"Our FinTech Future – AI’s Opportunities and Challenges?" is a presentation by Jim Kyung-Soo Liew, Ph.D. to the Artificial Intelligence Maryland (MD-AI) meetup (https://www.meetup.com/Maryland-AI/), November 20, 2019. Dr. Liew is Co-Founder of SoKat.co and Associate Professor at Johns Hopkins Carey Business School.

1. Our FinTech Future – AI’s
Opportunities and Challenges?
Artificial Intelligence Maryland (MD-AI)
November 20th, 2019
Betamore, 101 West Dickman St, Baltimore, MD 21230
6:30pm-7:30pm
Jim Kyung-Soo Liew, Ph.D.
Co-Founder of SoKat.co and
Associate Professor at Johns Hopkins Carey Business School

2. Contents
Professor Liew’s Top Six Predictions for 2020 on “FinTech Future:
AI’s Opportunities and Challenges”
(1) Regulators Back-off and Allow for “Mucho” FinTech
Innovations
(2) Blockchain Killer App Discovered - Surprise - It Saves Trees!
(3) How will AI invest? Low-to-high frequency, Warren “AI”
Buffet!
(4) Mobile Banking without the Bank!
(5) AI Singularity Over the Horizon? But when?
(6) Edge of Network Valuable Geo-Location Data
Case Studies
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(1)Regulators Back-off and Allow for
“Mucho” FinTech Innovations

4. China moves aggressively into AI &
Blockchain…US fast-follower, regulators
will be forced to keep at bay
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“China is really bullish on
blockchain, the technology that
verifies bitcoin transactions.”
-- Charlie Wood Oct 28, 2019, 7:04 AM (Business Insider)

5. Understanding AI/Machine Learning
11/22/2019
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
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6. AI’s Proliferation with “Git and GPUs”
11/22/2019
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
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(2) Blockchain Killer App Discovered -
Surprise - it saves trees!

8. Blockchain
hype
continues
into 2020,
will we finally
discover the
killer app?
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9. SEC issues “No-
Action” Letter
Watch Paxos for T+2 to T+0,
settlement success!
Blockchain replaces complex
“paper” processes, saves trees!
Watch for Frank Yiannas (the
“Mango-Man”) at FDA (prior
Walmart)
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https://www.investopedia.com/terms/c/clearinghouse.asp
https://www.coindesk.com/paxos-wins-sec-no-action-letter-to-settle-equities-on-a-blockchain

10. (3) How will AI invest?
Low-to-high frequency
Warren “AI” Buffett!
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11. 11/22/2019
https://www.visualcapitalist.com/what-is-quantamental-investing/
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https://markets.businessinsider.com/news/stocks/quant-traders-train-their-algorithms-invest-like-warren-buffett-2019-10-1028614698

13. AI Hedge Fund Performance
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AI Hedge Fund
12.5%
FOF Index
4.8%
Hedge Fund
6.1%

14. Biggest AI Threat to Investment
Management Industry
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15. FinTech spin-out, add-on, etc.
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16. Go Mobile! – New Wealth Managers
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https://www.personalcapital.com/financial-software/mobile-app

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(4) Mobile Banking without the Bank

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Mobile Banking Success!

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Mobile Banking Social Benefits

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• Largest FinTech in
Latin America
• Founded in 2013
• 1st transaction
Nubank card in April
2014
• 10 million customers
• Raising $400m

21. China’s Digital
Payment System
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https://www.brookings.edu/wp-content/uploads/2019/05/ES_20190617_Klein_ChinaPayments.pdf

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(5) AI Singularity Over the Horizon?
But when?

23. 11/22/2019
Artificial
Intelligence
Machine
Learning
and Deep
Learning
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24. AI History
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25. 11/22/2019
Andrew L. Beam
https://beamandrew.github.io/deeplearning/2017/02/23/deep_learning_101_part1.html
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26. 11/22/2019
https://www.coursera.org/learn/machine-learning
Highly recommend interested students to take Ng’s Machine Learning course on Coursera
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28. Machine Learning gives "computers the
ability to learn without being explicitly
programmed."
-Arthur Samuel (1901-1990) pioneer of Artificial Intelligence research
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29. Chess (Feb ‘96)
DeepBlue vs Kasparov
Machines Beating Humans in Games
Go (Mar ‘16)
AlphaGo vs Lee Sedol
Jeopardy (Feb ‘11)
Watson vs Ruffer vs Jennings
Poker (Jan ‘17)
Libratus vs. Jason Les
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30. AlphaGo Zero defeats AlphaGo 100-0!
11/22/2019
https://deepmind.com/blog/alphago-zero-learning-scratch/
• Based on first principles, only black/white stones as inputs
• No human data
• 3-days AlphaGo Zero surpasses AlphaGo level
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31. Quiz:
What is the Turing Test?
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32. Turing’s Test
11/22/2019
https://en.wikipedia.org/wiki/Turing_test
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33. Google’s Duplex Passes the “narrow” Turning Test
11/22/2019
May 2018 (+10 months)
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34. Fall out
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35. AI Singularity
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https://swisscognitive.ch/2018/01/06/ai-and-work-of-the-future/

36. AI
Artificial Intelligence (AI) is the ability of a machine to
think and learn better than humans.
11/22/2019
IntellectualLevel
Time
Humans
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37. AI
Artificial Intelligence (AI) is the ability of a machine to
think and learn better than humans.
11/22/2019
IntellectualLevel
Time
Humans
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38. AI Singularity
Point in time whereby AI surpasses human capabilities.
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IntellectualLevel
Time
Humans
1950s 2000s 2030-2040(?)
Machines
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(6) Edge of Network Valuable Geo-
Location Data

40. Pairs Trading Strategy with Geolocation Data
The Battle between Under Armour vs Nike?
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Jim Kyung-Soo Liew, Tamas Budavari, Zixiao Kang, Fengxu Li,
Xuzhi Wang, Shihao Ma and Brandon Fremin
Johns Hopkins University
July 22, 2019
Forthcoming - The Journal of Financial Data Science 2020

41. Contents
• Motivation
• Data
• Results
• Conclusions
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42. Motivation
• Cell phone location proxies demand
• Buying shoes, which store?
Under Armour (UA) or Nike (NKE)
• Linkages to prices
Q: Does geo-location activity predict spreads
between UA and NKE prices?
Answer: Yes! But with many caveats…
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43. Geo-location Data (43 billion, Jan-July ‘18)
www.Fysical.com
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44. Time to Shop!
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46. Time to Shop!
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47. 52 Paired locations of UA and NKE
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48. Example of Geo-location activity
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49. Examples of Data Files
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50. Daily Data – Correlation Matrix
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51. Geo-Location Ratio -- Top Feature
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52. 11/22/2019 Prof Jim Kyung-Soo Liew 52
Cross Validation Accuracy

53. Conclusions
• Geo-location statistically significant (daily)
• Bests usual suspects: prior prices, volume, tweets, etc.
• Consistently in top features across AI/ML algos
• Weakness lack of long history
• Promising results -- hints at possible daily over-
reaction behavior
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54. What is Big Data?
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55. Big Data Definitions:
Merriam-Webster:
An accumulation of data that is too large and
complex for processing by traditional database
management tools
Wikipedia:
Big data is a term for data sets that are so
large or complex that traditional software is
inadequate to deal with them
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56. Big Data’s 3 V’s
• Volume – How much data is created?
“The data volumes are exploding, more data has been created in the past
two years than in the entire previous history of the human race.” -Forbes
• Velocity – How fast (velocity) is data created?
http://www.internetlivestats.com/ (per second)
• Variety – What type of data is created?
i-Phone – voice, SMS, email, photos, videos, emoji
chats, geo-location lat/long, networks, etc.
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57. 11/22/2019
Exploratory
Data Analysis
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58. IQR over time for Fama-French India-Data
11/22/2019
https://rkohli3.github.io/india-famafrench/method.html
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59. Reality of Big Data
• Never perfect, and painful to clean up
• Allocate about 70-80% time/effort to munge,
never 100% done!
• Work with Subject Matter Expert (SME) -- great
results!
• Even with non-perfect data, typically discover
very insightful relationships
Best results are intuitive, obvious ex post
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60. Case Study 1: Buy, Why?
Logistic
SVM
Random Forest
Neural Network
Perceptron
k-Nearest Neighbor
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61. Linear vs Non-Linear Classifiers
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62. Linear vs Non-Linear Classifiers
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63. Linear vs Non-Linear Classifiers
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64. Linear vs Non-Linear Classifiers
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65. Linear vs Non-Linear Classifiers
11/22/2019
Linear Classifiers “Square” Classifiers Non-Linear Classifiers
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66. Thanks again and please stay in touch!
Email me at jim@sokat.co
www.SoKat.co
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67. Machine Learning Best Practices
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68. 11/22/2019
https://github.com/rasbt/stat479-machine-learning-fs18
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69. Moving to multiple Data Inputs to fuel AI
11/22/2019
* All Images from various Images on the internet
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