The document outlines the agenda for a meetup focused on data science, design, and technology, highlighting discussions on AI's impact on finance. It includes survey results addressing participant expectations for future meetups, emphasizing a desire for more networking and time for Q&A. Additionally, it provides insights on the transformative value of AI in financial markets, personalized client experiences, and operational efficiency.

1. Data Science | Design | Technology
May 22
2019
https://www.meetup.com/DSDTMTL
1

2. Agenda
6:15 - 6:45 Arrival & Networking
6:45 - 7:15 News & Survey Results
7:15 - 8:00 AI is dramatically transforming the
world of finance
8:00 - 8:30: Wrap-up, Snack & Networking
2
AI is Changing the World

3. 3
A special thanks to our contributors…
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Thanks
Merci
&
venue
sponsor
& snacks
brains

4. 4
AI is Changing the World
JL Maréchaux
“DSDT survey results &
lessons learned"
Nick Vandewiele
“AI is dramatically transforming the
world of finance.”

5. 5https://careers.smartrecruiters.com/IVADOLabs
• Data Scientists - ML
• Data Engineers / Architect
• Software Engineers / Full Stack
Developers
… and more…
is hiring
www.ivadolabs.com

6. DSDT Survey Results
&
Lessons Learned
Data Science | Design | Technology 6
http://bit.ly/DSDTsurvey042019

7. Our rating on DSDT meetups
7

8. Who we are - Background
8

9. Who we are - Current role
9
7.1 %
Product Owner & BA
Software Developer
Data Engineer
Data Scientist

10. What we are expecting from meetups - Format
10

11. 11
"Less time for the main
presentation, and more time
for directed moderated Q+A"
"Overall - all good!"
"Cutting a little bit the speakers to
allow more networking would be
great [...]"
"More time for networking, but don't
cut in the content".
"Keep up the excellent work - ..
think of ways to further include the
community in the event planning"

12. What we are expecting from meetups - Content
12
Data visualization
Emerging technologies for big data
and digital economy
Technologies & tools for AI
Examples of AI in real business use-cases
User Experience for AI
Operation Research for decision-making systems
ML / DL patterns & algorithms

13. DSDT Survey: http://bit.ly/DSDTsurvey042019
LinkedIn group: https://www.linkedin.com/groups/12156799/
Facebook page: https://www.facebook.com/dsdtmtl/
Twitter: @dsdtmtl (https://twitter.com/DsdtMtl)
13
Call to action - share more with us and get involved

14. AI is dramatically
transforming the
world of finance
Data Science | Design | Technology 14
Nick Vandewiele

15. Merci / Thank You
@DsdtMtl
Data Science | Design | Technology
(Check for next DSDT meetup at https://www.meetup.com/DSDTMTL)
http://bit.ly/dsdtmtl-in

16. AI in Finance
Nick Vandewiele
Credit: Data Driven Investor
DSDT MeetupMay 22 2019

17. The Financial Landscape
2

18. Overview
The Value of AI in Finance
AI in Capital Markets
AI in Credit
Challenges
3

19. Finance is Transforming
4Credit: JP Daunais

20. The Value of AI
5

21. Better Client Experience
Personalized Recommendations Dialogue Engines
6
Credit: Netflix Credit: DigitalConnectMag

22. Increase Operational Efficiency
7

23. The Value of AI in Finance
AI in Capital Markets
AI in Credit
Challenges
8

24. The best investors are right
only 53% of the time.
Perry Boyle, Head of Equities @ Point72
9

25. 10

26. 11
Image Credit: Zerohedge

27. 12

28. 13

29. 14

30. High dimensional input features
t1
t2
t3
t4
t5
t6
t7
t…
P1
x1 x2 x3 x4 x5 x6 x7 x
…
MODELINPUT DATA X TARGET
ෝ𝒚
15

31. Innovation when everything is public?
16Credit: trump2cash.biz

32. Innovation through proprietary datasets.
17Credit: Tachos Group

33. Innovation through
public, alternative datasets.
18
Credit: Koto.ai

34. The Value of AI in Finance
AI in Capital Markets
AI in Credit
Challenges
19

35. The Business of Credit Cards
20

36. Making money by not losing money.
21
AI leveraging
In-house Historical
Credit Bureau
Transactional
Life Events
Building a model of
the financial behavior of our clients.
Identifying good vs. bad clients.

37. But what about new clients?
22
External, proprietary data?
Alternative but public data?

38. The Value of AI in Finance
AI in Capital Markets
AI in Credit
Challenges
23

39. Legacy
24

40. AI Hierarchy of Needs
25
Monica Rogati, Hacker Noon, 2017

41. AI Lifecycle Management
26D. Scully, Hidden Technical Debt in Machine Learning Systems, Google

42. Explainability
27
David Walliams, Little Britain, 2000

43. Discrimination, Regulation
28

44. Experimentation and Ethics
29

45. Conclusions and Perspectives
30

46. Questions?
31