2. PwC
Agenda
2
• Our Perspective
• Background on Robo-Advice
• Overview of $ecureTM
• Value Proposition For Stakeholders
• Appendices
3. PwC
The market for financial education, advice, and distribution of
products and services is undergoing significant changes
Competitive Landscape
401 K & 403B Providers
Specialization in
retirement products
reveals successful
marketing and
distribution methods for
complex products
The maturity of Health &
Wellness Programs
illustrates employee
engagement and education
best practices
Health & Wellness
Providers
Indicate technology trends
within the space and
potential disruptors
Emerging Players
Knowledge and use of a
variety of products
demonstrate employee
and employer preferences
around product offer mix
Financial
Planners
ILLUSTRATIVE
3
Competitive Landscape
4. PwC
Fueled by important megatrends, Robo-Advisors are gaining in
prominence with significant VC funding and adoption
4
The Emergence Of Automated Financial Advice
… are feeding the rise of “robo advisors” in financial services
Today’smega-
trends…
• From now until 2020, data
generated globally will
double every two years and
attain 40T GB in size
• Machine Learning and AI are
going mainstream – a number of
hedge funds (e.g. Bridgewater
Associates) are leveraging AI
in hedge funds
• As Boomers retire, the next three
decades will bear witness to the
greatest wealth transfer ($59T)
in U.S. history
• U.S. Millenials have grown up
with online/mobile platforms in an
always-on digital world - 90% are
almost always or always online
• The financial advisory business is
being forced to reckon with pricing
disruptions
With higher required
minimums, Fidelity charges 63
– 170 bps for managed accounts
With no minimum balances,
Betterment charges 15 – 35 bps
depending on account size
Technology
Acceleration
Evolving Customer
Behaviors
Financial Services
Margin Pressure
More than 200
companies have
entered the digital wealth
management business
since 2009
Robo Advisors raised
$290M in VC funding
in 2014, 2X the total in
2013 and 10X the total in
2010
Recent fundraising
activity for comparable
startups has valued
these companies
based on at least 25X
revenue
Four of the largest
robo advisors manage
less than $10B
combined, a miniscule
fraction of the $17T
managed by U.S. wealth
advisors
Sources: “The Digital Universe Of Opportunities”, EMC/IDC (Apr 2014), “This Hedge Fund Is Seeking an Artificial Intelligence Edge”, Foxman S. & Clark J.,
Bloomberg.com (Jul 14, 2015), “Coming soon: The biggest wealth transfer in history”, Harjani A., CNBC.com (Jan 13, 2015), “Digital lives of Millennials”, American
Press Institute (Mar 16, 2015) “Putting Robo Advisers to the Test”, Moyer L., WSJ (Apr 24, 2015), “Investors Snap Up Online Financial Advisers”, Demos T., WSJ
(Feb 12, 2015), “The Future Of Financial Services”, Final Report, World Economic Forum (June 2015), Fidelity & Betterment corporate websites
5. PwC
There has been a flurry of activity both in terms of new offerings
developed internally and strategic acquisitions in this space
5
Industry Response
The FinTech world has been awash in such deals - this year Personal Finance
Management (PFM) transaction volume has already surpassed $1B.
Partnerships and new offerings
involving robo-advisors
M&A involving robo-advisors
Sources: “As Envestnet Buys Yodlee For $590M, Total PFM Acquisitions In Advisor #FinTech Crosses $1B!”, Kitces M., Kitces.com – 9 Aug 2015), Web clippings
from The Wall Street Journal, Investment News, Forbes, ThinkAdvisor and The Philadelphia Inquirer
6. PwC
Hedge funds and AWM players have started recruiting, partnering,
and acquiring AI & data and analytics companies and players
6
Industry Response
• Analyzes 130,000 things
that people do every day
• Identifies 85 million
individual behavioral
patterns
• UBS uses it for
personalized advice to
wealthy clients
Artificial Intelligence in Personalized Advice
• Bridgewater Associates
creates AI group
• Rebellion Research uses
machine learning to
analyze thousands of
variables each day
Machine Learning & AI in Asset Management
7. PwC 7
Enablement Education Guidance Support Motivation
Adapt program
to employees’
busy lifestyles
Provide
digestible,
impactful,
personalized
content
Provide
personalized
guidance and
ongoing
feedback to
celebrate
success and
recover from
failure
Integrate social
elements (e.g.,
ability to share
goals and
progress or
compete with
others). Social
support
increases
motivation and
is inherently
rewarding
Improve
incentive
structures to
align with the
desired pattern
of behavior and
maximize
impact
PillarsofChange
ApplicationtoEmployee
Well-Being
In offering holistic solutions, effective strategies must leverage the
principles of sustainable behavioral change…
Based on research in behavioral economics and psychology, we have identified five pillars that capture the best practices
related to engaging and empowering employees to achieve sustainable behavior changes
Source: PwC’s Fall 2014 HR Innovation
Our Perspective (2/3)
8. PwC 8
• 1.8 billion internet users in 2010 to 5 billion users by
2020
• Connected devices will far outnumber the world
population. 500 million connected devices in 2003 & 50
billion by 2020
• Wellness program providers strive to leverage technology to
incorporate incentives, coaching and competition to
drive desirable outcomes
• Wellness program providers are also investing in mobile
apps/technology and user profiling/personalization
…and technology can be used to support this change
Wellness program providers are embracing technology to drive awareness, engagement and better outcomes for employees
Source: PwC’s Fall 2014 HR Innovation
1. Employers were surveyed to determine current state features of their wellness
programs and any future state interests
Technology and the Five Pillars of Change Wellness Program Features
Our Perspective (3/3)
9. PwC
Household finances and preferences may be organized into
household financial statements (HFSs) …
9
02Income Statement
Household level income and expenses
pertaining to each member of the
household and applicable dependents
04Behavioral Preferences
Behavioral tendencies exhibited by the
members of the household, which drive
accumulation and consumption decisions
The Household Financial Statement
• Describes the financial position and outlook
attributable to the client, as well as, their
spouse/partner
• Takes stock of liabilities associated with
dependents (children, elderly parents, etc.)
• Captures the behavioral attributes
uniquely associated with each household
Balance Sheet
A view of the combined assets, liabilities
and resulting net personal equity (NPE)
associated with each member of the
household
03
Demographics / Family Structure
A demographic profile of the household
that is used to project liabilities and
understand consumption over time
01
“Household Financial
Statements evolve over
the course of time”
“Household Financial
Statements vary by HH
situations and aspirations”
Introducing The Household Financial Statement (HFS)
10. PwC
… which may be used to estimate accumulation levels that can
fund the retirement needs of the entire household
10
Benefits Associated With The HFS
Targeted Solutions:
• Incorporate the entire household (client +
spouse/ partner + survivors) in the planning
process
• Leverage behavioral triggers to influence clients
and promote prudent savings habits
• Segments customers into tiers and allow
prioritized targeting
Key Benefits:
• Personalized planning differentiate and
improve sales
• Gain an understanding of all household
assets beyond the client account
• Forge deep advisory relationships
capitalize on opportunities to engage with
spouse / future generations
11. PwC
However, operationalizing this vision requires firms to
address two key capability gaps
11
Meeting The Challenge
• Augment recordkeeping data with
estimates of other household assets and
liabilities and creation of household
financial statements
• Estimate family and behavioral attributes
associated with each household
Holistic View of Individuals and Households
• Project HFSs using behavioral
simulation models, which remain true to
the unique behavioral traits exhibited by
each participant household
• Evolved scenario analysis (economic +
health shocks) during the simulation is
critical in ensuring optimal outcomes
Understand Past and Future Behaviors
1 2
12. PwC
More advanced cognitive robo-advisors that can fully
exploit the emerging advances in AI technology address
these needs
12
Meeting The Challenge
Evolution of Robo-Advisors
Standalone
Robo-advisors
Self-directed
consumers
• Aggregation
• Trade execution
Integrated Robo-
advisors
Advisors and
End Consumers &
Providers
• Retail & Institutional
products
• Assisted Advice
• Predictive models
Cognitive Robo-
advisors
Time
Advisors, End
Consumers &
Providers
• Economic & market
outlook
• Enhanced & Holistic
Advice
• Machine learning
• Agent-based
modeling
13. PwC
Most robo-advisor platforms are either standalone or
moving towards an integrated advisor-client model; with
very few cognitive robo-advisors in the market
13
Meeting The Challenge
Evolution of Robo-Advisors
Standalone
Robo-advisors
Integrated Robo-
advisors
Cognitive Robo-
advisors
Time
14. PwC
In addition, the vast majority of these robo-advisor
platforms are focused on the accumulation stage as opposed
to the decumulation or retirement income stage
14
Meeting The Challenge
Working Age (Ages 20–49) Retirement (Ages 65+)
MassMarket
Pre-retirement (Ages 50-64)
HNWandUHNW
= Advice Need
Assessment
= Product
Advice
= Portfolio
Allocation
Service ProvidedCatered Towards
A - Advisors
B - Both
C - Clients
Accumulation
Decumulation
A B
C
C A
A
B
C
B
B
B C
C
CC
C
C
B
C
B
B
B
B A
A A
B
A
C C
C
C
15. PwC
$ecureTM is a cognitive robo-advisor that leverages six key features
to address the consumer, advisor and financial service provider
needs
15
2
Synthetic US
Population
/Household
Cradle to
Grave
Simulations
Scenario
Based
Planning
Behavioral
Economics &
Simulation
Holistic
Household
View
1
3
4
5
$ecure
$ecure - Overview
16. PwC
$ecureTM models the entire household, their life events, balance
sheet, income statement and financial choices
16
$ecureTM - Holistic Household View
Account Details
• Account Value
• Number of years
• Advisor
• Number of customer
service contacts
Life Events
• Getting married
• Buying a house
• Having a child
• Retiring
Balance Sheet
• Assets
- Home
- Financial assets
• Liabilities
- Mortgage
- Personal debt
Choices
• Rational
• Behavioral
- Mental accounting
- Joint decision
making
- Financial literacy
Household
Composition
• Age of head of
household
• Marital status
• Number of children
and dependents
Income Statement
• Salary
• Expenses
- Nondiscretionary
- Discretionary
- Health costs
17. PwC
$ecureTM combines a large number of data sets to develop a simulated,
complete picture of the household balance sheet
17
$ecureTM – Synthetic US Population/Household
Developing the Full Synthetic
Household Balance Sheet
Client
Internal
Data
$ecureTM uses stochastic statistical matching techniques to create a full synthetic
population built on client customer data and augmented with a wide range of public and third
party data, both structured and unstructured
Additional
Third Party
Data
Additional
Third Party
Data
Third Party
Data
Additional
Third Party
Data
Additional
Third Party
Data
Public Data &
Social Media
Data
Additional
Third Party
Data
Additional
Third Party
Data
Proprietary
PwC Data
Selected data sets used:
Client data:
1. Account balances
2. Product details
3. Demographic information
4. Transactional data
Publicly available data:
1. Bureau of Labor Statistics (BLS) – Consumer Expenditure
Survey (CES) of US households’
2. Employee Benefits Research Institute (EBRI)
3. National Bureau of Economic Research (NBER)
Proprietary/ 3rd Party Licensed data:
1. MacroMonitor data
2. Nielsen-Claritas or Acxiom data
3. Proprietary PwC Surveys
18. PwC
Augmenting internal client data with 3rd party data can enrich the depth
and level of detail of customer information
18
Selected External Information Sources Ascertainable Client Information
Assets / account holdings
– By account type (e.g., brokerage, IRA, 401k)
– By product type (e.g., equities, bonds, deposits)
– Total balances across providers
– Non-financial assets (e.g., home equity, business ownership)
Channel preferences
– Self-directed
– Advised
– Discretionary
Risk appetite
– Aggressive / focused on growth
– Defensive / focused on preservation
Recent and impending life events
– Inheritance (probates)
– Marriage / divorce
– Job change or move
Lifestyle
– Non-financial asset purchases (homes, cars)
– Purchase patterns
Personal
– Residence (ownership status, duration, property details)
– Vehicle (year, model, affinity, ownership status)
– Health (conditions, needs, brand preferences)
Digital preferences
– Technology (platform, OS, mobile usage)
– Social media (websites, usage, activities)
Company Source
Household assets and allocations data
Surveys cover ~40% of US household assets
Zip+4 / age level granularity
Public records data
~115 MM households
Individual-level
Customer demographic and lifestyle data
Individual-level
Auto, property asset value and ownership
data
Individual-level
Payment history and credit accounts
Individual-level
Retail transaction data
~110 MM households
Individual-level
Predictions based off web tracking
technologies cross-referenced with
demographic and lifestyle data
Nearly all US Households
$1T+ offline transaction data
$ecure – Synthetic US Population/Household
19. PwC
Combining “large and incomplete” data with “small and detailed” data at a
household level enables us to understand complete consumer balance sheets
19
+ =
Client database
• Millions of records
• Hundreds of fields (mostly
transactional & product-
specific)
• Tens of useful fields
Household Level Surveys
• Thousands of records
• Thousands of fields (e.g. full
household balance sheet,
behavioral / attitudinal
variables, income and
expenses)
• Hundreds of useful fields
“Large and Incomplete” – Many
records, few fields (e.g. client data)
“Small and Detailed” – Few
records, many fields (e.g. SBI
Macromonitor, Census micro
sample, Consumer Expenditure
Survey)
Matched Dataset
• Millions of records
• Representative of US Population
or Client Customer Base
• Thousands of useful fields
• Accurate distributions within
households
Synthetic Household
Population
ExampleFields
Client account balances
& product details
Basic demographic
information
Rich transactional data
Detailed demographic
information
Complete household
balance sheet
Rich behavioral &
attitudinal data
Full household dataset
with realistic
distributions both
across and within
households
$ecureTM – Synthetic US Population/Household
20. PwC
…to create a synthetic US population and their HHBS and IE
statement
20
Environmental
Factors
Economics
Factors
Consumer Financial Behavior
Synthetic US Population
$ecureTM – Synthetic US Population/Household
21. PwC
Behavioral Simulation
Simulation of how individuals
really make decisions and
their emergent group
behaviors based on modeling
individual behaviors as
‘agents’. Choice made by
individuals get reflected as
‘market-level’ emergent
behaviors that are calibrated
with actual and survey data
$ecureTM uses behavioral simulation that combines agent-based modeling and
behavioral economics to model individual decision-making and emergent
behaviors
Artificial Intelligence
Cognitive thought through
machines
Complex Systems
Emergent system
behavior from individual
actions
Computational Power
Rapid cycle-time
for intensive calculations
Agent Based Modeling
Sophisticated, computationally
intensive modeling technique
that relies upon a decentralized
set of behavioral rules and
studies emergent behaviors
Classical Economics
Individual decision-making
driven by self-interest and
utility maximization
Psychology
Scientific study of mental
functions and behaviors of
individuals and groups
Behavioral Economics
Study of individual decision-
making based on cognitive,
heuristic, emotional and social
factors
+
+
+
+
=
=
=
21
$ecureTM - Behavioral Economics & Simulation
22. PwC
Interactions between the model and the real-world allows us
validate and infer individual behaviors and emergent properties
22
Agent-based modeling simulates
agents’ (e.g., individuals and
companies) interactions with
their environment and other
agents in order to understand the
emergent behavior of complex
systems.
Problem definition
Data
collection
Monitor results
Define pilot
Implement pilot
Simulate
Validate model
Real world
outcomes
Simulate
Design model
$ecureTM - Behavioral Economics & Simulation
Each agent encodes the
behavioral economic principles
(e.g., defaults, risk aversion etc)
based on their own personal
characteristics to act
23. PwC
Behavioral economics, behavioral simulations and interventions
are used to validate and infer individual and household behaviors
23
$ecureTM - Behavioral Economics & Simulation
24. PwC
By focusing on individual behaviors, the $ecureTM is able to drive
insights around how consumer needs change across the life cycle
24
Policyholder Dormant
Need Cash
Use disposable
income
Partial VA
withdrawal
Consideration of
withdrawal
Cash need covered
Event
(i.e., health issue)
Full VA
withdrawal
Account
withdrawal
hierarchy
Cash need
Unfulfilled
Other accounts
(CD, mutual funds,
401k)
Cash need fulfilled
1
2
3
4
1
2
4
5
6
While he is retired and his fixed income covers his expenses, he will
remain dormant with no financial concerns.
When his wife gets sick, he will calculate how much money he will
need to cover her medical bills.
5
While he is looking for a job to cover her medical bills, he will
calculate how long they can live off of their current income sources.
If he does not believe his sources of income will cover his
expense during the time he is job searching, he will begin to
worry and consider withdrawing cash from his investments.
If he decides to withdraw, he will follow a “withdrawal
hierarchy,” tapping into one account at a time until he has
fulfilled his cash need.
3
Once his cash need is fulfilled, he will return to the dormant
state.6
$ecureTM - Behavioral Economics & Simulation
25. PwC 25
Dependents Single & ‘Rich’ Growing Family Pre-Retiree Retiree New Generation
Liability Creation
Asset Transfer
Asset Creation Asset Creation
Asset Protection
Asset Preservation
Asset Depletion
PolicyholderLife-CycleStagesLifeEventsAdvice
Asset Cycle
• Paying off student loans
• Starting a career
• Getting married
• Buying a home
• Having or adopting children
• Paying tuition bills
• Caring for parents
• Planning for retirement
• Withdrawal money for retirement
• Paying for health care
• Creating a legacy
Understanding life events and choices
Life events change the individual’s understanding of themselves and their relationship to others
and to the environment.
$ecureTM - Cradle-to-Grave Simulations
27. PwC 27
$ecureTM - Scenario Based Planning
Comparison with ‘someone like you’ and ‘what if’ analysis allows
individuals and advisors to navigate the uncertainties of the future
Cradle-to-
grave planning
Individual
scenarios
28. PwC
$ecureTM combines power of data, advanced analytics or AI and
behavioral economics principles to generate actionable insights
28
$ecureTM Summary
APPLICATIONS
DATA MODEL
Product Features
Macro-Economic
Life Events
Healthcare Costs
HH Demographic
HH Financials
ANALYTICS
Behavioral
Simulation
Once upon a
time Once
upon a time
Once upon
Synthetic
Population
Household
Fundedness
Scenario
Building
What
if?
INSIGHTS
Household
Simulations
Market Insights
Product Insights
+
Opportunity
Sizing
Analytics
Segment-
ation
Analytics
Risk &
Profit-ability
Analytics
Channel
Analytics
Customer
Service
Analytics
Retention
Analytics
Consumer
Behavior
Analytics
Conceptual Architecture of $ecureTM
30. PwC
Data Types:
Participant Education
Provide tools that enable
plan health monitoring
for sponsors to improve
participant outcomes and
helps sponsors fulfill their
fiduciary obligations.
Assist advisors in offering
relevant, targeted plan
menus that feature
products and features
customized against plan
participant profiles.
Facilitate curation and active
management of the
retirement shelf to ensure
continued relevance to
customers.
Help retirement plan
participants benchmark
contribution and
allocation choices to
improve retirement
readiness
Plan Health Monitoring Targeted Plan Design Active Shelf Monitoring
30
XYZ Platform
PwC’s $ecureTM Platform
Simulating better investment strategies with data and analytics
Analytical Techniques:
PwC’s $ecure TM Platform
Analytical Techniques: Data Enrichment Cradle To Grave Household Projections Behavioral Simulation
Data Types:
Granular Household
Level Time Series …
Balance Sheet
Assets, Liabilities,
Net Worth, etc.
Income Statement
Income, Fixed And
Discretionary Expenses
Life Events
Births, Deaths, Health
Events, etc.
External Shocks
Macroeconomic,
Unemployment, etc.
31. PwC
THE BOTTOM LINE
THE IMPACT OF ANALYTICS
Lacking guidance to make prudent retirement decisions, retirement plan participants tend to demonstrate
sub-optimal savings behavior. Such behavior has contributed to the United States’ ballooning retirement
savings deficit.
Leveraging $ecure, retirement services providers can educate and guide participants on how much they
should save, given their personal situation. Sophisticated analytics provides future retirees with
actionable information on how households should save to maintain their standard of living.
Enhanced retirement education can result in improved plan participation and higher contributions.
Implementing such programs can significantly improve the depth of providers’ relationships with their
plan participants.
THE CHALLENGE TODAY
Case Study is Illustrative 31
PwC’s $ecureTM Platform – Retirement Plan Participant Education Module
How can I assist my client or retirement plan participants identify
strategies that may foster better outcomes?
A retirement services provider
would like to show participants
how households similar to
them are saving for
retirement.
401K Via $ecure, participants
are shown how their
retirement savings
compare against savings
in other similar
households.
401k
Doing so may spur participant
action, positively impacting
participation and
contribution levels without
explicitly offering advice.
$ $
$
$ $
32. PwC
THE BOTTOM LINE
THE IMPACT OF ANALYTICS
Fiduciary expectations of sponsors are becoming more exacting over time. However, developing tactical
programs that take a holistic view and actively monitor participant retirement readiness continues to be a
challenge.
With LARI's advanced analytic capabilities, retirement service providers can help sponsors benchmark
the retirement readiness of participant households against that of peer households to assess plan health
and facilitate interventions for vulnerable participants.
Regulators are taking a closer look at the steps taken by providers and sponsors to improve participant
retirement wellness. Active plan health monitoring can help providers to help their sponsors meet
regulatory expectations.
THE CHALLENGE TODAY
Case Study is Illustrative 32
PwC’s $ecureTM Platform – Plan Health Monitoring Module
Can I support my retirement plan sponsors by offering active plan
health monitoring services?
A retirement services provider
wants plan sponsors in its
network to be able to monitor
and improve plan health
for participants.
Using $ecure, PwC helps the
provider create and deliver to
its plan sponsors reports that
identify plan
participants in danger of
retirement readiness
downgrades.
Using these reports, plan
sponsors are able to
facilitate interventions or
share educational
materials to vulnerable
participants.
33. PwC
THE BOTTOM LINE
THE IMPACT OF ANALYTICS
Retirement service providers’ intermediaries often populate plan menus with options that do not align
with participants’ unique needs. This may result in participants making sub-optimal savings and
allocation decisions.
Drawing useful insights from $ecure’s simulation analysis, retirement service providers can guide their
intermediaries to offer tailored plan menus, featuring defaults that address the specific needs of each
participant household.
By helping participants make allocations that are well-aligned with their personal situations, $ecure in
turn helps intermediaries grow and retain their business, and ultimately makes the provider more
attractive to its intermediaries.
THE CHALLENGE TODAY
Case Study is Illustrative 33
A retirement service
provider wants to help its
sales intermediaries
identify plan menu
choices that closely
match the needs of
target participants.
Using Secure’s simulation
capabilities, plan menu
options are tested
against the retirement
needs and preferences of
target participants. Providers can help
intermediaries improve the
participant and sponsor
experience by demonstrating
how each plan is designed to
improve retirement readiness for
their specific pool of participants.
PwC’s $ecureTM Platform – Targeted Plan Design Module
How can I empower my intermediaries to offer tailored plan menus
tailored for participants?
34. PwC
THE BOTTOM LINE
THE IMPACT OF ANALYTICS
Many retirement service providers are seeking to enhance consumer choices via “open architecture”
strategies. However, if they do not actively curate product and service choices, they may encounter
disengagement over time.
Using $ecureI’s simulation engine to project the household financial situations of a base of plan
participants over time, retirement service providers can work their way back to identify the most relevant
set of products and services.
By actively managing the mix of products and services on the “retirement shelf,” providers are positioned
to protect their revenue and market share via stickier relationships with participants, plan sponsors, and
intermediaries.
THE CHALLENGE TODAY
Case Study is Illustrative 34
A retirement service
provider wants to make
sure that the products
and services on its
retirement shelf
continue to resonate
with its customers
Using $ecure’s behavioral
simulation capabilities, PwC
helps the client identify
products and services that
will meet the evolving
needs of customers
Periodic action based on the
review of $ecure insights helps
facilitates how products and
service offerings continue
to improve retirement
readiness as participant needs
and preferences evolve
PwC’s $ecureTM Platform – Active Shelf Monitoring Module
How do I ensure that my “retirement shelf” of products and services
stays aligned with my participants’ evolving needs?
44. PwC
Underfunded Population Number of Households (%)
Life Stage Wealth Scenario 1 Scenario 2 Scenario 3 % Change (S3-S1) Sparkline Trend
All All 66.8% 79.2% 79.4% 19%
Marginal 18.9% 19.6% 21.0% 11%
Mass Market 4.8% 5.0% 4.2% -12%
Affluent 1.4% 1.2% 0.8% -42%
Wealthy 0.1% 0.1% 0.1% 40%
Marginal 4.5% 4.5% 4.7% 5%
Mass Market 5.2% 5.4% 5.5% 6%
Affluent 0.4% 1.0% 1.2% 246%
Wealthy 0.1% 0.1% 0.1% 33%
Marginal 10.1% 10.8% 11.0% 9%
Mass Market 8.8% 12.8% 12.5% 42%
Affluent 0.4% 1.7% 1.6% 340%
Wealthy 0.0% 0.1% 0.2% 34%
Marginal 6.9% 8.6% 8.7% 26%
Mass Market 5.0% 7.3% 6.9% 39%
Affluent 0.3% 0.8% 0.8% 166%
Wealthy 0.0% 0.1% 0.1% -20%
Starters
Builders
Preretired
Retired
** Percentages add up to UF Totals across all segments.
We can derive insights from these outputs by studying
patterns across the segments and scenarios
Supplemental RIM Insights
44
Here we see the population of Underfunded segments across the 3 scenarios.
45. PwC
Underfunded Population Number of Households (%)
Life Stage Wealth Scenario 1 Scenario 2 Scenario 3 % Change (S3-S1) Sparkline Trend
All All 66.8% 79.2% 79.4% 19%
Marginal 18.9% 19.6% 21.0% 11%
Mass Market 4.8% 5.0% 4.2% -12%
Affluent 1.4% 1.2% 0.8% -42%
Wealthy 0.1% 0.1% 0.1% 40%
Marginal 4.5% 4.5% 4.7% 5%
Mass Market 5.2% 5.4% 5.5% 6%
Affluent 0.4% 1.0% 1.2% 246%
Wealthy 0.1% 0.1% 0.1% 33%
Marginal 10.1% 10.8% 11.0% 9%
Mass Market 8.8% 12.8% 12.5% 42%
Affluent 0.4% 1.7% 1.6% 340%
Wealthy 0.0% 0.1% 0.2% 34%
Marginal 6.9% 8.6% 8.7% 26%
Mass Market 5.0% 7.3% 6.9% 39%
Affluent 0.3% 0.8% 0.8% 166%
Wealthy 0.0% 0.1% 0.1% -20%
Starters
Builders
Preretired
Retired
** Percentages add up to UF Totals across all segments.
We can derive insights from these outputs by studying
patterns across the segments and scenarios
Supplemental RIM Insights
45
Here we see the population of Underfunded segments across the 3 scenarios.
Insight
Wealthy segments
generally avoid
underfundedness
Insight
Wealthy segments
generally avoid
underfundedness
Insight
The scenarios don’t
impact the Affluent
when they are
Starters… but DO
when they are
Builders
46. PwC
Diving deeper, we can uncover more insights, such as
changes to net worth of underfunded PreRetired segments
Supplemental RIM Insights
46
Marginal
(Underfunded)
Mass Market
(Underfunded)
Affluent
(Underfunded)
$146K $592K $1,655KScenario 1
* Wealthy segments not present in Underfunded category.
$46K $401K $972KScenario 2
-$91K $250K $714KScenario 3
While Scenario 3 (rising costs) did not significantly raise the share of
underfunded households, it greatly impacted average net worth
-$237K (S1-S3) -$342K (S1-S3) -$941K (S1-S3)
