In this talk, I answer why, what and how Machine Learning can help solve major problems in the Banking sector.

1. Machine Learning Adoption:
Crossing the Chasm for banking
and insurance sector
Rudradeb Mitra | http://www.linkedin.com/in/mitrar/
EFMA Operational Excellence Council
23rd March 2018, Athens, Greece

2. Chasm

3. Content (~30-40 mins)
• Bio
• Why to use Machine Learning?
• What problems to solve using Machine Learning?
• How to solve those problems?
• What are the Challenges?

4. Brief Bio
• 2003-2009: Worked in European Research Labs, Universities and
Startups on AI/ML. NLP, Semantic web, Declarative Language.
• 2009-2010: Graduation from University of Cambridge.
• 2010-2017: Built 6 startups in 4 countries. One of them an ML startup in
Belgium on profiling risky drivers.
• Mentor of Google Launchpad, Writer and Speaker.

5. Objective
• To show you something that will add a lot of value and
you can start working in a week.

6. What is Machine Learning?

7. Machine Learning
• Machine Learning algorithms can be used to learn
patterns from data without explicitly being
programmed.

8. I. Why to use it?

9. • Many real world scenarios
cannot be modeled
accurately via statistical/
mathematical models.
• No learning in other
models.
Why to use it?

10. • Using data to build behavioral models
• Machine Learning has become a commodity
• A lot of data
What is new?

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

12. @copyright: Rudradeb Mitra
Technology adoption
Full article: https://www.linkedin.com/pulse/crossing-chasm-stop-identifying-cats-start-creating-value-mitra/

13. Focus Area
Operational
Excellence
Machine
Learning
Increase transparency and frequency in
communication
Refocus on client risk/return profile
Focus on easy-to-access and easy-to-deal
with private bankers and simplify process
Enhance pro-activeness and anticipation of
client needs
Simplify certain products
Reduce # of relationships per Relationship
Manager
Enhance array of products
Enhance technical and product skill set of
private bankers
Produce more reliable, timely management
information and comprehensive external
reporting
High Medium LowRelevance

14. What problems to solve?
• Are there problems where Bayes Error rate is
>80% and which have high cost?

15. • Solving problems that were thought unsolvable (For ex,
Anticipation of clients needs, Loans)
• 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)
Three groups of problems

16. III. How to solve it using Machine Learning?

17. Step 1: Intuitive Thinking

18. 1. Identify people who say they need the product
2. Identify people who may need the product
3. Identify people who will need the product
Customer acquisition

19. Identify people who will need the product
The only way to sales conversation with
high-value prospects is to interrupt them
- Fanatical Prospecting, Jeb Blount

20. Identify patterns in your best customers
• Who are your best customer
• Why they became customers
• Why they still buy from you
• Why do prospects choose you over other similar products

21. Step 2: Collect data

22. B2B segment - Often data is public

23. Snapshot: Data

24. B2C segment - Works differently

25. Record a trip Trip feedback
PREDICTING CAR INSURANCE PREMIUMS

26. 1: Provide Incentive
Goals &
challenges
Rewards

27. 2: Cannot force to adopt and let
users be in control
vs

28. 3: Do not try to change behaviors
https://techcrunch.com/2013/07/13/why-behavior-change-apps-fail-to-change-behavior/

29. 4. Identify early adopters
• Who will be best early adopters?

30. 5. Educate your customers/users

31. Step 3: Algorithm

32. Type of Data
• Labeled data then use Supervised Learning
• Unlabeled data then use Unsupervised Learning

33. Using Algorithms
• Classification: Customer acquisition
• Clustering: Risk profiling
• word2vec: Customer engagement
• LSTM: Customer acquisition, engagement (long term
memory)

34. Step 4: Development

35. Open source Libraries

36. IV. What are the challenges?

38. Challenges
• Technology: Very low
• Data: Yes esp. lack of data. Find innovative ways.
'A simple model with more data is accurate than a complex
model. Most ML system do not need PhD'

39. Challenges
• Adoption: Yes but follow the points mentioned.
• Cost: High but
• one does not need Phd in Data Science. Get the right person
for the right role, even students.
• Get external person for short term project to architect and
build.
• Cost can be quite low!

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

41. Machine Learning is NOT a Technology Challenge
Adoption
How to collect data?
Intuitive
Thinking
Feel free to contact:
https://www.linkedin.com/in/mitrar/
mitra.rudradeb@gmail.com
Challenge is in
Algorithm
How to deal with
incomplete data?
What data to collect?