QuantUniversity Fintech Bootcamp Day- 4

1. Location:
Boston Fintech Week 2019
Babson College
Boston, MA
Fintech Bootcamp
Day 4
2019 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.analyticscertificate.com

2. 2
QuantUniversity
• Analytics and Fintech Advisory
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data & Fintech
• Programs
▫ Analytics Certificate Program
▫ Fintech Certification program
• Building

3. • Founder of QuantUniversity LLC. and
www.analyticscertificate.com
• Advisory and Consultancy specializing in Data
Science, ML and Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
customers.
• Charted Financial Analyst and Certified
Analytics Professional
• Teaches Analytics in the Babson College and at
Northeastern University, Boston
Sri Krishnamurthy
Founder and CEO
3

4. 4
Agenda – Day 1

5. 5
Agenda – Day 2

6. 6
Agenda – Day 3

7. 7
Agenda – Day 4

8. 8

9. The history of payment automation
(https://www.forbes.com/sites/falgunidesai/2015/12/13/the-evolution-of-fintech/#3ad227377175)
Credit card
1950s
ATM
1960s
Electronic stock trading
1970s
bank mainframe computers
1980s
PayPal was founded
1998
Digitalization in
financial institutions
2000s

10. Major trends in payment innovation
Mobile
Payments
• Smart phone
platform for
payment
process
Streamlined
Payments
• Mobile
ordering and
payment
applications
Integrated
Billing
• Geotagging-
location-based
payment
• Machine to
machine
payment
Next Generation
Security
• Biometrics-
location-based
identification

11. Current payment process by traditional financial institutions
1. Transaction request from sending bank.
2. Secure message from sending bank to recipient bank.
3. Flow of funds through a clearing house or correspondent bank.
(Graph from WEF’s “The Future of Financial Service”
report)

12. The leading companies in the revolution of Fintech
payment

13. Major solutions of innovative payment
Open-loop mobile payment solutions
• Link customers to existing parties on the
platform
• Make payments more convenient for
customers leveraging new form factors(NFC,
QR code)
Companies using open-loop mobile payment solutions (Graph from WEF’s “The Future of Financial Service”
report)

14. Major solutions of innovative payment(2)
Closed-loop mobile payment solutions
• Combine POS, acquirer and payment network
as one single entity for a more flexible
experience.
• Allow customers to fund transactions through
traditional payment network ecosystem.
Companies using closed-loop mobile payment solutions (Graph from “The Future of Financial Service” report)

15. Major solutions of innovative payment(3)
Mobile merchant payment solutions
• Leverage mobile connectivity to replace
current POS infrastructure
• Make payment process more effortless and
accessible
Companies using mobile merchant
payment solutions
(Graph from “The Future of Financial Service” report)

16. The impact of payment revolution on traditional
financial institutions
• Increasing network of alternative financial service
providers.
• Price competition will be more fierce.
• Traditional financial institutions will be challenged and
motivated to launch alternative payment solutions.
• Traditional financial institutions will have to play new role
as an interaction between alternative payment and
traditional ways increase.

17. Future for payment innovation
Payment behavior
• One single default card to process all payment.
• Amazon 1-click ordering
• Uber’s seamless payment
Payment preference
• Increasing focus and preference on differentiation of card
brand and design
• Proliferation of niche and merchant issued cards.
Payment market
• Elimination of physical cards and optimization on card usage.
• Seamless link to customer’s bank accounts

18. 18

19. 19
The players
Company Name Company Website Service Highlights
Lending Club
https://www.lendingclub.com/busin
ess/
Business loans + lines of credit;
No repayment penalty;
Competitive APR for true annual borrowing cost;
OnDeck https://www.ondeck.com/
Business loans + lines of credit;
Repeat customer benefits;
Fast speed in fund receiving;
Discount and rewards on loans for loyalty management
Paypal Working Capital
https://www.paypal.com/us/webap
ps/mpp/merchant-working-capital
No credit check;
Pay with a proportion of sales;
Receive funding in less than 1 minute;
Pay one fixed fee instead of periodic interest.

20. 20
The players
Company Name Company Website Service Highlights
Social Finance (Sofi) https://www.sofi.com/
Personal loan + student loans + mortgage;
Student loan with career coach and parent loans;
Fixed rates/variable rates;
Flexible payment options and forbearance.
Amazon amazon-lending@amazon.com
Registered Amazon seller only;
Merchant cash advance;
Competitive interest rates;
Use of loan only applied to inventory purchasing through Amazon
Marketplace.
Alibaba https://loan.mybank.cn/ Chinese P2P lending platform partnership with Lending Club in the US.
Square Capital https://squareup.com/capital
Eligible to Square seller;
Automatic repayment with fixed percentage from sells.

21. 21
SWOT

22. #Disrupt19
Credit Risk Decision Making Using Lending Club Data

23. 23
1. Case Intro
2. Data Exploration of the Credit risk data set
3. Problem Definition and Machine learning
4. Performance Evaluation
5. Deployment
Case study

24. 24
Credit risk in consumer credit
Credit-scoring models and techniques assess the risk in
lending to customers.
Typical decisions:
• Grant credit/not to new applicants
• Increasing/Decreasing spending limits
• Increasing/Decreasing lending rates
• What new products can be given to existing applicants ?

25. 25
Credit assessment in consumer credit
History:
• Gut feel
• Social network
• Communities and influence
Traditional:
• Scoring mechanisms through credit bureaus
• Bank assessments through business rules
Newer approaches:
• Peer-to-Peer lending
• Prosper Market place

26. 26
The Data
26
https://www.kaggle.com/wendykan/lending-club-loan-data

27. Machine Learning Workflow
Data Scraping/
Ingestion
Data
Exploration
Data Cleansing
and Processing
Feature
Engineering
Model
Evaluation
& Tuning
Model
Selection
Model
Deployment/
Inference
Supervised
Unsupervised
Modeling
Data Engineer, Dev Ops Engineer
Data Scientist/QuantsSoftware/Web Engineer
• AutoML
• Model Validation
• Interpretability
Robotic Process Automation (RPA) (Microservices, Pipelines )
• SW: Web/ Rest API
• HW: GPU, Cloud
• Monitoring
• Regression
• KNN
• Decision Trees
• Naive Bayes
• Neural Networks
• Ensembles
• Clustering
• PCA
• Autoencoder
• RMS
• MAPS
• MAE
• Confusion Matrix
• Precision/Recall
• ROC
• Hyper-parameter
tuning
• Parameter Grids
Risk Management/ Compliance(All stages)
Analysts&
DecisionMakers

28. 28
Credit Risk pipeline
Data Ingestion
from Lending
Club
Pre-Processing
Feature
Engineering
Model
Development
and Tuning
Model
Deployment
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5

29. 29
29

30. 30
www.QuSandbox.com

31. 31

32. 32
Agenda – Day 1

33. 33
Agenda – Day 2

34. 34
Agenda – Day 3

35. 35
Agenda – Day 4

36. 36
http://www.analyticscertificate.com/fintech/

37. 37
http://www.analyticscertificate.com/fintech/

38. 38
http://www.analyticscertificate.com/fintech/

39. 39
http://www.analyticscertificate.com/fintech/

40. 40
• You will get a questionnaire + a quiz covering all four lectures by
mail today
• You have an option to waive the quiz and just get a participation
certificate
• You have time till Monday Sep 16th to complete the quiz
• Certificates will be mailed out on Sep 17th.
Good luck!
Next steps and Certification

41. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.
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