills
shortages
drive
wages
boom.
“
Financial
Times.,
30
Oct.
2014.
Web.
21
Nov.
2014.
13
Ibid
14
How
Industry
Experts
Are
Making
Sense
of
Behavioral
Economics.
FinancialPlanning,
Feb.
2013.
Web.
28
September
2014

7. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
7
of
a
mean-­‐variance
optimization
–
a
quantitative
tool
to
make
allocations
by
considering
the
trade-­‐off
between
risk
and
return.
This
procedure
works
well
for
most
institutional
investors,
but
individuals
often
want
to
modify
their
asset
allocation
plan
in
response
to
short-­‐term
market
fluctuations
and
dramatic
news
that
negatively
impact
long-­‐term
investment
or
retirement
plans.
Table
1
lists
typical
behavioral
irrationalities
causing
unsound
client
behavior.
Behavioral
Bias
Description
Loss
aversion
The
tendency
to
feel
pain
of
losses
more
than
the
pleasure
of
gains.
Anchoring
and
adjustments
The
tendency
to
believe
that
current
market
levels
are
“right”
by
unevenly
weighting
recent
experiences.
Selective
memory
The
tendency
to
recall
only
events
consistent
with
one’s
understanding
of
the
past.
Availability
bias
The
tendency
to
rely
on
immediate
examples
that
come
to
a
person's
mind
when
thinking
of
a
certain
topic.
Overconfidence
The
tendency
to
overestimate
one’s
skill
and
experience
in
investing.
Present-­‐bias
The
tendency
to
favor
rewards
today
instead
waiting
till
tomorrow.
Regret
The
tendency
to
feel
deep
disappointment
for
having
made
incorrect
decisions.
Table
1:
Behavioral
irrationalities
impacting
financial
decision-­‐making
15
To
avoid
spending
valuable
time
on
modifying
investment
and
retirement
plans
later
on,
financial
planners
and
advisors
have
to
quickly
moderate
or
adapt
to
unsound
client
behavior.
Pompian
(CFA,
CFP)
and
Longo
(Ph.D.,
CFA)
rely
on
Kahneman’s
“best
practical
allocation”
model
to
suggest
an
asset
allocation
that
suits
clients’
natural
psychological
preferences
and
opposes
the
traditional
model
of
maximizing
expected
returns
for
a
pre-­‐determined
level
of
risk.16
Pompian
and
Longo
recommend
that
advisors
moderate
cognitive
biases,
such
as
selective
memory
and
present
bias,
and
adapt
to
emotional
biases
such
as
loss
aversion
and
regret.
Advisors
should
also
moderate
behavior
if
their
client’s
wealth
is
low
since
biases
and
irrational
behavior
can
jeopardize
financial
security.
Overall,
advisors
have
to
weigh
these
biases
for
a
“best
practical
allocation”
as
shown
on
the
biaxial
model
of
adapting
and
moderating
in
Attachment
B.
Currently,
most
mean
variance
outputs
only
allow
a
+/-­‐
10%
deviation
from
suggested
allocations.17
Financial
software
should
not
only
allow
adjustments
for
unsound
behavior
at
the
discretion
of
practitioners,
but
also
incorporate
behavioral
models
to
provide
guidance
to
practitioners.
For
example,
a
client
plans
to
retire
with
the
goal
to
not
outlive
his
assets
and
is
afraid
of
losing
money
since
he
still
remembers
the
Financial
Crisis
and
the
Dot
Com
bubble,
indicating
selective
memory
and
loss
aversion.
The
client
is
also
prone
to
anchoring
and
adjustments
since
he
15
Longo,
John
M.,
and
Miachel
M.Pompian.
The
Future
of
Wealth
Management:
Incorporating
Behavioral
Finance
into
Your
Practice.
Dartmouth
U,
n.d.
PDF
file.
26
October
2014.
16
Ibid
17
Longo,
John
M.,
and
Miachel
M.Pompian.
The
Future
of
Wealth
Management:
Incorporating
Behavioral
Finance
into
Your
Practice.
Dartmouth
U,
n.d.
PDF
file.
26
October
2014.

8. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
8
believes
current
market
levels
are
“right.”
Adapting
to
these
biases
would
lead
to
a
portfolio
with
mostly
bonds,
jeopardizing
the
client’s
financial
security.
Since
these
biases
are
principally
cognitive,
an
advisor
would
moderate
his
client’s
behavior
by
mixing
stocks
into
the
portfolio
and
administering
an
investor
education
program,
explaining
the
risk
of
outliving
one’s
assets.
The
key
to
incorporating
behavioral
models
into
asset
allocation
lies
in
evaluating
clients’
behavior
as
deeply
and
objectively
as
possible.
Standard
risk-­‐tolerance
questionnaires
do
not
fulfill
this
purpose
and
most
financial
advisors
lack
training
and
only
subjectively
evaluate
clients’
behavior.
Thus,
indicative
tests
have
to
be
developed
that
analyze
clients’
behavioral
biases
and
also
allow
input
from
advisor’s
firsthand
experience.
Designing
these
tests
requires
extensive
research
and
the
help
of
behavioral
scientists.
One
example
is
Merrill
Lynch’s
“Investment
Personality
Assessment”
which
is
mostly
administered
to
its
ultra-­‐high
net-­‐worth
clients
to
determine
their
“mindset
towards
risk,
preferred
investment
approach,
and
purpose.”18
Developing
tests
that
automatically
code
for
emotional
and
cognitive
biases
and
incorporating
these
results
into
asset
allocation
programs
will
facilitate
the
work
of
financial
advisors.
At
the
same
time,
financial
advisors
have
to
become
skilled
in
using
behavioral
cues
to
deduce
their
customers’
risk
tolerance
and
investment
objective,
which
will
also
help
fend
off
the
growing
competition
of
online
advising
and
wealth
management
robots.
For
example,
despite
agreeing
verbally,
customers’
physical
reactions
such
as
nervous
hand
movements,
an
agitated
voice,
sweat,
and
other
signs
can
inform
advisors
that
clients
are
not
comfortable
with
their
investment
plans.
These
attitudes
may
remain
hidden
unless
advisors
are
trained
to
recognize
non-­‐verbal
feedback,
which
reflects
the
importance
of
face-­‐to-­‐face
interactions
with
clients.
Current
allocation
models
do
not
only
need
revision
in
terms
of
emotional
and
cognitive
biases,
but
also
need
to
consider
the
definitions
of
risk
and
return.
Independent
of
the
investing
objective,
returns
are
usually
perceived
as
“potential
happiness.”
Often,
financial
advisors
and
planners
serve
as
life
planners
who
are
ultimately
concerned
about
their
client’s
comfort
and
happiness.19
Thus,
shifting
the
focus
from
pure
return
maximization
to
incorporating
comfort
and
potential
happiness
may
help
financial
planners,
behavioral
tests,
and
allocation
programs
determine
what
is
most
important
to
clients.
With
the
rise
of
various
online
competitors
offering
low-­‐cost
advising
and
wealth
management
alternatives,
it
is
evermore
important
for
advisors
to
offer
financial
advice
in
the
context
of
lifestyle,
future
plans,
and
personality
traits.
Since
computer
algorithms
lack
the
ability
to
find
underlying
motives
18
How
Industry
Experts
Are
Making
Sense
of
Behavioral
Economics.
FinancialPlanning,
Feb.
2013.
Web.
28
September
2014
19
Tomlinson
Joseph.
Behavioral
Finance—Implications
for
Investment
Planning.
Joe
Tomlinson,
n.d.
PDF
file.
26
October
2014.

9. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
9
and
life
goals
of
customers,
financial
advisors
have
to
build
their
service
model
around
understanding
the
customer
and
offering
individualized
services.
Various
studies
have
shown
that
personal
control
rather
than
income
predicts
people’s
happiness.20
Moreover,
most
people
experience
happiness
in
relation
to
the
fortunes
of
others.
Service
models
that
incorporate
such
behavioral
aspects
can
build
an
even
deeper
relationship
between
advisors
and
clients.
Similarly,
risk
should
be
considered
“potential
regret”.
Thus,
advisors
essentially
maximize
happiness
with
as
little
regret
as
possible.
21
Greg
Davies,
managing
director
and
head
of
behavioral
finance
and
investment
philosophy
at
Barclays,
defines
risk
as
the
“anxiety-­‐adjusted”
return,
taking
into
account
the
“anxiety,
discomfort,
and
stress”
a
client
endures.22
Based
on
individual
client
profiles,
financial
software
can
assist
advisors
by
evaluating
potential
investments
in
terms
of
experienced
risk
for
each
client.
For
instance
“potential
regret”
could
be
a
composite
measure
of
volatility,
intrinsic
risk,
and
news
coverage
of
an
asset,
which
is
then
automatically
evaluated
based
on
personality
tests.
Behavioral
models
are
not
only
important
in
asset
allocation
models
but
can
also
help
in
the
retirement
savings
crisis
by
using
behavioral
nudges
to
encourage
clients
to
save
and
invest.
According
to
the
Center
for
Retirement
at
Boston
College,
“the
fraction
of
workers
at
risk
of
having
inadequate
funds
to
maintain
their
lifestyle
through
retirement
has
increased
from
approximately
31%
to
53%
from
1983
to
2010.”23
Such
statistics
may
alarm
financial
planners
whose
goal
is
to
assure
their
clients
of
a
secure
retirement.
Financial
advising
software
needs
to
incorporate
social
proof
and
visualization
while
promoting
seamless
change
to
ensure
secure
retirement
for
clients.
Social
proof
refers
to
human’s
biological
predisposition
to
imitate
behavior.
It
is
an
evolutionary
adaptation
promoting
survival
over
thousands
of
generations.
Financial
planners
have
been
using
dramatic
messages
such
as
“61%
of
workers
report
less
than
$25,000
in
retirement
savings
to
convince
people
to
save
and
invest.”
However,
such
messages
may
inform
people
that
having
a
shortfall
is
a
normal
behavior
and
beguile
them
into
thinking
that
there
is
no
need
to
act.
In
fact,
people
with
only
$50,000
would
feel
great
about
themselves.
An
effective
application
of
social
proof
should
use
injunctive
norms
showing
success,
not
descriptive
norms
of
20
Nettle,
Daniel.
Happiness:
The
Science
behind
Your
Smile.
Oxford,
UK:
Oxford
UP,
2005.
Google
Books.
Web.
1
Jan.
2015.
21
Benartzi,
Shlomo,
and
Richard
H.
Thaler.
"Behavioral
Economics
and
the
Retirement
Savings
Crisis."
Science
339
(2013):
1152-­‐153.
Web.
27
Oct.
2014.
22
How
Industry
Experts
Are
Making
Sense
of
Behavioral
Economics.
FinancialPlanning,
Feb.
2013.
Web.
28
September
2014
23
Ibid

10. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
10
common
failure.
Thus,
financial
planners
can
encourage
financial
planning
by
telling
prospective
clients
“the
average
successful
retiree
had
an
account
balance
of
$750,000.”24
Moreover,
constantly
growing
databases
with
numerous
client
metrics
allow
financial
planners
to
use
social
proof
for
individual
clients
based
on
their
demographics.
At
the
same
time,
financial
advisors
need
to
take
advantage
of
technology
that
allows
clients
to
visualize
themselves
during
retirement.
Chip
and
Dan
Heath’s
prominent
model
considers
the
relation
between
an
elephant
and
its
rider
an
analogy
to
internal
decision-­‐making:
The
rider
is
rational
and
tries
to
steer
the
elephant;
however,
the
elephant,
driven
by
emotions,
is
more
powerful
and
can
overrule
the
rider.
Thus,
to
accomplish
behavioral
change,
messages
have
to
impact
people’s
emotions
and
provide
actionable
goals.
25
Clients
who
imagine
their
future
selves
vividly,
including
their
problems
and
needs,
are
better
prepared
for
retirement
and
more
motived
to
save.26
Hershfield
conducted
a
study
with
computer-­‐generated
digital
representation
of
people
as
they
age.
Seeing
an
avatar
of
themselves
in
the
future
significantly
increased
people’s
willingness
to
save
for
retirement.27
Joseph
Coughlin,
the
director
of
MIT's
AgeLab,
further
explains
the
importance
of
visualization:
“While
consumers
are
acutely
concerned
about
‘their
numbers’,
they
are
far
more
likely
to
understand
and
engage
in
discussion
around
products
that
are
connected
to
concrete
expenses
rather
than
an
ambiguous
goal
of
‘secure
retirement’”.28
To
prevent
decision
paralysis,
technology
has
to
aid
in
creating
vivid
and
concrete
forecasts
of
living
circumstances
during
retirement,
including
expected
and
unexpected
expenses.
The
most
crucial
step
toward
secure
retirement
is
establishing
an
automatic
investment
behavior.
Since
people
are
loss
averse
and
often
unwilling
to
give
up
money
today
to
invest
for
retirement,
behavioral
economists
developed
a
savings
plan
called
“Save
More
Tomorrow”.
Employees
commit
to
increasing
their
savings
rate
as
they
receive
pay
raises.
Since
the
increase
in
savings
rate
is
only
a
proportion
of
the
pay
raise,
there
is
no
decrease
in
discretionary
income.
29
At
the
first
company
which
implemented
this
plan,
participants
almost
quadrupled
their
saving
rate
from
3.5%
to
13.6%
in
24
Kitces,
Michael.
"Using
Social
Proof
To
Help
Clients
Make
Better
Financial
Planning
Decisions
|
Kitces.com."
Kitces.com:
Advancing
Knowledge
in
Financial
Planning.
30
Oct.
2013.
Web.
13
Dec.
2014.
25
Heath,
Chip,
and
Dan
Heath.
Switch:
How
to
Change
Things
When
Change
Is
Hard
New
York:
Broadway,
2010.
Print.
26
How
Industry
Experts
Are
Making
Sense
of
Behavioral
Economics.
FinancialPlanning,
Feb.
2013.
Web.
28
September
2014
27
Benartzi,
Shlomo.
Behavioral
Finance
in
Action.
Allianz
Global
Investors,
Mar.
2011.
PDF
file.
26
October
2014.
28
How
Industry
Experts
Are
Making
Sense
of
Behavioral
Economics.
FinancialPlanning,
Feb.
2013.
Web.
28
September
2014
29
Benartzi,
Shlomo,
and
Richard
H.
Thaler.
"Behavioral
Economics
and
the
Retirement
Savings
Crisis."
Science
339
(2013):
1152-­‐153.
Web.
27
Oct.
2014.

11. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
11
less
than
4
years.
Today,
more
than
50%
of
larger
employers
in
the
U.S.
offer
the
program.30
Innovative
technology
can
help
financial
planners
to
capitalize
on
“Save
More
Tomorrow,”
by
applying
the
concept
to
investing.
“Invest
More
Tomorrow”
serves
as
an
action
framework
that
overcomes
investor
paralysis
and
procrastination
since
clients
pre-­‐commit
to
have
pay-­‐raises
transfer
into
retirement/college/nursing/etc.
funds.
Advances
in
financial
software
can
facilitate
this
process
by
allowing
communication
and
potentially
even
integration
with
corporate
payroll
and
ERP
systems.
Besides
establishing
an
automatic
investment
behavior,
we
believe
advisors
have
to
increasingly
target
college
graduates.
Immediately
after
graduation,
most
college
graduates
experience
a
sudden
spike
in
disposable
income,
allowing
them
to
invest
excess
funds
and
benefit
from
compound
interest
due
to
their
young
age.
This
not
only
combats
the
retirement
crisis
but
also
ensures
extraordinary
gains
for
clients
by
avoiding
the
cost
of
delaying
investments
as
illustrated
in
Attachment
C.
In
order
to
appeal
to
the
younger
generation,
we
believe
advisors
have
to
make
themselves
more
available
and
fight
the
stigma
of
being
a
service
for
the
wealthy
and
elderly.
Even
though
generation
Y
wants
to
be
independent
and
handle
their
finances
themselves,
financial
advisors
are
more
qualified
to
help
them
plan
their
future.
Thus,
advisors
need
to
rebrand
themselves
and
highlight
how
their
convenient,
individualized,
and
experienced
services
can
help
recent
college
graduates.
To
do
so,
financial
advisors
may
start
with
educating
college
students
about
financial
planning,
investing,
and
retirement.
Even
though
college
students
are
educated
in
their
respective
discipline,
many
lack
financial
literacy.31
Thus,
financial
educational
programs
that
truly
aim
at
helping
students
can
be
an
excellent
starting
point
for
advisors
to
introduce
their
services
and
how
they
can
help
recent
graduates.
Overall,
incorporating
behavioral
aspects
into
a
holistic
service
model
helps
financial
advisors
to
retain
and
attract
customers,
while
differentiating
themselves
from
online
advising
robots.
Simultaneously,
advisors
benefit
from
better
understanding
their
clients’
needs
and
having
more
money
available
to
invest
so
their
clients
are
more
likely
to
achieve
secure
retirement.
Alternative
Financial
Services
The
financial
services
industry
is
undergoing
a
rapid
stage
of
flux.
The
old
saying
that
‘nothing
endures
but
change’
describes
pertinently
the
impact
of
disruptive
technology
on
wealth
management.
The
shortening
time
horizon
in
transactions
and
advances
of
efficient
technology
allow
new
service
models
to
emerge,
serving
the
needs
of
the
industry.
In
fact,
CNN
listed
the
top
15
financial
apps
and
30
Benartzi,
Shlomo.
Behavioral
Finance
in
Action.
Allianz
Global
Investors,
Mar.
2011.
PDF
file.
26
October
2014.
31
Bidwell,
Allie.
"Closing
the
Financial
Literacy
Gap
to
Combat
Student
Debt."
US
News.
U.S.News
&
World
Report,
3
Oct.
2013.
Web.
1
Jan.
2015.

12. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
12
sites
with
most
having
customized
portfolios,
free
advising
services,
mobile
platforms
accessibility,
and
real
time
trading
in
2014.32
Disruptive
technology
prompts
deliberations
on
how
consumers
will
seek
financial
advice,
where
technology
advancement
will
lead
the
industry,
and
how
financial
advising
should
best
adapt
to
the
new
environment.
In
order
to
acquire
new
customers,
online
competitors
have
already
taken
several
steps
to
incorporate
technologies
into
new
service
models.
For
instance,
new
service
models
offer
additional
features
such
as
automated
risk
assessments
using
Big
Data.33
Computerized
programs
then
match
individual
risk
tolerance
with
corresponding
ETFs.
Such
service
models
appeal
to
various
demographics
and
aim
to
provide
superior
services,
such
as
high-­‐speed
trading,
mobile
accessibility,
and
diversifiable
portfolios
without
forgoing
profits.
Conventional
service
models
should
target
multiple
demographics
by
offering
multiple
instruments
and
services.
We
believe
models
should
not
only
be
built
around
a
time
horizon,
risk
tolerance,
and
income
levels,
but
also
address
the
needs
of
different
genders,
generations,
and
ethnic
groups.
Traditionally,
the
absence
of
taking
transactional
fees
into
consideration
has
been
a
downside
to
various
finance
theories,
such
as
the
efficient
market
hypothesis
and
the
option-­‐pricing
model.
LOYAL3
and
Robinhood
are
online
platforms
for
fee-­‐free
investing.
This
empowers
investors
to
trade
freely
without
concern
for
the
underlying
fees
behind
each
transaction.
The
downside
of
these
sites
is
that
they
do
not
offer
real
time
trading
or
sufficient
investing
platforms,
such
as
providing
trades
only
on
apps.
In
general,
the
advantage
of
fee-­‐free
investing
will
become
less
significant,
since
transaction
and
service
fees
are
slowly
diminishing
in
the
foreseeable
future.
New
service
models
should
not
only
aim
to
profit
from
service
charges
but
rather
build
on
a
comprehensive
view
of
clients’
wealth.
In
addition,
financial
companies
are
also
conducting
services
in
a
more
personal
manner.
The
terms
wealth
management,
financial
claim,
and
client
relationship
management
aim
to
grow
a
closer
relationship
with
consumers
to
replace
traditional
terms
such
as
saving
and
borrowing.34
As
consumers
have
more
control
over
their
accounts,
their
influences
on
how
to
allocate
assets,
and
manage
risk
and
return
increases.
Hence,
service
models
should
incorporate
the
dynamics
of
consumer
behavior
to
accommodate
the
new
environment
as
well
as
to
serve
individual
needs.
Technology
has
revolutionized
the
traditional
practices
of
investing
and
led
to
a
new
stage
of
wealth
management.
Financial
advisors
from
investment
companies
have
to
learn
to
provide
32
"Save
with
Every
Purchase."
CNNMoney.
Cable
News
Network,
n.d.
Web.
12
Dec.
2014.
33
"Betterment
vs.
Wealthfront
-­‐
How
Do
These
Robo
Advisors
Compare?"Investor
Junkie.
N.p.,
28
July
2014.
Web.
34
Charles
S.
Sanford,
Jr.
"Financial
Markets
in
2020."
Proceedings.
1994.

13. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
13
information
digitally
and
rapidly.
PwC’s
research
forecasts
expenditure
on
mobile,
tablet,
and
social
networks
will
nearly
double
to
promote
interaction
digitally
with
clients
to
help
achieve
their
goals
within
the
minimum
time
frame.
Currently,
47%
of
communication
between
financial
advisors
and
clients
is
carried
digitally
through
emails,
text
messages,
and
social
networks
as
shown
in
Attachment
D.35
In
addition,
wealth
management
robots
promote
computer
programming
to
evaluate
most
of
the
risk
assessments.
This
enables
the
new
generation
to
look
for
wealth
models
that
are
convenient
and
fast
progression,
a
succinct
and
accurate
approach.
To
outperform
online
service
models,
retain
existing
clients,
and
attract
new
the
generation,
a
lifetime
model
helps
plan
for
clients’
future
expenses
such
as
education,
marriage
and
retirement.
This
model
will
consist
of
a
comprehensive
personal
wealth
account
that
includes
personal
assets,
such
as
housing,
cars,
savings,
etc.36
Owners
of
wealth
account
will
be
able
to
optimize
their
credit
margins,
manage
their
wealth,
allocate
funds
for
upcoming
events
such
as
vacations
and
weddings.
For
instance,
if
clients
indicate
an
early
interest
in
financing
a
house
or
moving
into
a
new
place,
wealth
accounts
will
provide
quick
evaluations
on
how
much
money
clients
are
going
to
need.
Automated
models
then
start
allocating
funds
periodically
to
ensure
sufficient
funds
will
be
available
to
finance
clients’
expenses.
To
visualize
such
transformation,
clients
may
indicate
a
preference
of
traveling
at
the
end
of
the
year
on
their
accounts.
By
doing
so,
a
subaccount
will
be
generated
to
start
taking
off
partial
returns
from
clients’
portfolios.
At
the
end
of
the
year,
an
account
indicated
as
“vacation”
will
be
ready
to
use
for
clients.
Clients
neither
have
to
make
any
changes
for
their
investments
nor
worry
about
market
fluctuations
if
additional
funding
is
needed
in
the
future.
This
also
ensures
funds
will
continue
generating
profits
instead
of
sitting
aside
in
checking
accounts
until
usage
for
future
purposes.
Transcending
wealth
management
is
essential
such
that
advisors
are
able
to
develop
a
lifetime
relationship
with
clients,
not
only
managing
their
wealth,
but
also
assisting
them
to
plan
for
their
future
expenses
and
allocate
funds
according
to
any
extenuating
circumstances.
Unlike
traditional
advising
that
depends
primarily
on
financial
advisors,
investors
now
rely
on
inputs
and
collective
thinking
from
peers
whether
they
are
choosing
wealth
advisors
or
purchasing
financial
instruments.37
For
instance,
wars,
oil
price
fluctuations,
currency
risk,
and
many
global
affairs
become
growing
concerns
for
investors.
New
service
models
should
be
able
to
provide
instant
and
professional
customer
service,
such
as
instant
messaging
or
chat
options
if
clients
so
desire.
Global
events
can
often
trigger
disastrous
effects
in
markets.
Advisors
should
be
able
to
reassure
clients
in
real-­‐
35
Crosby,
C.
Steven,
Jensen,
Jeremy,
Ong,
Justin.
Navigating
to
Tomorrow:
Serving
Clients
and
Creating
Value.
PDF
file.
36
Charles
S.
Sanford,
Jr.
"Financial
Markets
in
2020."
Proceedings.
1994.
37
Venkateswaran,
S.,
&
Vaed,
K.
(2013).
The
future
of
wealth
management
services.
FT.Com,

14. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
14
time
and
prevent
them
from
making
rash
decisions.
This
provides
financial
advisors
with
an
edge
over
self-­‐managed
and
algorithm-­‐based
online
advisors.
Although
investors
are
drifting
away
from
traditional
financial
practices
through
phone
calls
and
brokers,
they
continue
to
seek
improved
and
more
precise
financial
advice.38
In
fact,
societal
change
is
inclined
to
strengthen
the
bond
between
clients
and
advisors.
While
companies
are
seeking
new
technology
and
predicting
upcoming
changes
of
the
market,
they
should
not
forget
the
goal
of
accomplishing
outstanding
relationships
with
clients.
Self-­‐managed
portfolios
are
a
rising
threat
to
financial
advisors.
Online
applications
allow
investors
to
monitor
the
market
remotely
and
devise
their
own
investment
strategies
to
obtain
higher
returns.
Websites
such
as
Macroaxis,
Investopedia,
Wikinvest,
and
other
open
source
intelligences
provide
services
free
of
charge,
analyses,
and
user
friendly
platforms
to
access
information
about
the
markets.
Although
they
do
not
provide
outstanding
services
and
analyses
that
firms
like
Morningstar
and
Bloomberg
do,
technology
allows
individuals
access
to
financial
advice
and
the
ability
to
share
them
with
others
in
a
more
accessible
and
affordable
manner.
Hence,
the
comparative
advantages
for
wealth
management
firms
have
to
be
substantial
to
offset
the
cost
of
seeking
financial
advice.
In
fact,
sites
such
as
‘Seeking
Alpha’
provide
analytical
services
and
additional
insights
from
industry
experts
such
that
investors
can
obtain
an
overview
of
companies’
performance
and
strategies.39
However,
unreliable
information
from
uncertified
experts
can
result
in
confusion
and
inaccuracy.
Investors
have
to
spend
time
researching
on
their
own
to
gather
useful
data.
Many
consider
the
process
to
be
lengthy
and
time
consuming.
In
spite
of
the
shortcomings,
consumers
are
now
able
to
choose
among
various
alternatives
and
platforms
to
pursue
independent
financial
advice
and
manage
their
portfolio
themselves.
Wealth
management
is
moving
to
a
more
complex
model
to
serve
a
wider
range
of
consumer
demographics
from
age,
income,
geographical
data,
gender,
and
behavior.
According
to
Movenbank,
42%
of
mass
affluent
clients
will
belong
to
generation
Y
by
2020.40
To
serve
and
capture
the
attention
of
generation
Y,
it
is
essential
to
accommodate
their
needs
to
seek
the
best
alternatives.
One
of
the
best
approaches
is
to
identify
their
interests.
In
particular,
Generation
Y
is
viewed
as
technologically
aware
with
desires
for
higher
return
and
lower
risk.
The
retention
of
clients
becomes
a
challenge
as
the
new
generation
constantly
seeks
new
opportunities
such
as
online
services
with
independent
advising
and
investment
offerings.41
38
Ibid
39
"About
Seeking
Alpha."
Seeking
Alpha.
N.p.,
n.d.
Web.
30
Nov.
2014.
40
Armstrong,
David.
"The
Advisor
of
the
Future."
The
Advisor
of
the
Future.
N.p.,
n.d.
Web.
19
Jan.
2015.
41
Crosby,
C.
Steven,
Jensen,
Jeremy,
Ong,
Justin.
Navigating
to
Tomorrow:
Serving
Clients
and
Creating
Value.
PDF
file.

15. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
15
The
automated
portfolio
solutions
commonly
known
as
robots
catch
plenty
of
attention
lately
due
to
their
emergence
in
the
financial
services
industry.
A
recent
study
from
Oxford
University
estimated
that
robots
will
replace
60%
of
financial
advisors
in
the
future.
42
The
conventional
practices
of
setting
high
expectations
and
providing
lengthy
reports
have
become
obsolete.
Robo-­‐advisors
such
as
Wealthfront
first
examine
investors’
risk-­‐tolerance
and
then
categorize
them
into
one
of
ten
possible
portfolio
models.
These
models
consist
of
inexpensive
ETFs
which
come
from
various
asset
classes.
An
algorithm
then
allocates
assets
between
taxable
and
non-­‐taxable
accounts
to
maximize
returns.
Another
algorithm
tracks
the
error
of
each
component
against
comparable
indices
and
makes
adjustments
if
necessary.
Similarly,
FutureAdvisor
links
to
their
clients’
401(k)
and
taxable
investment
accounts.
Clients’
portfolio
holdings
are
compared
to
numerous
investment
options,
and
FutureAdvisor’s
algorithm
then
suggests
specific
recommendations
of
index
funds
and
other
asset
classes.
This
service
is
currently
free
of
charge
and
poses
a
significant
threat
to
advisors’
traditional
service
model.43
Understanding
clients’
advising
and
investment
alternatives
is
essential
to
foster
long-­‐term
relationships
between
clients
and
advisors.
Financial
advisors
help
clients
to
set
realistic
goals,
and
pinpoint
useful
information
from
a
pool
of
data.
Developing
outstanding
customer
service
is
key
to
the
everlasting
success
for
advisors
that
could
not
easily
be
replaced
by
automated
robots.44
While
various
functionalities
of
online
resources
continue
to
emerge,
it
is
crucial
for
financial
advisors
to
understand
them
and
improve
upon
them
based
on
what
they
are
currently
missing.
The
science
of
wealth
management
has
been
diverted
into
a
passive
movement
due
to
the
changing
environment.
Wealth
management
should
continue
to
take
an
active
measure
in
order
to
develop
a
more
sophisticated
service
model.
Subsequently,
financial
advisors
should
recognize
the
use
of
technology
and
learn
how
to
provide
adequate
financial
advice
to
investors
with
new
ways
of
communication
through
technology.
Technology
has
enabled
the
dynamics
of
the
financial
world.
At
the
same
time,
having
the
knowledge
of
financial
instruments
is
no
longer
enough
for
financial
firms
to
prove
their
success.
Despite
the
emphasis
on
technology
and
detaching
the
focus
of
face-­‐to-­‐face
interactions,
client
relationship
management
remains
crucial
for
success.
42
Carlson,
Ben.
"How
Financial
Advisors
Can
Fend
Off
the
Robots
-­‐
A
Wealth
of
Common
Sense."
A
Wealth
of
Common
Sense.
N.p.,
04
Apr.
2014.
Web.
22
Jan.
2015.
43
Veres,
Bob.
"The
Most
Underappreciated
Threat
to
the
Advisory
Business."
The
Most
Underappreciated
Threat
to
the
Advisory
Business.
N.p.,
n.d.
Web.
22
Jan.
2015.
44
Ibid

16. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
16
Conclusion:
The
Holistic
Service
Model
Big
Data,
behavioral
finance,
and
technology
usage
should
be
integrated
into
a
holistic
service
model,
which
still
maintains
personal
and
face-­‐to-­‐face
client
interactions.
Big
Data
technology
allows
firms
to
gain
insights
into
their
customers
and
prospects,
discover
investment
opportunities,
and
assist
with
risk
management
and
compliance.
New
service
models
incorporating
Big
Data
will
be
able
to
meet
and
transcend
customers’
ever-­‐changing
demands
and
overcome
potential
threats
created
by
self-­‐
managed
services
and
robo-­‐advisors.
Behavioral
models
assess
unsound
client
behavior
and
aid
practitioners
in
moderating
or
adapting
to
such
behavior.
By
addressing
cognitive
and
emotional
biases
and
redefining
risk
and
return
in
terms
of
behavioral
aspects,
the
new
service
model
increases
the
degree
of
individualization
and
goes
beyond
purely
quantitative
measures
mainly
offered
by
wealth
management
robots.
As
another
essential
part
of
the
holistic
service
model,
behavioral
science
also
helps
encourage
clients
to
save
and
invest.
Technology
helps
identify
future
competitors
and
recognize
changes
in
the
competitive
environment.
New
developments
such
as
wealth
management
robots
and
the
rapid
growth
of
generation
Y
clientele
need
to
be
addressed
with
urgency
in
order
for
traditional
firms
to
preserve
their
dominance
in
the
industry.
In
general,
advisors
should
use
technology
to
reduce
cost,
bolster
the
bond
with
customers,
and
incorporate
successful
aspects
of
e-­‐services.
The
new
service
model
should
be
able
to
adapt
easily
to
the
new
environment
in
order
to
serve
individual
needs.
Incorporating
Big
Data,
behavioral
insight,
and
technology
into
a
holistic
service
model
augments
services
and
client
interactions
of
wealth
managers
and
financial
planners,
allowing
them
to
build
long-­‐term
relationships
with
clients
that
trump
online
wealth
management
tools.
At
the
same
time,
the
holistic
service
model
provides
wealth
managers
and
financial
planners
with
a
competitive
edge
over
emerging
e-­‐services
that
often
lack
resources
to
provide
a
credible,
customized,
and
holistic
service
model.

17. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
17
Attachements
A:
Urgent/Important
Matrix45
There
are
four
quadrants
to
the
urgent/important
matrix.
Customer
segments
can
then
be
ranked
from
highest
to
lowest
in
terms
of
significance.
If
a
customer
segment
has
high
importance
and
high
urgency,
firms
should
act
on
that
segment
before
all
other
segments.
Then,
if
a
customer
segment
is
placed
in
the
high
urgency
and
low
importance
or
vice
versa,
they
should
be
addressed
next.
Lastly,
the
segments
with
low
urgency
and
importance
can
either
be
ignored
or
acted
upon
last
if
needed.
45
Eisenhower,
Dwight
D.
“Eisenhower
Matrix.”
University
of
California.
31
Jan.
2015.

18. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
18
B:
Model
for
Adapting
and
Moderating
Biases46
C:
Cost
of
Delaying
Investing47
Investor
A
starts
investing
at
age
25
and
is
investing
$5,000
each
year.
Investor
B
is
doing
the
same
but
starts
10
years
later.
If
both
investors
earn
6%
interests
each
year
and
take
out
their
money
at
age
65,
Investor
A
will
have
accumulated
49%
more
in
savings
due
to
compound
interest.
46
Longo,
John
M.,
and
Miachel
M.Pompian.
The
Future
of
Wealth
Management:
Incorporating
Behavioral
Finance
into
Your
Practice.
Dartmouth
U,
n.d.
PDF
file.
26
October
2014.
47
"The
Power
of
Compound
Interest."
-­‐Why
You
Should
Start
It
Early.
HBSC
Bank
USA.
Web.
19
Jan.
2015.

19. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
19
D:
Prospect
development
of
wealth
management48
PwC
conducted
a
survey
in
2013
to
forecast
the
upcoming
challenges
and
changes
in
private
banking
and
wealth
management
industry.
As
predicted
by
financial
advisors,
operations
in
wealth
management
will
grow
more
personally
and
digitally
in
the
next
two
years.
In
order
to
stay
competitive
and
build
stronger
bonds
with
clients,
expenditure
will
focus
on
improving
and
outsourcing
new
functions
to
serve
and
strengthen
new
service
models.
The
next
survey
shows
how
financial
advisors
perceive
companies’
current
position.
Achieving
an
adaptable
and
efficient
process
and
technology
platform
is
one
of
the
priorities
of
wealth
management
industry.
For
instance,
new
service
models
should
incorporate
the
use
of
smartphones
and
tablets,
real
time
trading,
and
accessible
financial
advice
and
services.
48
Crosby,
C.
Steven,
Jensen,
Jeremy,
Ong,
Justin.
Navigating
to
Tomorrow:
Serving
Clients
and
Creating
Value.
PDF
file.

20. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
20

21. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
21
Bibliography
Armstrong,
David.
"The
Advisor
of
the
Future."
The
Advisor
of
the
Future.
N.p.,
n.d.
Web.
19
Jan.
2015.
"About
Seeking
Alpha."
Seeking
Alpha.
N.p.,
n.d.
Web.
30
Nov.
2014.
Asani
Sarkar
&
Kai
Li,
2002.
"Should
U.S.
investors
hold
foreign
stocks?,"
Current
Issues
in
Economics
and
Finance,
Federal
Reserve
Bank
of
New
York,
vol.
8(Mar).
Benartzi,
Shlomo,
and
Richard
H.
Thaler.
"Behavioral
Economics
and
the
Retirement
Savings
Crisis."
Science
339
(2013):
1152-­‐153.
Web.
27
Oct.
2014.
Benartzi,
Shlomo.
Behavioral
Finance
in
Action.
Allianz
Global
Investors,
Mar.
2011.
PDF
file.
26
October
2014.
"Behavioral
Finance:
Key
Concepts
-­‐
Prospect
Theory."
Investopedia.
4
Feb.
2007.
Web.
13
Dec.
2014.
<http://www.investopedia.com/university/behavioral_finance/behavioral11.asp>.
"Betterment
vs.
Wealthfront
-­‐
How
Do
These
Robo
Advisors
Compare?"Investor
Junkie.
N.p.,
28
July
2014.
Web.
"Big
Data
and
Apache
Hadoop
for
Financial
Services."
MapR,
Hadoop.
n.d.
Web.
21
Nov.
2014.
<https://www.mapr.com/solutions/industry/big-­‐data-­‐and-­‐apache-­‐hadoop-­‐financial-­‐services>.
Bidwell,
Allie.
"Closing
the
Financial
Literacy
Gap
to
Combat
Student
Debt."
US
News.
U.S.News
&
World
Report,
3
Oct.
2013.
Web.
1
Jan.
2015.
Carlson,
Ben.
"How
Financial
Advisors
Can
Fend
Off
the
Robots
-­‐
A
Wealth
of
Common
Sense."
A
Wealth
of
Common
Sense.
N.p.,
04
Apr.
2014.
Web.
22
Jan.
2015.
Charles
S.
Sanford,
Jr.
"Financial
Markets
in
2020."
Proceedings.
1994.
“Combating
Financial
Fraud
with
Big
Data
and
Hadoop.”
MapR,
Hadoop.
18
Dec
2013.
Web.
21
Nov.
2014.
Connors,
Shawn,
Courbe,
Julien,
Varsha,
Waishampayan.
“Where
have
you
been
all
my
life?
How
the
financial
services
industry
can
unlock
the
value
in
Big
Data.”
PwC.,
1
Oct.
2013.
Web.
21
Nov.
2014.
Crosby,
C.
Steven,
Jensen,
Jeremy,
Ong,
Justin.
Navigating
to
Tomorrow:
Serving
Clients
and
Creating
Value.
PDF
file.
"Deriving
Business
Insight
from
Big
Data
in
Banking."
IBM.
n.d.
Web.
21
Nov.
2014.
<http://www-­‐
01.ibm.com/software/data/bigdata/industry-­‐banking.html>.
“Financial
Services.”
Datameer.
n.d.
Web.
21
Nov.
2014.
Gerneglia,
Bill.
“Finding
Value
in
Open
Data
Vs
Big
Data.”
myBigDATAview.,
Blog.
21
Nov.
2014.

22. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
22
Heath,
Chip,
and
Dan
Heath.
<i>Switch:
How
to
Change
Things
When
Change
Is
Hard</i>.
New
York:
Broadway,
2010.
Print.
How
Industry
Experts
Are
Making
Sense
of
Behavioral
Economics.
FinancialPlanning,
Feb.
2013.
Web.
28
September
2014
Huber,
Andreas,
Hannappel
Hauke,
Nagode
Felix.
“Big
Data:
Potentials
from
a
risk
management
perspective.”
Banking
Hub.,
01
Jul.
2014.
Web.
21
Nov.
2014.
Kahneman,
Daniel,
and
Amos
Tversky,
(1979).
“Prospect
Theory:
An
Analysis
of
Decision
under
Risk.”
Econometrica
47.2
(1979):
263-­‐291.
Web.
27
Oct.
2014.
Kitces,
Michael.
"Using
Social
Proof
To
Help
Clients
Make
Better
Financial
Planning
Decisions
|
Kitces.com."
Kitces.com:
Advancing
Knowledge
in
Financial
Planning.
30
Oct.
2013.
Web.
13
Dec.
2014.
Kumar,
Anjani.
“Big
Data
use
cases
in
financial
services.”
Infosys.,
19
Jul.
2014.
Web.
21
Nov.
2014.
Longo,
John
M.,
and
Miachel
M.Pompian.
The
Future
of
Wealth
Management:
Incorporating
Behavioral
Finance
into
Your
Practice.
Dartmouth
U,
n.d.
PDF
file.
26
October
2014.
Nettle,
Daniel.
Happiness:
The
Science
behind
Your
Smile.
Oxford,
UK:
Oxford
UP,
2005.
Google
Books.
Web.
1
Jan.
2015.
"Save
with
Every
Purchase."
CNNMoney.
Cable
News
Network,
n.d.
Web.
12
Dec.
2014.
Saraf,
Sanjay.
“Western
Union
Implements
Enterprise
Data
Hub
on
its
Path
to
Deliver
an
Omni-­‐channel
Customer
Experience.”
Cloudera.
n.d.
Web.
21
Nov.
2014.
Shamgar,
Idor.
“5
Big
Data
Use
Cases
for
Banking
and
Financial
Services
–
Part
2.”
SAP.,
Blog.
21
Nov.
2014.
Thaler,
R.
H.
"Mental
Accounting
and
Consumer
Choice."
Marketing
Science
27.1
(2008):
15-­‐25.
Web.
13
Nov.
2014.
"The
Power
of
Compound
Interest."
-­‐Why
You
Should
Start
It
Early.
HBSC
Bank
USA.
Web.
19
Jan.
2015.
Tomlinson
Joseph.
Behavioral
Finance—Implications
for
Investment
Planning.
Joe
Tomlinson,
n.d.
PDF
file.
26
October
2014.
Venkateswaran,
S.,
&
Vaed,
K.
(2013).
The
future
of
wealth
management
services.
FT.Com,
Veres,
Bob.
"The
Most
Underappreciated
Threat
to
the
Advisory
Business."
The
Most
Underappreciated
Threat
to
the
Advisory
Business.
N.p.,
n.d.
Web.
22
Jan.
2015.
Wagle,
Likhit.
“What’s
Driving
Financial
Services?
Think
Big
Data.
”
Forbes.,
31
Jul.
2013.
Web.
21
Nov.
2014.

23. DST
Robert
L.
Gould
Scholastic
Award
[2014
-­‐2015]
23
Warrell,
Helen.
“Demand
for
Big
Data
and
skills
shortages
drive
wages
boom.
“
Financial
Times.,
30
Oct.
2014.
Web.
21
Nov.
2014.