Many people think success in data science is all about big data – being able to manipulate extremely large volumes of information and scale black box algorithms. This is not always the case. Getting data science right isn’t a matter of the volume of data you have. It’s about understanding the quality of data you’re working with and knowing exactly what to look for in that data set. Data scientists are artists who use human skill and intuition to build effective models. In this talk, ZestFinance will discuss the distinction between big data and data science and share examples of how data science techniques can be used to provide insights that help predict risk, revenue outcomes and click-through behavior.

1. © 2015 ZestFinance, Inc.
Data Science ≠ Big Data
Jim McGuire
ZestFinance

2. 1
© 2015 ZestFinance, Inc.
Disregard (Possibly) If You Work Here:

3. 2
© 2015 ZestFinance, Inc.
Real “Big Data” Challenges
Company Amount of Data
Facebook 1.2B Users
Google 60T Pages
Amazon 200M products
XBox 50M Users

4. 3
© 2015 ZestFinance, Inc.
ZestFinance and Big Data
 Big Data Lends New Zest to Banks’ Credit Judgments
 How Big Data is Revolutionizing the Credit Scoring Industry
 Beware Big Data’s Easy Answers
 How Big Data Might Mean Better Business for Big Banks
 ZestFinance… Uses Big Data to Weed Out Deadbeats
 For ZestFinance, Big Data Comes With Big Responsibility
 Big Data Uncovers Some Weird Correlations
 How Big Data Could Replace Your Credit Score
 Can ‘Big Data’ Lift People Out of Cycles of Debt?

5. 4
© 2015 ZestFinance, Inc.
ZestFinance
• Started in 2009 by Douglas Merrill, ramped up in 2011
• Mission: to provide fair and transparent credit to everyone
• Initial focus on the underbanked in the US
• Extensive use of ML
• Provide models and services for:
• Underwriting
• Fraud Prevention
• Customer Service
• Collections
• Marketing
• Consult with external clients
• Traditional lenders
• Subprime lenders
• Media providers
• eCommerce
• Telemarketing
• Insurance
• International

6. 5
© 2015 ZestFinance, Inc.
Extending Credit to the Underbanked
• Traditional credit scoring methodology in use for 40+ years
• FICO et al work great for those with ample credit history
• Lumped together at the bottom score bands:
• Little credit history
• Bankruptcies
• Poor credit history
• Use of alternative sources of credit
• Problems in modeling credit for <600 FICO
• Traditional credit bureau data is sketchy
• Alternative providers of “credit” data with limited coverage and quality
• Higher incidence of fraud
• Most borrowers already live financially “on the edge”

7. 6
© 2015 ZestFinance, Inc.
ZestFinance & JD.com
• Credit in China:
• Historically a “savings” culture
• < 25% of population has access to credit
• Growing middle class
• Growing population of young workers in tech industry
• No credit bureau’s per se
• Credit available for corporations and the wealthy
• Companies don’t have data or tools to assess risk
• Tech companies are being given licenses to lend and
build credit scores
• Learnings from “deep subprime” in America
translate to new markets
• No extensive credit data
• Must combine noisy data from disparate sources
• Traditional credit scoring philosophies may not apply

8. 7
© 2015 ZestFinance, Inc.
Why Data Science is More Than Just a
Lot of Data
• Interpretability / insights necessary for buy-in
• Past business decisions leave imprint on your data
• Expectations of model must be financially grounded
• Noisy data with lots of holes and biases
• Often trying to capture ephemeral “human behavior”
• Complicated structure / relations between fields

9. 8
© 2015 ZestFinance, Inc.
EC2 + Favorite ML Software

10. 9
© 2015 ZestFinance, Inc.
Shaped Datasets
• Past business
decisions shape
datasets used for
building models
• More extreme for
some verticals, like
lending
• Toy example to
demonstrate effect

11. 10
© 2015 ZestFinance, Inc.
Shaped Datasets
• Launch new model,
change
• Collect more data
• Iterate on model build

12. 11
© 2015 ZestFinance, Inc.
Shaped Datasets
• After several
iterations, end up in
this situation
• How to predict swap-
in population after
next model iteration?

13. 12
© 2015 ZestFinance, Inc.
Blind Spots in Data Collection
Blind
Spot Training Set
Entire
Applicant
Population
• Model is not validated
in regions of feature
space that exist in
whole population
• Capping variables may
help, or may give you
false security
• Box cuts can
exacerbate issue
• Univariate analysis may
be misleading

14. 13
© 2015 ZestFinance, Inc.
Blind Spots in Data Collection
VolumeinBlindSpotPerformanceinBlindSpot

15. 14
© 2015 ZestFinance, Inc.
Univariate Signals and Shaping
• In business settings,
predictors are often
intuitive and interpretable
• After building a model,
this variable bubbles up
as significant

16. 15
© 2015 ZestFinance, Inc.
Univariate Signals and Shaping
• In this case, univariate
behavior of the variable
has exactly the opposite
of expectations
# Delinquent Accounts

17. 16
© 2015 ZestFinance, Inc.
Univariate Signals and Shaping#Trades>30DQEver
# Trades Ever Opened
Possible to have high
number of delinquent
accounts if only a small
fraction of total accounts

18. 17
© 2015 ZestFinance, Inc.
• Categorical feature
showed good risk
splits
Historical Business Performance and
Unintentional Proxies
Category Metric
Red 8.3%
Green 7.9%
Blue 6.4%
But really it just
proxied for time

19. 18
© 2015 ZestFinance, Inc.
Lots of Missing Data That Have Different
Meanings
• Context relevant
• Combination of ML
expertise and
business acumen to
handle appropriately

20. 19
© 2015 ZestFinance, Inc.
Hire to Solve Your Business Problem
We Need Data Artists To Save Zombie
Borrowers:
The hottest job right now is ‘data scientist’,
but that label is wrong. It’s not just about
science, it’s also about art. The hottest job
should be data artist – those who
understand the quirks of data and think
through and enjoy the nature of what you’re
talking about… People think you get a
whole bunch of bits and drop them into a
stats box and get all the answers. Turns out
every answer you get is incorrect.
• “You can find a great developer and a
great researcher who has a background
in statistics, and maybe you can find a
great problem solver, but to find that in
the same person is hard.”
• Stan Humphries, Zillow
• “Practice. I strongly believe that being
a data scientist is an interesting merger
of science and craftsmanship. You need
to understand the theory but at the
same time you also need to exercise
your gut feeling.”
• Michael Berthold, KNIME

21. 20
© 2015 ZestFinance, Inc.
Hiring for Diversity
• Naturally an interdisciplinary field that is rapidly evolving
• “Data Scientist” means a lot of different things
• “Full stack analyst” vs. “data engineer” vs. …
• Are coding skills important? Are big data skills important?
• Draw from range of backgrounds:
• Psychology
• Biostatistics
• Physics
• Applied Math
• Computer Science
• Statistics
• Engineering
• MBA

22. 21
© 2015 ZestFinance, Inc.
Q & A
• Thanks!