Re-inventing Digital Advice ($ecure)

Robo-Advice and Beyond

Behavioral Simulation Applied to Enhancing Decision Making

December 2015

PwC

Agenda

2

• Our Perspective

• Background on Robo-Advice

• Overview of $ecureTM

• Value Proposition For Stakeholders

• Appendices

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

FinancialPlanners

ILLUSTRATIVE

3

Competitive Landscape

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

To

da

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me

ga

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… • 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% arealmost 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

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

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

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

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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)

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)

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

03Demographics / 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)

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

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

PwC

More advanced cognitive robo-advisors that can fully exploit the emerging advances in AI technology address these needs

12


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

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


Evolution of Robo-Advisors

Standalone

Robo-advisors

Integrated Robo-

advisors

Cognitive Robo-

advisors

Time

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


Working Age (Ages 20–49) Retirement (Ages 65+)

Mass M

ark

et

Pre-retirement (Ages 50-64)

HN

W a

nd U

HN

W

= Advice Need

Assessment

= Product

Advice

= Portfolio

AllocationService ProvidedCatered Towards

A - Advisors

B - Both

C - Clients

Accumulation Decumulation

A B

C

C A

A

B

C

BB

B C

CCC

C

C

B

C

B

B

B

B A

A A

B

A

C C

C

C

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

BehavioralEconomics & Simulation

Holistic Household View

1

3

4

5

$ecure

$ecure - Overview

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

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


Data

Third Party Data


Data


Data

Public Data & Social Media

Data


Data


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

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

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

Ex

am

ple

Fie

lds

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


PwC

…to create a synthetic US population and their HHBS and IE statement

20

Environmental Factors

Economics Factors

Consumer Financial Behavior

Synthetic US Population


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 IntelligenceCognitive thought throughmachines

Complex SystemsEmergent system behavior from individual actions

Computational PowerRapid cycle-timefor intensive calculations

Agent Based Modeling

Sophisticated, computationally intensive modeling technique that relies upon a decentralized set of behavioral rules and studies emergent behaviors

Classical EconomicsIndividual decision-making driven by self-interest and utility maximization

PsychologyScientific study of mental functions and behaviors ofindividuals and groups

Behavioral Economics

Study of individual decision-making based on cognitive, heuristic, emotional and social factors

+

+

+

+

=

=

=

21

$ecureTM - Behavioral Economics & Simulation

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


Each agent encodes the behavioral economic principles (e.g., defaults, risk aversion etc) based on their own personal characteristics to act

PwC

Behavioral economics, behavioral simulations and interventions are used to validate and infer individual and household behaviors

23


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


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

Po

licyh

old

er

Life

-Cycle

Sta

ge

sL

ife

Eve

nts

Ad

vic

e

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

PwC

Synthetic Policyholder Population

Projected Product

Attributes

Projected Policyholder

Attributes

Competitive Factors

Economic Factors

Policyholder Factors

ProjectedSavings

Behavior

Parameters(For ‘what-if’ analysis)

Model ‘Agents’

Scenario Outputs

Simulation Model

Withdrawal

Medical

PolicyholderBehaviors

Social Security

Savings

Products

Economic Environment

Advisors&

Company

Policyholders

External Data

Views & Calibration

ProjectedWithdrawal

Behavior

ScenarioCombination

Scenario Inputs

26

Assumptions&

Scenarios

$ecureTM - Scenario Based Planning

The model includes a range of components that simulate a variety of scenarios – economic, market, individual, household – over the lifetime of individuals

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

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-ationAnalytics

Risk & Profit-ability Analytics

Channel Analytics

Customer Service Analytics

Retention Analytics

Consumer Behavior Analytics

Conceptual Architecture of $ecureTM

PwC

Appendix 1

$ecureTM use cases

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.

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.

$ $$

$ $

PwC

THE BOTTOM LINE


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


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.

PwC

THE BOTTOM LINE


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


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?

PwC

THE BOTTOM LINE


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


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?

PwC

Appendix 2

Retirement Income ModelSM (RIM) Screenshots and Sample Outputs

PwC

Retirement Heat Map View

Appendix – RIM Screenshots

36

PwC

Household / Individual Micro-View


37

PwC

Customer Demographic Dashboard


38

PwC

Annuity Behavior Dashboard


39

PwC

Economic Environment View


40

PwC

Economic Control Panel


41

PwC

Consumer Finance Control Panel


42

PwC

Appendix 3

Supplemental RIM Insights

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


44

Here we see the population of Underfunded segments across the 3 scenarios.

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


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

PwC

Diving deeper, we can uncover more insights, such as changes to net worth of underfunded PreRetired segments


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)

PwC

Advisory

Team

Contacts

This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the

information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the accuracy

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assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in

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© 2015 PricewaterhouseCoopers LLP. All rights reserved. PwC refers to the United States member firm, and may sometimes refer to the PwC network. Each member

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Anand RaoPricewaterhouseCoopers LLP (www.pwc.com)125 High StreetBoston, MA 02110+1 617 530 4691 (o) | +1 617 633 8354 (m)[email protected]

Juneen BelknapPricewaterhouseCoopers LLP (www.pwc.com)CNL Tower, 420 South Orange Avenue, Suite 200Orlando, FL 32801+1 407 236 5102 (o) | +1 617 312 9463 (m)[email protected]

Pallav RayPricewaterhouseCoopers LLP (www.pwc.com)2001 Ross Avenue, Suite 1800Dallas, TX 75201+1 214 754 4839 (o) | +1 202 230 1869 (m)[email protected]

Spencer AlleePricewaterhouseCoopers LLP (www.pwc.com)One North WackerChicago, IL 60611+1 847 4776 2430 (m)[email protected]

mailto:[email protected]




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