This is a talk given to bankers at CCX Forum where I share how Machine Learning products can be built for retail banking sector, what are the challenges and how can they be overcome.

1. Machine Learning for
Retail Banking
Rudradeb Mitra | Serial entrepreneur, Writer and Mentor (Google Launchpad)
http://www.linkedin.com/in/mitrar/
CCX Forum
18th May 2018, London, UK

2. make you all Machine Learning experts in 30 minutes!
My goal is to ..

3. Unlearn Machine Learning

4. Real power of Machine Learning is
in predicting human behavior

5. Relearn Machine Learning

6. "Communication and making people feel valued are
the most important for banking CX"
Drivers of customer experience in banking sector
But how to make someone valued? What to communicate?
Machines help us to know the answers!

7. I. Sceptic's ask Why Machine Learning:
Arguing with a puzzle

8. Puzzle I
Imagine you bought a wine for $20. It now sells for $75. If you
decide to drink the wine, what cost will you assign to the bottle?
1. $0
2. $20
3. $20 + interest
4. $75
5. $-55 (made profit of $55)
Source: Misbehaving: The making of Behavioral Economics by Richard Thaler.

9. Most people said $0 (30%) or $-55 (25%)
Answer
Source: Misbehaving: The making of Behavioral Economics by Richard Thaler.

10. Puzzle II
Imagine you bought a wine for $20. It now sells for $75. And you
broke the wine bottle, so what cost will you assign to the loss?
1. $0
2. $20
3. $20 + interest
4. $75
5. $-55 (made profit of $55)
Source: Misbehaving: The making of Behavioral Economics by Richard Thaler.

11. Most people said $75
Answer
Source: Misbehaving: The making of Behavioral Economics by Richard Thaler.

12. Normal people do not behave like economical theory.

13. Machine Learning can model behaviors
Machine Learning algorithms can be used to learn
patterns from data including human behavior without
explicitly being programmed.

14. II. What problems to solve using Machine
Learning?

15. Problems where Bayes Error rate is >80% and which
have high cost

16. Patterns, Patterns, and more Patterns!

17. • What we buy, when we buy?
• What makes us engaged?
• What makes us move to another product?
Everywhere....

18. • Solving problems that were thought unsolvable (For ex,
Anticipation of clients needs, Loans to people without bank
accounts)
• Solving problems that were thought not a problem (For ex,
customer acquisition, retention)
• Improving upon existing systems (For ex, Increase transparency
and frequency of communication, risk assessment)
Three groups of problems

19. III. How to build products using Machine Learning?

20. Step 1: Intuitive Thinking to decide what data
to collect to train your model

21. Example : Modeling "gamblers"

22. Puzzle III
You won $30. Which of the following you are likely to
take?
1. 50% chance of gaining $9 or 50% chance of losing $9
2. No further gain
Source: Misbehaving: The making of Behavioral Economics by Richard Thaler.

23. 70% choose option 1 (50% change of win or lose $9)
Answer
Source: Misbehaving: The making of Behavioral Economics by Richard Thaler.

24. Puzzle III
You lost $30. Which of the following you are likely to take?
1. 33% chance of gaining $30 or 67% chance of nothing
2. Sure $10
Source: Misbehaving: The making of Behavioral Economics by Richard Thaler.

25. 60% choose option 1 (33% to gain $30)
Answer
Source: Misbehaving: The making of Behavioral Economics by Richard Thaler.

26. Model
If you want to model people's next behavior you
need to have data about their past looses or
gains.

27. Step 2: Collect data (GDPR)

28. For business - Often data is public

29. B2C - How to make people share their data?
(GDPR)

30. 1: Build trust and likeability:
Create community and gamification
Goals &
challenges
Rewards

31. 2: Cannot force to adopt and let users be in control
vs

32. 3: Do not try to change behaviors
https://techcrunch.com/2013/07/13/why-behavior-change-apps-fail-to-change-behavior/

33. 4: Educate your customers/users

35. Step 3: Algorithm

36. Algorithms
• Group people (risk profiling, communication):
Classification and Clustering
• Individual future behavior (what, when someone will
act): word2vec and LSTM

37. Step 4: Development

38. Open source Libraries

39. • Intuitive thinking
• Collect data
• Select algorithms
• Development
Summarize - How to build ML products?

40. Cost of not doing!
• Amazon becoming partially a bank!
• New 'payment banks' are already in Asia (India,
Vietnam).
• Existential crisis in era of Internet and Globalization.

41. Machine Learning is NOT rocket science
Adoption
How to collect data?
Intuitive
Thinking
Feel free to contact:
https://www.linkedin.com/in/mitrar/
mitra.rudradeb@gmail.com
As ML experts answer the following
Algorithm
What algorithm to
use?
What data to collect?