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Re-inventing Digital Advice ($ecure)

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Re-inventing Digital Advice ($ecure)

  1. 1. Robo-Advice and Beyond Behavioral Simulation Applied to Enhancing Decision Making December 2015
  2. 2. PwC Agenda 2 • Our Perspective • Background on Robo-Advice • Overview of $ecureTM • Value Proposition For Stakeholders • Appendices
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 23. PwC Behavioral economics, behavioral simulations and interventions are used to validate and infer individual and household behaviors 23 $ecureTM - Behavioral Economics & Simulation
  24. 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. 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
  26. 26. PwC Synthetic Policyholder Population Projected Product Attributes Projected Policyholder Attributes Competitive Factors Economic Factors Policyholder Factors Projected Savings Behavior Parameters (For ‘what-if’ analysis) Model ‘Agents’ Scenario Outputs Simulation Model Withdrawal Medical Policyholder Behaviors Social Security Savings Products Economic Environment Advisors & Company Policyholders External Data Views & Calibration Projected Withdrawal Behavior Scenario Combination 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
  27. 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. 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
  29. 29. PwC Appendix 1 $ecureTM use cases
  30. 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. 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. 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. 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. 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?
  35. 35. PwC Appendix 2 Retirement Income ModelSM (RIM) Screenshots and Sample Outputs
  36. 36. PwC Retirement Heat Map View Appendix – RIM Screenshots 36
  37. 37. PwC Household / Individual Micro-View Appendix – RIM Screenshots 37
  38. 38. PwC Customer Demographic Dashboard Appendix – RIM Screenshots 38
  39. 39. PwC Annuity Behavior Dashboard Appendix – RIM Screenshots 39
  40. 40. PwC Economic Environment View Appendix – RIM Screenshots 40
  41. 41. PwC Economic Control Panel Appendix – RIM Screenshots 41
  42. 42. PwC Consumer Finance Control Panel Appendix – RIM Screenshots 42
  43. 43. PwC Appendix 3 Supplemental RIM Insights
  44. 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. 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. 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)
  47. 47. 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 or completeness of the information contained in this publication, and, to the extent permitted by law, PwC, its members, employees and agents do not accept or 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 this publication or for any decision based on it. © 2015 PricewaterhouseCoopers LLP. All rights reserved. PwC refers to the United States member firm, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details. Anand Rao PricewaterhouseCoopers LLP (www.pwc.com) 125 High Street Boston, MA 02110 +1 617 530 4691 (o) | +1 617 633 8354 (m) anand.s.rao@us.pwc.com Juneen Belknap PricewaterhouseCoopers LLP (www.pwc.com) CNL Tower, 420 South Orange Avenue, Suite 200 Orlando, FL 32801 +1 407 236 5102 (o) | +1 617 312 9463 (m) juneen.belknap@us.pwc.com Pallav Ray PricewaterhouseCoopers LLP (www.pwc.com) 2001 Ross Avenue, Suite 1800 Dallas, TX 75201 +1 214 754 4839 (o) | +1 202 230 1869 (m) pallav.ray@us.pwc.com Spencer Allee PricewaterhouseCoopers LLP (www.pwc.com) One North Wacker Chicago, IL 60611 +1 847 4776 2430 (m) spencer.allee@us.pwc.com