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Retirement Isn't Linear: Mapping the Future with Big Data & Big Data Analytics

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Talk presented at the Retirement Income Industry Association Annual conference, held in Indianapolis on September 18, 2015. The talk presents an overview of demographic, medical, and technological advances. Big Data analytics and its role in robo-advice is profiled with the three generations of robo-advice.

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Retirement Isn't Linear: Mapping the Future with Big Data & Big Data Analytics

  1. 1. RETIREMENT ISN’T LINEAR: MAPPING THE FUTURE WITH BIG DATA & BIG DATA ANALYTICS DR. ANAND RAO, PWC @AnandSRao
  2. 2. • Retirement isn’t Linear – Life Expectancy Increase is not linear – Medical advances are not linear – Technology acceleration is not linear – Artificial Intelligence progress is not linear • Hype and Reality of Robo-Advice • Future of Advice – Assisted Advice or Autonomous Advice – Art of the Possible • Key Takeaways 2 Executive Summary
  3. 3. Life Expectancy has almost doubled in 100 years…. 3Source: World Climate Report, March 17, 2011
  4. 4. …but exponential rise considering a longer time frame 4Source: Life Expectancy, Max Roser, in OurWorldInData.org, 2015
  5. 5. Has the increase in life expectancy plateaued? OR Will we see another doubling over the next 100 years? 5
  6. 6. 6 Medical advances are not linear… Source: "DNA Sequencing Costs“, National Human Genome Research Institute, available online – http://www.genome.gov/sequencingcosts/
  7. 7. 7 Revolution in personalized medicine…. Source: The new frontier of genome engineering with CRISPR-CAS9, Doudna and Charpentier, Science, Nov 2014
  8. 8. 8 Revolution in personalized medicine…. Source: The new frontier of genome engineering with CRISPR-CAS9, Doudna and Charpentier, Science, Nov 2014
  9. 9. 9 Revolution in personalized medicine…. Source: Organs-on-chips emulates human organs for better biomedical testing, The New Stack, 2015
  10. 10. 10 Life expectancy in the Future – Linear or non-linear …. Source: National Geographic, May 26, 2013 Source: Time, Feb 23, 2015
  11. 11. 11Source: "Average Annual Expenditures of All Consumer Units by Race, Hispanic Origin, and Age of Householder: 2009" - U.S. Census, PwC Analysis Household retirement income and expenses rise and fall in a nonlinear fashion…
  12. 12. 12Source: "Average Annual Expenditures of All Consumer Units by Race, Hispanic Origin, and Age of Householder" - U.S. Census (1999, 2009 Data), PwC Analysis Rising expenditure on healthcare… * Households headed by individuals 65 years or older
  13. 13. MORE OF US LIVING LONGER LIVING HEALTHIER & SPENDING MORE 13 Summary of Demographics …
  14. 14. 14Source: “The Singularity Is Near: When Humans Transcend Biology”, Ray Kurzweil (2006) Technology acceleration is also not linear
  15. 15. 15 Hype & Reality of Artificial Intelligence… 2004: CMU Red Team (DARPA Challenge) 2014: Google’s Autonomous Car
  16. 16. 16 Hype & Reality of Artificial Intelligence… 1984: Neural Nets - backpropagation 2014: Deep Learning Source: Facebook's DeepFace Software Can Match Faces With 97.25% Accuracy, Forbes, March 18, 2014.
  17. 17. 17 Hype & Reality of Artificial Intelligence… Source: The Current State of Machine Intelligence, Bloomberg, December 11, 2014
  18. 18. 18 Hype & Reality around Artificial Intelligence
  19. 19. 19 Hype & Reality around Artificial Intelligence
  20. 20. 20 Reality of Robo-advisors… Large valuations $500M of VC funding
  21. 21. 21 Reality of Robo-advisors… Advisor & Institutional $2 Trillion in e-advice by 2020
  22. 22. 22 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
  23. 23. Future of Advice – Deep Learning 23 Keywords at Intersection of Technology and Finance: • Artificial intelligence • Interactive marketing • Entrepreneurial • Digital marketing • Software vendors • Next generation Artificial intelligence and cutting edge technologies are becoming more central to the financial advisory service Financial Advice Concepts Next Generation Technology Tools and Concepts Keywords at Intersection of Technology and Finance Traditional Industry-Wide Technical Competencies
  24. 24. PwC Comparison of investment strategies and products: Retirement planners mostly focus on options for retirement savings plan or annuity products • Retirement savings plan • Annuity product options: e.g.: Guaranteed Minimum Withdrawal Benefit (GMWB) Future of Advice – Deep Learning 24 Fund managers opt for riskier financial products such as Exchange Traded Funds or other index funds • Exchange Traded Funds (ETFs) • Other index funds that follows broader market
  25. 25. Future of Advice – ‘SimCity for Advice’ 25
  26. 26. Future of Advice – ‘Data Enrichment and Synthetic Data sets’ 26 + = “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) 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 $175,000
  27. 27. Future of Advice – ‘Clients like you’ 27 • Auto-fills missing/incomplete data for “clients like you” • The ranges narrow as more data becomes available $175,000
  28. 28. Future of Advice – ‘Retirement Heatmap’ 28 • RIIA segments and fundedness • Future projection of HHBS
  29. 29. Future of Advice – Scenario Planning 29 Cradle-to-grave planning Individual scenarios
  30. 30. Financial Advisors • Focus on client relationship and advice – not on client administration and data gathering • Be the trusted advisor – not a product seller or product advisor • Extract insights from tool and personalize the advice • Machines are coming – ‘Don’t resist them – embrace them’ 30
  31. 31. Financial Service Providers • Focus on understanding customers and their latent needs using data and analytics • Recognize and embrace digital channels as part of an omni- channel approach to serve all segments cost-effectively • Start with the questions that need to be answered/decisions to be made – not with the ‘big data’ that you can get • Start small – test and learn – scale and deploy data and analytics solutions 31

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