Think different, in Finance. An outsider's two cents on how could finance majors rethink their role and value in the rapidly changing AI era, with some FinTech case studies.

1. Think Different—in Finance
FinxChance @ NCU
Albert Y. C. Chen, Ph.D.
Chief Scientist
Viscovery

2. Think Different
• Good business model NEVER last forever.
• Average “shelf life” on S&P 500: 20 years.
• 100-year old companies constantly reinvent
themselves every 10-20 years
• Startups contribute to 20% of USA’s GDP.

3. The Death of a Good
Business Model
• Foxconn 20 year revenue v.s. net profit (now at 5%)

4. What do 100 year old
corporations do?
GE Schenectady, 1896

5. History of change at GE
• 1886: one of the 12 original companies on the Dow
Jone Industrial Average (also the only one remaining).
• 1889: lightbulbs
• 1919: radios
• 1927: TV
• 1941: jet engine
• 1960: nuclear power
• 1971: room AC units
• 1995: MRI

6. History of change at IBM
• 1960s: mainframe computer
• 1980s: personal computer
• 2000s: integrated solutions
• 2020s: AI, Watson

7. How about the leading
Semiconductor companies?

8. NVidia reinventing itself
—2 times in 20 years

9. “Bad money drives out good”
in the desktop GPU market

10. The rise of mobile computing,
and how NVidia missed the boat!

11. NVidia’s Tegra mobile
processors never took off
then, the market
saturated…

12. NVidia not just survived.
NVidia is thriving!

13. Meet the new NVidia: Deep Learning,
Deep Learning, and still, Deep Learning

15. The king is dead,
long live the king!

16. Now, again, do we want to
do OEM/ODM forever?
Optimizing an old business model
is just delaying its eventual death.

17. Why Startups?
Business
Relevance
Academic
Relevance
plentiful resources; bureaucratic organization
lack of resources; responsive organization
traditional corporations
talking “innovation”
corporate research
startups struggling to survive
academic spinoffs
MSR

19. Startups
A company, partnership, or temporary organization
designed to search for a new, repeatable and
scalable business model.

20. Your Idea
• Are you passionate about it?
• Is it disruptive enough?
• What is your business plan?
• What is it?
• Can it make money?
• What is the future of the idea?
• What is your competitive advantage?
• How do you build up your entry barrier?

21. Visual Search, Simply Smarter

22. Once in a lifetime opportunity in
China’s video streaming market

23. What do we need?
Face Motion
Image
scene Text Audio Object
Semantics

24. Viscovery VDS (Video Discovery Service)

25. Viscovery VDS (Video Discovery Service)

26. Viscovery VDS (Video Discovery Service)

27. Challenges Encountered
Along the Way
• From Product Recognition in Images, to Face,
Logo, Object, Scene recognition in Videos.
• Number of Categories
• Recognition Accuracy
• Recognition Speed
• System Architecture
• Business Model

28. Viscovery’s Edge
• Market: first mover’s advantage in China’s video
streaming market.
• Speed: we built the whole VDS thing in a few months!
• Team:
• Technology:
• Depth
• Breadth
• Cloud
• Customizability
• Self-Learning

29. Disruptive Innovation
for Finance majors

30. Catering to banking
customers after 3:30pm
• 362 days a year, open until 8pm on weekdays.
• Added nearly 1-million customers in just 6 years.
• Water bowls for dogs, free coin counting and
sorting machines.

31. Providing Cheaper
International Wire Transfers
• Targeting Western Union and MoneyGrams.
Bypassing the middlemen in remittances.
• Targeted at places where banks are not easily
accessible.
• Remit money with bank deposit, cash pickup, e-
wallets, mobile phone credit, home delivery.

32. p2p Currency Exchange

33. p2p Lending

34. Credit Scores, for those
without credit history

35. Peer-to-business Lending

36. Data Analytics for Individual
Investment Records

37. Wealth Management for the
Masses
• Private banking starting with as little as £1000.
• Transactions free of charge.

38. The 21st century pawn shop
12% APR55.8% APR 18.8% APR

39. Where am I getting the
money from?

40. 200M funding in 8 rounds,
500M valuation.
46M funding in 5 rounds
116.37M in 6 rounds,
1 billion valuation
68.63M funding in 7 rounds
273.24M in 6 rounds,
75M in 3 rounds
177.47M in 6 rounds

41. More Demanding != More Likely to Fail

42. What role should a
finance major play in a
startup?

43. A minimal startup team
• A hacker
• A hustler
• A hipster

45. Startup Timeline

46. Prototype
• Hack out a prototype
• Spend 2-10 weeks max.
• Investors are much more likely to fund you if
you have a minimal initial version of your idea.
• Hackathons are a good place to start.
• Iteratively improve the prototype

47. Money!

49. Buildup your entry barrier!
• Market (users)
• Speed
• Team
• Technology

50. How much should a finance
major learn about technology?
• Programming Skills?
• Machine Learning Skills?
• Data Science?

51. Programming Skills
• Look ma! My first website!!

52. Programming Skills
• Hard-core Computer Science

53. Programming Skills
• Scripting language for actually doing stuff

55. Programming Skills
• Learn as you go.
• Learn enough to get the job done, then go!!
• Focus not on the language, but on the problem
you have at hand.

56. Machine Learning
Classification Clustering
Regression
Dimension
Reduction
supervised unsupervised
continuousdiscrete

57. Data Science
• The difference between:
• Data Engineer
• Data Analyst
• Data Scientist
• Does running some regressions and fitting some
models justify the title of a “data scientist”?

60. Spurious Correlations

61. Spurious Correlations

62. Spurious Correlations

63. Deep Learning

64. Deep Learning

65. Deep Learning

66. Life is not all rosy at startups
• High Risk, High Pressure, High Uncertainty!
• Resources are scarce, but you MUST DELIVER!
• Forming your all-star team is not that easy…
• Focus, and persistence.

67. Thank You!
albert@viscovery.com