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.

1. RETIREMENT ISN’T LINEAR: MAPPING
THE FUTURE WITH BIG DATA & BIG
DATA ANALYTICS
DR. ANAND RAO, PWC
@AnandSRao

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. Life Expectancy has almost doubled in 100 years….
3Source: World Climate Report, March 17, 2011

4. …but exponential rise considering a longer time frame
4Source: Life Expectancy, Max Roser, in OurWorldInData.org, 2015

5. Has the increase in life expectancy plateaued?
OR
Will we see another doubling over the next 100
years?
5

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
Revolution in personalized medicine….
Source: The new frontier of genome engineering with CRISPR-CAS9, Doudna and Charpentier, Science, Nov 2014

8. 8
Revolution in personalized medicine….
Source: The new frontier of genome engineering with CRISPR-CAS9, Doudna and Charpentier, Science, Nov 2014

9. 9
Revolution in personalized medicine….
Source: Organs-on-chips emulates human organs for better biomedical testing, The New Stack, 2015

10. 10
Life expectancy in the Future – Linear or non-linear ….
Source: National Geographic, May 26, 2013 Source: Time, Feb 23, 2015

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. 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. MORE OF US
LIVING LONGER
LIVING HEALTHIER
& SPENDING MORE
13
Summary of Demographics …

14. 14Source: “The Singularity Is Near: When Humans Transcend Biology”, Ray Kurzweil (2006)
Technology acceleration is also not linear

15. 15
Hype & Reality of Artificial Intelligence…
2004: CMU Red Team (DARPA Challenge) 2014: Google’s Autonomous Car

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
Hype & Reality of Artificial Intelligence…
Source: The Current State of Machine Intelligence, Bloomberg, December 11, 2014

18. 18
Hype & Reality around Artificial Intelligence

19. 19
Hype & Reality around Artificial Intelligence

20. 20
Reality of Robo-advisors… Large valuations
$500M of
VC
funding

21. 21
Reality of Robo-advisors… Advisor & Institutional
$2
Trillion in
e-advice
by 2020

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. 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. 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. Future of Advice – ‘SimCity for Advice’
25

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. 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. Future of Advice – ‘Retirement Heatmap’
28
• RIIA segments and fundedness
• Future projection of HHBS

29. Future of Advice – Scenario Planning
29
Cradle-to-grave
planning
Individual
scenarios

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. 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